<![CDATA[JayWink Solutions - Blog]]>Fri, 02 Dec 2022 22:58:00 -0500Weebly<![CDATA[Revenge ON the Nerds]]>Wed, 30 Nov 2022 15:30:00 GMThttp://jaywinksolutions.com/thethirddegree/revenge-on-the-nerds     Unintended consequences come in many forms and have many causes.  “Revenge effects” are a special category of unintended consequences, created by the introduction of a technology, policy, or both that produces outcomes in contradiction to the desired result.  Revenge effects may exacerbate the original problem or create a new situation that is equally undesirable if not more objectionable.
     Discussions of revenge effects often focus on technology – the most tangible cause of a predicament.  However, “[t]echnology alone usually doesn’t produce a revenge effect.”  It is typically the combination of technology, policy, (laws, regulations, etc.), and behavior that endows a decision with the power to frustrate its own intent.
     This installment of “The Third Degree” explores five types of revenge effects, differentiates between revenge and other effects, and discusses minimizing unanticipated unfavorable outcomes.
     Revenge effects could easily be confused with side effects, but there is an important difference.  Side effects are incidental to the main effect, unrelated to objectives of the decision or action, and may be temporary.  Revenge effects, on the other hand, are directly related and in opposition to objectives.  Additional action must be taken to counter revenge effects, lest the main objective be negated.
     Trade-offs are similar to side effects, in that an undesirable outcome must be accepted in order to attain the main effect.  Usually indefinite, trade-offs can be accepted as reasonable cost of achieving the main objective or additional action can be taken to mitigate the undesired effects.
     A reverse revenge effect is an unexpected advantage of a technology or policy implementation in Tenner’s terminology.  In the generic terms of unintended consequences, this is a serendipitous outcome.

Five Types
     Revenge effects are activated by various mechanisms depending on the technology involved, who or what is effected, and how.  Five types of revenge effects are described below; examples are provided to clarify the differences.
     When the solution to a problem causes the very same problem, or multiplies it, the “new” problem is a regenerating effect.  The “solution” regenerates the problem.
Example:  Settlers occupying lands with harsh winter climates built log cabins for greater protection from the elements than an animal hide can provide.  When a fire used to heat the cabin gets out of control, damaging the structure, the frontiersman is exposed to wind and rain in addition to frigid temperatures.  If the fire is successfully contained in a stone fireplace, smoke and ash are still present in the air and burns may result from tending the fireplace.  In either case, the fire causes regenerating effects that impact the health and safety of the cabin’s inhabitants.
     Increasing the capacity of a system invites increased utilization.  The result is stagnant performance brought to you by recongesting effects.
Example:  As use of mobile phones increased, performance dropped.  Service providers countered this by building additional towers and expanding coverage.  The increased performance attracted new cellular customers; the performance drop repeated… several times.  Data transmission followed a similar pattern with the transition to smartphones.  Recongesting effects continue to occur until the system’s capacity exceeds the demands placed on it.
     Repeating effects commonly occur when introduction of technology changes behavior.  Rather than simply facilitating a task so it is less time-consuming, the availability of a tool or aid leads to more frequent repetition of the task.  The time spent on the task, using the tool or aid, may match or exceed that spent before the technology was developed.
Example:  Lawn care requires much more time and energy when performed with a walking mower and manual trimmer than with a riding mower and string trimmer.  The reduced effort required encourages more frequent grooming, raising residential landscaping standards.
     Implementation of technology may raise standards of performance or encourage expansion of its use beyond its original intent.  The resulting challenges are recomplicating effects.
Example:  Computer numerical control (CNC) substitutes machine control for human operation to reduce errors and improve consistency in manufacturing.  Coordinate measuring machines (CMM) provide reliable verification of dimensions.  The increased precision and repeatability of these machines inspire more stringent design specifications to perform the same function as was achieved with manual machines and vernier calipers.  The difficulty of obtaining “acceptable” results has been maintained or increased by way of recomplicating effects.
     Likely the most common, rearranging effects are also the most recognizable.  If you have ever played Whac-A-Mole, you are familiar with the concept of rearranging effects – addressing a problem in one place only causes it to “pop up” in another.
Example:  The shifting of responsibility for product flaws between two “quality” groups (similar to another game – Volleyball), discussed in “Beware the Metrics System,” exemplifies rearranging effects.  Each group’s metrics performance depends on the other’s in a zero-sum game.
     Similarly, flood control measures do not prevent flooding; they simply relocate it to other (unprotected) areas.  Air conditioning does not eliminate heat; it transfers it elsewhere.  Trash barges move refuse from large cities to less populated areas; they do nothing to address excessive waste.  Rearranging effects are ubiquitous; you have probably noticed many, though you did not have a name for them.
 
Respect the Law Redux
     Several strategies for minimizing undesirable outcomes were presented in “Unintended Consequences – It’s the Law.”  As a subset of unintended consequences, each of the strategies described is applicable to revenge effects.  One may stand above the rest, however, as the key to preventing revenge effectshigher-order thinkingRevenge effects are multidimensional, involving changes in standards, behaviors and norms, locations, magnitudes, and more.  These dimensions are orthogonal to the “layered” nature of unintended consequences, further complicating the task of predicting outcomes.
 
     Discussions of unintended consequences typically presume that these effects are in addition to the intended outcomes.  However, this may not be the case.  Failure to attain desired outcomes may be due to poor planning, flawed execution, or the “bite” of revenge effects.  While the first two may require iteration, the third may require an entirely new action plan, possibly more urgent than before.
     In other scenarios, effects are transformed from acute to chronic problems.  As development of safety apparatuses and medical interventions increase survivability of many accidents and diseases, the effects of these occurrences become “spread in space and time.”  A helmet that “reduces” the consequences of a head injury from death to brain damage leaves the victim in a state of diminished capacity for the remainder of his/her life.  Likewise, a cardiac patient may recover from a heart attack, but heart disease will forever plague him/her.
     The specter of potential revenge effects is ever-present.  Thorough analysis is essential to effective decision-making; contingency planning is required to minimize the impacts of effects that materialize.  Development of several alternatives may be necessary to accommodate various combinations of repercussions that could result.
 
     For additional guidance or assistance with Operations challenges, feel free to leave a comment, contact JayWink Solutions, or schedule an appointment.
 
References
[Link] Why Things Bite Back:  Technology and the Revenge of Unintended Consequences.  Edward Tenner. Alfred A. Knopf, 1996.
[Link] “When Technology Takes Revenge.”  Farnam Street.
[Link] “The revenge effect in medicine.”  Balaji Bikshandi.  InSight+, February 6, 2017.
[Link] “Revenge effect.”  Academic Dictionaries and Encyclopedias, New Words.
[Link] “Revenge effects.”  Stumbling & Mumbling. January 24, 2014.

 
Jody W. Phelps, MSc, PMP®, MBA
Principal Consultant
JayWink Solutions, LLC
jody@jaywink.com
]]>
<![CDATA[Unintended Consequences – It’s the Law]]>Wed, 16 Nov 2022 15:30:00 GMThttp://jaywinksolutions.com/thethirddegree/unintended-consequences-its-the-law     The Law of Unintended Consequences can be stated in many ways.  The formulation forming the basis of this discussion is as follows:
The Law of Unintended Consequences states that every decision or action produces outcomes that were not motivations for, or objectives of, the decision or action.”
     Like many definitions, this statement of “the law” may seem obscure to some and obvious to others.  This condition is often evidence of significant nuance.  In the present case, much of the nuance has developed as a result of the morphing use of terms and the contexts in which these terms are most commonly used.
     The transformation of terminology, examples of unintended consequences, how to minimize negative effects, and more are explored in this installment of “The Third Degree.”
Unanticipated Consequences of Social Action
     Sociologist Robert K. Merton popularized the concept of unanticipated consequences in his 1936 paper “The Unanticipated Consequences of Purposive Social Action.”  In this paper, Merton presents several causal factors; these are explained, in brief, below.
     Incomplete information creates “blind spots” in decision-makers’ understanding of likely or potential outcomes.  Merton does not use this term; it is the conjunction, for simplicity, of two concepts.  Merton differentiates between knowledge possessed (the “ignorance” factor) and the knowledge conceivably obtainable.  Ignorance is a second-order cause; the distinction has little value at this level of discussion.  Therefore, incomplete information can be used to describe any lack of knowledge, regardless of its cause.
     Drug and alcohol prohibition provides the quintessential example of unintended consequences.  Lawmakers do not account for the ways in which people will respond to legislation.  Black markets are established, where the elimination of open competition allows organized crime to flourish.  As profits from illicit sales rise, escalating violence follows.  Systems develop to facilitate criminal activity, leading to an expansion into other enterprises – counterfeiting, human trafficking, or any other.  And then there’s the corruption…
     Even if decision-makers possess “perfect” (i.e. complete, accurate) information, the potential for error remains.  Several types of error could occur, including analysis, interpretation, and prediction errors.  All decisions are susceptible to errors; this is one reason many are paralyzed by the need to make one (see the “Making Decisions” series).
     Transitioning to piece-rate wages in a manufacturing operation often suffers from error in analysis and prediction.  The wage structure may be changed with the expectation of an increase in employee wages and improved standard of living.  A simultaneous increase in profits, due to higher production rates, may also be anticipated.  However, an intense focus on quantity may result, causing workers to accept lower quality and relaxed safety standards.  The end result is lower sales, degrading conditions, and a decline in employee’s overall well-being.
     The “imperious immediacy of interest” refers to situations in which a sense of urgency prompts actions, sometimes extreme, without thorough consideration of risks or consequences.  Attention is focused on addressing the most pressing concern, while all other issues are essentially ignored.  Consider the case of a parent entering a burning building to rescue a child.  The imperious immediacy of a child in danger prevents consideration of the probability of death from smoke inhalation, roof collapse, etc. before running inside.
     The “basic values” of a country, family, or other cultural group can lead people to act in predetermined ways without consideration of potential outcomes or alternative actions.  The term “cultural norms” is often used to describe a similar phenomenon.  For an illustration of the impact of basic values or norms, consider the following example.
     In a society in which a woman is subjugated to the will of her father, caring for him in his elder years may be a primary responsibility.  She is likely to do so without hesitation, though it may deprive her of opportunities to create a fulfilling life for herself.  She may be unable to develop social ties, through marriage or otherwise, that ensure she has a caregiver in her time of need.
     Finally, public predictions can influence behavior to such an extent that the predictions do not come true.  This is known as a “self-defeating prophecy,” where awareness of a prediction prevents its fruition.  For example, the prediction of the extent to which ozone depletion would effect the earth and its inhabitants led to bans on the use of chlorofluorocarbon-based refrigerants and aerosols (CFCs).  Instead of continued depletion, the ozone layer has entered a recovery phase.
     As an aside, self-defeating prophecy has a better-known counterpart – the self-fulfilling prophecy.  In this case, the prediction influences behavior such that realization of predicted outcomes is accelerated.  For example, news of distress in a nation’s financial system may cause a “run” on banks, precipitating collapse.
 
Basic Terminology
     Consequences of a decision or action can be considered on two dimensions, as summarized in Exhibit 1anticipated/unanticipated and desirable/undesirable.  The anticipated/unanticipated dimension describes whether or not a certain outcome had been predicted.  It says nothing about how a prediction was made or why an outcome was not predicted (error, incomplete information, etc.).  Accuracy of predictions is a secondary matter for purposes of this discussion; decision-makers are credited for conducting an analysis, despite its imperfections.
     The desirable/undesirable dimension is highly subjective, requiring a normative judgment.  The perspective of a person is fundamental to this judgment; there may be strong disagreement regarding the desirability of an action or outcome (e.g. labor vs. management, homeowner vs. property developer, etc.)  In the public policy realm, officials may declare opposition to a decision while secretly finding it personally or politically beneficial.
     Consequences of a decision or action that are anticipated and desirable are the intended outcomes – the objectives and motivations for it.  Outcomes that are neither desirable nor anticipated are the unintended consequences.  Evolution and usage of this term is discussed further in the next section.
     Outcomes of a decision or action that are undesirable, but anticipated, are called “double effect” consequences.  Double effect consequences occur when anticipated negative effects of an action are deemed acceptable in light of the desirable outcomes expected.  For example, the displacement of valley residents is accepted to obtain the benefits of man-made “lakes” or reservoirs, such as hydroelectric power and drought resilience.
     Outcomes that are unanticipated, but desirable, are attributed to serendipity.  In our modern society, however, it is likely that some grandstander will take credit for any positive effects.  Worse, this ne’er-do-well will probably spout buzzwords like synergy, equity or other words s/he doesn’t understand.
 
Transformation of Terminology
     The problem of unintended consequences has been considered by a number of historical luminaries, including Niccolo Machiavelli, Adam Smith, Karl Marx, and Vilfredo Pareto.  Despite the intellectual might of his predecessors, Merton’s work seems to be the foundation of contemporary thought on the subject.
     Use of Merton’s term of art, “unanticipated consequences,” is uncommon today; it has been supplanted by “unintended consequences.”  In fact, Merton himself made this transition in later works.  He also dropped the word “purposive” from later versions of his seminal paper, claiming it is redundant (“all action is purposive”).  This is an odd claim, however, as it seems important to him to differentiate between conduct (purposive) and behavior (reflexive) in the original paper.
     Merton’s abandonment of purposive is peripheral to the current discussion.  It is mentioned only to demonstrate that the transition in terminology used in his work is not isolated to one word and seems to indicate evolving views or a new agenda.  We cannot be certain of his motivations, of course, but they are worth pondering.
     In modern usage, the term “unintended consequences” is typically invoked to imply that referenced outcomes were both undesirable and unanticipated.  Unfortunately, it can also be used to make intentionally opaque statements.  Those making such statements rely on a form of self-deception or, at minimum, a lack of critical analysis to manipulate an audience.  Receivers of the message are encouraged to interpret “unintended” to mean “unanticipated”, though that may not be true.  The unforeseen is presented as unforeseeable to avoid culpability.  Similarly, the messages are intended to convey to the audience that the speaker finds the outcomes objectionable – even if profiting from them – to project a favorable image.  While not exclusive to it, nefarious use of ambiguity is pervasive in the political arena.  For this reason, discussions of terminological distortion frequently return to examples in politics.
     As de Zwart points out, conflating “unintended” and “unanticipated” obscures the fact that many decisions are difficult or unpopular because undesirable outcomes are anticipated.  Opacity is often used to deflect or minimize responsibility for decisions or their consequences, particularly when there is no solid, logical defense (i.e. rational basis) to offer.
     Critics of a decision often cite unintended consequences as evidence of decision-makers’ incompetence, complacency, or indifference.  Doubts about the truth of claims that negative consequences were unanticipated may even prompt accusations of blatant malice.
     If decision-makers seek to distance themselves further from responsibility for actions, Merton provides additional replacement terms.  There are no longer intended outcomes (objectives) and unintended consequences; there are merely “manifest functions” and “latent functions.”  It is difficult to imagine a purpose to which these terms are better suited than providing political “cover” through opacity.  “Don’t blame me; it’s a latent function!
 
Respect the Law
     When it comes to unintended consequences, respecting the law begins with recognizing it as a legitimate concern and accepting responsibility for all outcomes.  To minimize negative effects, unintended consequences must be considered as thoroughly as objectives.  That is, both manifest functions and latent functions require analysis.  Several strategies for minimizing unintended consequences are described below.
  • Define a reversing mechanism.  Prior to implementing a decision, define the actions necessary to reverse it, should the negative effects be deemed unacceptable.  Definition of unacceptable, i.e. what conditions will trigger reversal, should be documented for all conceivable scenarios.
  • Invert thinking.  Focus on outcomes to be avoided, working “backward” to find solutions that achieve the desired objectives.
  • Develop a disconfirmation bias.  Actively seek evidence that contradicts assumptions or conclusions about likely outcomes of a decision.
  • Engage in higher-order thinking.  Consequences are often layered.  Each outcome propagates additional consequences – some positive, some negative.  To adequately assess a decision, define and evaluate as many layers of consequences as possible to determine the decision’s net effect.  A common “layer” to be considered is risk compensation, where a reduction of risk is followed by an increase in risky behavior.
  • Stay in your lane.”  Act only within the scope of your abilities, or “circle of competence;” seek advice from others on unfamiliar topics.  Be honest with yourself about the depth of your knowledge of relevant subjects; overconfidence leads to poor decisions and catastrophic consequences.
  • Do nothing.  If a net positive effect cannot be predicted with sufficient confidence, resist pressure to “Do something!” until an appropriate course of action can be defined.  (See “Making Decisions” – Vol. I and Vol. VII).
 
     Early use of “unintended” as a synonym for “unanticipated” may have been an innocuous substitution.  As the terminological transformation is near-complete, however, the term has come to represent purposeful ambiguity.  “Fuzzy” terminology, where a speaker’s meaning is not clear, makes disingenuous statements more defensible.  A lack of clear definition permits literal use of terms when an alternate understanding is common and vice versa.  If questioned, the misinformation is attributed to a simple misunderstanding, rather than obfuscation.  In an ironic twist, nefarious use of the term “unintended consequences” may, itself, be an unintended consequence.
 
     For additional guidance or assistance with Operations challenges, feel free to leave a comment, contact JayWink Solutions, or schedule an appointment.
 
References
[Link] “The Unanticipated Consequences of Purposive Social Action.”  Robert K. Merton.  American Sociological Review; December, 1936.
[Link] “Unintended but not unanticipated consequences.”  Frank de Zwart. Theory and Society; April 12, 2015.
[Link] “The Law of Unintended Consequences.”  Mark Manson.  MarkManson.net.
[Link] “The Law of Unintended Consequences: Shakespeare, Cobra Breeding, and a Tower in Pisa.”  Farnam Street.
[Link] “The Law of Unintended Consequences.”  Lori Alden.  Econoclass, 2008.
[Link] “Unintended Consequences.”  Rob Norton.  EconLib.
[Link] “Unintended consequences.”  Wikipedia.

 
Jody W. Phelps, MSc, PMP®, MBA
Principal Consultant
JayWink Solutions, LLC
jody@jaywink.com
]]>
<![CDATA[Blinded by the Light]]>Wed, 02 Nov 2022 14:30:00 GMThttp://jaywinksolutions.com/thethirddegree/blinded-by-the-light     An organization’s safety-related activities are critical to its performance and reputation.  The profile of these activities rises with public awareness or concern.  Nuclear power generation, air travel, and freight transportation (e.g. railroads) are commonly-cited examples of high-profile industries whose safety practices are routinely subject to public scrutiny.
     When addressing “the public,” representatives of any organization are likely to speak in very different terms than those presented to them by technical “experts.”  After all, references to failure modes, uncertainties, mitigation strategies, and other safety-related terms are likely to confuse a lay audience and may have an effect opposite that desired.  Instead of assuaging concerns with obvious expertise, speaking above the heads of concerned citizens may prompt additional demands for information, prolonging the organization’s time in an unwanted spotlight.
     In the example cited above, intentional obfuscation may be used to change the beliefs of an external audience about the safety of an organization’s operations.  This scenario is familiar to most; myriad examples are provided by daily “news” broadcasts.  In contrast, new information may be shared internally, with the goal of increasing knowledge of safety, yet fail to alter beliefs about the organization’s safety-related performance.  This phenomenon, much less familiar to those outside “the safety profession,” has been dubbed “probative blindness.”  This installment of “The Third Degree” serves as an introduction to probative blindness, how to recognize it, and how to combat it.
     There are three types of safety activity mentioned throughout this discussion of probative blindness (PB), often without being explicitly identified.  They are assessment, ensurance, and assuranceAssessment activities are susceptible to PB; these activities seek to update an organization’s understanding of its safety performance and causes of accidents, but may fail to do so.
     Ensurance activities are conducted to increase the safety of operations as a result of successful assessments (beliefs about safety have changed).  Assurance activities seek to increase confidence in the organization’s safety performance.  This may involve publicizing statistics or describing safety-related features of a system or product.
     The term “probative blindness” was coined by Drew Rae, an Australian academic, safety researcher, and advocate, and his colleagues.  It is used to describe activities that increase stakeholders’ subjective confidence in safety practices beyond that which is warranted by the insight provided or knowledge created about an organization’s actual safety-related performance.  Stated another way, activities that reduce perceived risk, while leaving actual risk unchanged, exhibit probative blindness.
     It is worthwhile to reiterate, explicitly, that PB is a characteristic of the activity, not the participants.  While use of the term in this way may not be highly intuitive, it will be maintained to avoid further confusion.  The relevance of the concept to the pursuit of safety, in any context, justifies tolerating this minor inconvenience.
 
     According to Rae, et al [2], “[p]robative blindness requires:
1. a specific activity, conducted at a particular time;
2. an intent or belief that the activity provides new information about safety; and
3. no change in organisational belief about safety as a result of the activity.”
     The specific activity could be a Fault Tree Analysis (FTA), Failure Modes and Effects Analysis (see the “FMEA” series), Hazard and Operability Studies (HAZOPS), or other technique intended to inform the organization about hazards, risks, and mitigation strategies.  The intent to provide new information differentiates the activity from pep talks, platitudes, and public affirmations.  Conducting an activity with the objective of acquiring new information does not ensure its achievement, however.
     A failure to provide new information is one mechanism by which the third element of probative blindness manifests.  It also occurs when the results or conclusions of the safety activity are rejected, or dismissed as faulty, for any reason.  This could stem from cognitive biases or distrust of the activity’s participants, particularly its leaders or spokesmen.
 
Precursors of Blindness
     There are several conditions that may exist in an organization than can make it more susceptible to probative blindness.  A “strong prior belief” in the safety of operations may lead managers to discount evidence of developing issues.  Presumably, the belief is justified by past performance; however, the accuracy of prior assessments is irrelevant.  Only current conditions should be considered.
     Preventing assessments of past performance from clouding judgment of current conditions becomes more difficult when success is defined by a lack of discovery of safety issues.  Deteriorating conditions are often ignored or discounted because an operation is “historically safe.”  Difficulty in spurring an appropriate response to a newfound safety issue, whether due to nontechnical (e.g. “political”) resistance, resource shortages, or other capacity limitation, may bolster the tendency to rely on past performance for predictions of future risk.
     A “strong desire for certainty and closure,” such as that to which the public assurance scenario, cited previously, alludes, may lead an organization to focus on activity rather than results.  For the uninitiated, activity and progress can be difficult to distinguish.  The goal is to assuage concerns about an operation’s safety, irrespective of the actual state of affairs.
     When organizational barriers exist between the design, operations, and safety functions, thorough analysis becomes virtually impossible.  Limited communication amongst these groups leads to a dearth of information, inviting a shift of focus from safety improvement to mere compliance.  Analysis is replaced by “box-checking exercises” that have no potential to increase knowledge or change beliefs about operational safety.  The illusion of safety is created with no impact on safety itself.
 
Blind Pathways
     Exhibit 1 provides a mapping of manifestations of PB.  Some of the mechanisms cited, such as failure to identify a hazard due to human fallibility, are somewhat obvious causes.  Others require further consideration to appreciate their implications.
     “Double dipping” is the practice of repeating a test until an acceptable result is obtained.  This term may be misleading, as it often requires many more than two attempts to satisfy expectations.  The more iterations or creativity of justifications for modifying parameters required, the more egregious and unscientific this violation of proper protocol becomes.
     Changing the analysis and post-hoc rationalisation can be used to end the iterative cycle of testing by modifying the interpretation of results, or the objective, to reach a “satisfactory” conclusion.  Real hazard and risk information is thus missing from analysis reports and withheld from decision-makers.
     Motivated skepticism involves cognitive biases influencing the interpretation of, or confidence in, analysis results.  Confirmation bias leads decision-makers to reject undesirable results, holding activities to a different standard than those producing more favorable results.  Reinterpretation may be attributed to normalcy bias, where aberrant system behavior is trivialized.  Seeking a second opinion is similar to double dipping; several opinions may be received before one is deemed acceptable.
     A valid analysis can be nullified by the inability to communicate uncertainty.  All analyses are subject to uncertainty; an appropriate response to analysis results or recommendations requires an understanding of that uncertainty.  If the analysis team cannot express it in appropriate terms and context, the uncertainty could be transferred, in the minds of decision-makers, to the validity of the analysis.
 
Alternate Pathways
     Accidents occur for many reasons; probative blindness is one model used to describe an organization’s understanding of the causes of an accident.  A brief discussion of others provides additional clarity for PB by contrasting it with the other models.  A summary of the models discussed below is shown in Exhibit 2.
     To restate, probative blindness occurs when safety analysis prompts no response indicating a change in beliefs about safety-relevant conditions has taken place.  Relevant information includes the existence of a hazard, risks associated with an acknowledged hazard, and the effectiveness of mitigation strategies related to an acknowledged hazard.
     The “Irrelevant” model of safety activity pertains to activities cited to demonstrate an organization’s concern for safety, though they are unrelated to a specific analysis or accident under investigation.  Citing these activities is often a “damage control” effort; spokesmen attempt to preserve an organization’s reputation in the face of adverse events.  In the “Aware but Powerless” model, activities are “neither blind nor safe.”  The organization is aware of safety issues, but responses to them, if undertaken, are ineffective.
     Each of the models discussed thus far include activities that, arguably, demonstrate concern for safety.  They differ in the influence that concern has on safety activity, but none improve accident prevention.
     Activities in the Aware but Powerless model, as made clear by its name, also demonstrate awareness of hazards within the organization.  Only one other does so – the “Lack of Concern” model.  In this model, both insufficient analysis and insufficient response to known hazards are present.  The underlying rationale is a subject for secondary analysis; overconfidence and callousness are possible motives for neglecting safety activity.
     In the final two models, activities fail to demonstrate either awareness or concern, suggesting the absence of both.  The direction of the causal relationship between the two deficiencies, if there is one, will vary in differing scenarios.  In the “Nonprobative” model, activities are not intended to discover causes of accidents or address specific safety concerns in any meaningful way.  Therefore, no awareness is generated; the absence of concern could be a cause or an outcome of pursuing nonprobative activities.
     The final model, and the simplest, is “Insufficient Safety Analysis,” wherein activities that could have revealed potential causes of accidents were not conducted.  The reasons for omission are, once again, the subject of secondary analysis that may reveal staffing shortages, lack of expertise in the organization, or other contributory factors.  Inactivity, like nonprobative activity, could be a byproduct of a lack of awareness or of concern.  The interplay of awareness, concern, and activity is presented pictorially in Exhibit 3.
Sight Savers
     Unfortunately, there is no silver bullet that will prevent probative blindness in all of an organization’s activities.  However, following a few simple rules will significantly improve visibility of safety-relevant conditions:
  • Commit to engaging in and following through on safety activities – assessments, mitigations, training, etc.  This commitment must be made at all levels of the organization to ensure sustained effort.
  • Maintain focus on safety; allow compliance to follow.  When the focus is on compliance, true safety cannot be assured.
  • Integrate safety reviews into all organizational activities to ensure product, process, and facility safety.
  • “Check your priors.”  Set aside preconceived beliefs about safety, historical safety records, and hubris.  It is more important to save people – employees, customers, the community at large – than to save face.
  • Show your work.  Double check it.  Just as taught in elementary school math class.
     In short, the best defense is a good offense.  Proactive elimination of the precursors of blindness is the first step in performing safety activities that are appropriate and effective.  Knowledge of blind pathways helps analysis teams recognize and avoid them.  Finally, safety professionals and decision-makers should always remember that shining a light on a subject is not always illuminating.
 
     For additional guidance or assistance with Operations challenges, feel free to leave a comment, contact JayWink Solutions, or schedule an appointment.
 
References
[Link] [1] “Probative Blindness:  How Safety Activity can fail to Update Beliefs about Safety.”  A.J. Rae, J. A. McDermid, R.D. Alexander, and M. Nicholson.  9th IET International Conference on System Safety and Cyber Security 2014.
[Link] [2] “Probative blindness and false assurance about safety.”  Andrew John Rae, and Rob D. Alexander.  Safety Science, February 2017.

 
Jody W. Phelps, MSc, PMP®, MBA
Principal Consultant
JayWink Solutions, LLC
jody@jaywink.com
]]>
<![CDATA[Be A Zero – Part 2:  Zero-Based Scheduling]]>Wed, 19 Oct 2022 14:30:00 GMThttp://jaywinksolutions.com/thethirddegree/be-a-zero-part-2-zero-based-scheduling     Another way to Be A Zero – in a good, productive way – is to operate on a zero-based schedule.  An organization’s time is the aggregate of individuals’ time and is often spent carelessly.  When a member of an organization spends time on any endeavor, the organization’s time is being spent.  When groups are formed, the expenditure of time multiplies.  Time is the one resource that cannot be increased by persuasive salespeople, creative marketing, strategic partnerships, or other strategy; it must be managed.
     “Everyone” in business knows that “time is money;” it only makes sense that time should be budgeted as carefully as financial resources.  Like ZBB (Zero-Based BudgetingPart 1), Zero-Based Scheduling (ZBS) can be approached in two ways; one ends at zero, the other begins there.
     One method of Zero-Based Scheduling (ZBS) parallels personal ZBB, ending with zero time.  An entire day is scheduled, including breaks, meals, and sleep.  Thirty-minute blocks of time are common, but can be varied as needed.
     Creating an effective zero-based schedule in this manner requires that task durations be known to a reasonable degree of accuracy.  Often, this is not the case; therefore, the first several iterations of ZBS may be less than satisfying.  Scheduling of repeated, or similar, tasks will improve with experience.
     Advocates of this scheduling method claim several benefits:
  • “Personal time is protected.”  Establishing a clear end time for “work” activities allows sufficient time to pursue personal interests that may otherwise be neglected or forsaken.
  • “Less “in-between” time is wasted.”  Short periods of time between scheduled tasks do not support “deep-dive” work on large or important projects.  A zero-based schedule includes smaller tasks in these time slots to maintain a high level of productivity.
  • “Decision fatigue is reduced.”  Preplanning a day’s activities eliminates the rushes of “what now?” anxiety that accompany unscheduled time.
  • “Focus on goals is maintained.”  A fully-booked schedule reminds the user what is important, discouraging frivolous or unproductive activities.
  • “Productivity is sustained.”  Defined durations for task completion minimize manifestations of Parkinson’s Law, where tasks take longer to complete than necessary, simply because additional time is available.
     While these benefits sound wonderful, some skepticism is in order.  The claims described above are somewhat misleading, overgeneralized statements that do not adequately reflect the diversity of tasks required by a broad array of professions.  Perhaps an independent novelist can maintain a schedule for writing, coffee-drinking, proofreading, dog-walking, and editing, but production-support and customer-service personnel, among others, have much less control over their daily activities.
     Protecting personal time requires boundaries that a schedule cannot create.  In a culture where “off-hours” work is expected by managers that do not respect subordinates’ personal time, a zero-based schedule will be quickly defeated and dismantled.  The same problem may exist within “working hours” if managers routinely redirect employees without regard for current work in progress.
     Wasted in-between time may be reduced with a zero-based schedule.  However, a more effective strategy is to group tasks such that fewer short in-between periods exist on the schedule.  Short-duration tasks can still be inserted when an unplanned in-between time occurs, such as when a task requires less time to complete than was predicted.
     Decision fatigue is more likely to be displaced than reduced.  That is, its experience is concentrated in the planning period rather than disbursed throughout the scheduled period.  Prioritization requires decision-making; comparable levels of fatigue are likely to be induced in either case, as it is largely dependent on the individual’s perception of the process.
     The existence of a schedule does not create, or even foster, focus.  Focus is an internal phenomenon, subject to the drives and distractions of the individual.  A schedule can merely provide reminders of previously-stated goals.  As such, it is a helpful tool, but it is not a fool-proof guide, as many advocates portray it.
     The existence of a schedule also cannot sustain productivity.  An individual’s productivity is effected by many factors, many of which are out of his/her control.  Circadian rhythms, illness, and fatigue are physical factors that can effect an individual’s ability to perform.  Personal issues and the task environment may also create distractions or suboptimal conditions that effect an individual’s productivity.
     The schedule acts as a reminder of where one wants to be, or believes s/he could be, in his/her work progression, but it cannot drive that progression.  It may ensure that one task ends and another begins, but it cannot ensure the quality or completeness of the work.
     Routine tasks can be scheduled with a high level of accuracy, resulting in reliable schedule compliance.  However, unique, original work, such as development projects of various types, may have highly unpredictable durations.  Discoveries may lead to new development paths that could not be pursued in a predefined schedule.  Compliance to a fully-loaded schedule is extremely difficult when engaged in this type of work.  In no way does this suggest that no plan should be made, only that it should be accepted at the outset that it will likely change.
     Requiring a fully-loaded schedule implies that unstructured time is wasted time.  Periods of thought and exploration that have not been predefined foster creativity and discovery that may be far more valuable than anything that could have been scheduled.  Spontaneity is squelched and opportunistic pursuits are precluded in a fully-loaded schedule; there is no chance to accommodate energy fluctuations or illness.  For example, a particularly strenuous – physically or mentally – session may require a recovery period before the next task can be effectively executed.  In this case, supporting well-being with a recovery period should be the highest priority, though it was not scheduled because it was not foreseen.  Schedule flexibility is key to overall productivity.
 
     The second ZBS method is a more effective alternative for many.  It parallels ZBB for business, beginning at zero; all time expenditures must be justified in order to be scheduled.  In an environment where an individual’s schedule is heavily influenced by external forces, this approach is integral to his/her productivity.  When several managers, project leaders, department heads, and coworkers can each reserve time on a person’s calendar, that person’s prospects for accomplishing anything of value can quickly evaporate.
     To maintain a zero-based schedule in this type of environment, an individual must have the autonomy to prioritize activities and to decline requests of his/her time.  In the production-support example cited above, unplanned downtime and improvement projects must take precedence over mindless meetings.
     In fact, ZBS is an effective way to eliminate pointless meetings.  Finding no value in attendance, attendees will deprioritize an unproductive meeting and remove it from their schedules.  As attendance dwindles, the meeting organizer is forced to accept the group’s perception, cancelling or reconstituting the meeting to provide value to participants.  Recurring meetings that have outlived their usefulness, or have diverged from their original intent, can be eliminated or realigned through this process.
     It should be clear that a zero-based schedule may have long periods of unallocated time.  This does not suggest, however, that the person is aimless or squandering time.  It simply means that the person has the flexibility to pursue the activity that is the highest priority at that time.
     Autonomy over one’s schedule also provides the flexibility to suggest alternative times for meetings that are deemed necessary.  This allows the person to protect his/her most productive times for activities that require high levels of energy and concentration, while attending meetings during the lulls.  Changeovers and other activities that may require special attention can also be sheltered in the schedule this way.
     To ensure continued productivity, a prioritized task list should accompany an individual’s zero-based schedule.  Upon completion of each task, the next on the list is undertaken.  During the in-between times that occur, a lower-priority task may be undertaken, simply because it can be completed in the time available.  This does not create decision fatigue; it is merely a process of comparing the estimated time required to the time available.
     The prioritized list also contributes more to one’s focus on goals than a fully-loaded schedule.  A list of required activities that stretches into the foreseeable future is more motivating than defining a time to end one activity and begin another.  It can provide a sense of purpose and direction that a shorter-term view cannot, a stronger deterrent to manifestations of Parkinson’s Law than a fully-loaded schedule.  A person can also get back on track immediately following an emergency situation without the need to review and reschedule activities.
 
     Managers can support their teams’ ZBS efforts by establishing guidelines for meetings and other requests for team members’ time.  Limits may be set on the number of attendees, ensuring that all those in attendance have an opportunity to contribute.  Without opportunity to contribute, there is likely little justification for the time expenditure.
     Limits may also be set on the duration of any single meeting or the total in any day or week to ensure adequate time for projects, experimentation, and contemplation.  A variation on this approach is to declare “meeting-free days,” reducing the strain of meeting preparation, attendance, and follow-up on team members’ attention.  To make the most of the meetings that remain, review “Meetings:  Now available in Productive Format!” for more tips.
     Managers may need to review requests that team members have declined.  A decision may be overridden, but it must be done judiciously to maintain a culture of autonomy.  It may be more prudent to assign the task to another individual possessing the requisite knowledge and skills.  A manager could also accept the assignment oneself, until such time that a qualified team member is available to assume responsibility.
 
     An individual’s attempts to utilize Zero-Based Scheduling can be thwarted by unsupportive organizational norms and policies.  For best results, upper management must perceive the value of each team member’s critical assessment of the demands on his/her time.  If only value-added activities are allowed to reside on team members’ schedules, the entire organization becomes more productive.
 
     For additional guidance or assistance with Operations challenges, feel free to leave a comment, contact JayWink Solutions, or schedule an appointment.

References
[Link] “How a Zero-Based Schedule Supercharges Your Productivity & Performance.”  The BestSelf Hub, January, 23, 2020.
[Link] “How a ‘Zero-Based’ Calendar Can Supercharge Your Productivity.”  Melanie Deziel; Inc.com.
[Link] “Let’s Audit Your Calendar.  It Will Only Hurt A Little.”  Darrah Brustein; Inc.com.
[Link] “Three Reasons You Should Create a Zero-Based Schedule.”  Andrea Silvershein; Ellevate, 2018.
[Link] “Help Your Team Spend Time on the Right Things.”  Ron Ashkenas and Amy McDougall; Harvard Business Review, October 23, 2014.
[Link] “Your Scarcest Resource.”  Michael Mankins, Chris Brahm, and Greg Caimi; Harvard Business Review, May, 2014.
[Link] “Management Tools 2017:  An executive’s guide.”  Darrell K. Rigby; Bain & Company, Inc., 2017.
[Link] “The Surprising Impact of Meeting-Free Days.”  Ben Laker, Vijay Pereira, Pawan Budhwar, and Ashish Malik; MIT Sloan Management Review, January 18, 2022.

 
Jody W. Phelps, MSc, PMP®, MBA
Principal Consultant
JayWink Solutions, LLC
jody@jaywink.com
]]>
<![CDATA[Viva Manufacturing!]]>Wed, 05 Oct 2022 14:30:00 GMThttp://jaywinksolutions.com/thethirddegree/viva-manufacturing
     October 7, 2022 is National Manufacturing Day in the United States.  It is a day of special events introducing future professionals to myriad career opportunities in a variety of manufacturing industries.  One day isn’t really enough, though, is it?  The entire month of October is dedicated to promoting the impact manufacturing industries can have on a region’s economy, quality of life, and individuals’ career satisfaction.
     Since 2012, the first Friday of October each year has been designated National Manufacturing Day.  The purpose is to bring attention to the wonderful world of manufacturing, dispelling myths and negative stereotypes that hinder recruitment of future talent.  Events include facility tours and open houses, virtual presentations, mobile interactive experiences, career fairs, a bus tour, and more.
     Diverse industries are represented in this year’s events.  Lasers, electronics, industrial equipment and robots, cabinetry, CNC machines, materials (metals, plastics, composites), healthcare products, and more are showcased in a mix of onsite and remote presentations.  Nearly 500 registered events are scheduled for this year’s celebration of makers and making.
 
     Anyone questioning the value of manufacturing careers to future generations should consider the following:
  • The manufacturing sector employs more than 12.8 million people in the US, yet reported over 800,000 job openings in July.
  • Manufacturing workers earn approximately 20% more, on average, than the average for all nonfarm workers in the US.
  • More than half of all private-sector Research & Development conducted in the US is performed by manufacturers.
  • Manufacturing is one of only three industry sectors in which, historically, societal wealth and prosperity is generated (agriculture and extraction industries are the other two).
  • Export of manufactured goods has increased substantially from past decades and is expected to continue its growth trajectory.
     To reinforce this last point, consider data from four southeastern US states – Georgia, North Carolina, South Carolina, and Tennessee.  During the decade following the “Great Recession,” manufacturing output in these states, shown in Exhibit 1, experienced significant growth.  In that same period, exports from Georgia and South Carolina exploded!  Other regions of the country exhibit similar trends.  The nation’s exports peaked in 2018, before the COVID-19 pandemic effected commerce worldwide.  Although the trend was interrupted, exports are expected to resume the long-term upward trend.
     Online resources provide information needed to host or attend a Manufacturing Day event, including a listing on the official website.  Affiliated organizations offer advice on participation, partnering with teachers and government officials, and generating publicity for events.  The following tips eschew the standard formula used to plan and host events, seeking to create a more engaging, memorable, and inspiring Manufacturing Day.
     Go beyond “canned” presentations.  Lectures, videos, and guided tours are useful tools, but should not comprise the entirety of the event.  Short attention spans require a more interactive experience to maintain interest.  Suggestions:
  • Invite students to take part in a designed experiment, where the outcome is not yet known.
  • Encourage them to share observations, thoughts, ideas, and predictions.
  • Share with them the challenge of conducting such an experiment – planning, data collection, etc. – and the excitement of discovery.
  • Include as much hands-on participation as can be safely permitted.
     Focus media – social and traditional – on the processes and people required to bring products to market.  Too often, the behind-the-scenes “magic” is obscured by flashy marketing of the end product.  If people are not shown where the “whiz-bang” of a product comes from, it should be no surprise when they assume it is irrelevant or uninteresting.  Suggestions:
  • Demonstrate that “technology” includes much more than programming, screens, and robots.
  • Share “fun facts” about the development of products, processes, equipment, and technology that reinforce the “there’s more to technology” message.  Short formats and visuals are likely to be most effective; use mixed media for maximum impact.
  • Ask current practitioners with various specialties and backgrounds to tell their stories.  Sharing the reasons they chose manufacturing careers and what continues to excite them about manufacturing.  To attract diverse talent, stories from those with “unconventional” backgrounds (e.g. the artist that became a fabricator or pre-Med major that switched to Engineering) are particularly helpful.
     Explain the history of products and process technologies (briefly, of course).  Show the timeline of development extending into the future, highlighting breakthroughs that members of the audience could potentially contribute during their manufacturing careers.
Describe links between manufacturing and famous inventors, encouraging those that aspire to be innovators and entrepreneurs.  Explain that these are complementary, not contradictory, pursuits.
     Promote internship and apprenticeship opportunities to participants that demonstrate interest and aptitude.  If your organization does not currently offer such a program, consider proposing one to executives, highlighting the benefits for both students and mentors.
     Invite the parents of all student participants to join the event.  Typically, only a few chaperones accompany student groups; however, it is likely that all of the students’ parents have been bombarded with misinformation about manufacturing for their entire lives!  Correcting the record, in the minds of parents, removes a significant impediment to recruiting talented young people.
     Don’t limit activities and advocacy to one day or month each year.  Partner with teachers throughout the year to reinforce the connection between manufacturing and classroom topics.  “When will you ever use this, you ask?  Let me show you.”  For example, seeing a plating process up close is far more engaging than a Chemistry textbook with a diagram labelled anode, cathode, and electrolyte!  Events held for Manufacturing Day could be repeated throughout the year to reach a broader audience; virtual presentations can be made available for use at any time.
 
Related websites
Manufacturing Day – the official website
The Manufacturing Institute
Creators Wanted
National Association of Manufacturers – NAM
Manufacturing USA – a public/private partnership
Tips for a High Impact Event – only the dates have changed.
7 Fun Manufacturing Month Ideas
 
     Though Antoine de Saint Exupéry’s exact quote, or proper translation, is in dispute, it is nonetheless profound.  One formulation is as follows:
     “If you want to build a ship, don’t drum up the men to gather wood, divide the work, and give orders. Instead, teach them to yearn for the vast and endless sea.”
     To paraphrase in the manufacturing context, discussing the job market, financial implications, or any tangible societal benefits that manufacturing industries provide will be only marginally effective.  Instead, those of us in the industry need to share our passion, to foster curiosity, and to nurture the human impulse to build.  Teaching younger generations to yearn for discovery will turn many of the endless possibilities into reality.
 
     If your organization is unable to host a Manufacturing Day event this year, it is a good time to start planning for next year… or next month.  There is no need to wait until October to launch your advocacy program.  Make every day Manufacturing Day!
     For additional guidance on Operations challenges or assistance with manufacturing industry advocacy, feel free to leave a comment, contact JayWink Solutions, or schedule an appointment.
 
Jody W. Phelps, MSc, PMP®, MBA
Principal Consultant
JayWink Solutions, LLC
jody@jaywink.com
]]>
<![CDATA[Be A Zero – Part 1:  Zero-Based Budgeting]]>Wed, 21 Sep 2022 14:30:00 GMThttp://jaywinksolutions.com/thethirddegree/be-a-zero-part-1-zero-based-budgeting     Interest in Zero-Based Budgeting (ZBB) is somewhat cyclical, rising in times of financial distress, nearly disappearing in boom-times.  This can be attributed, in large part, to detractors instilling fear in managers by depicting it as a “slash and burn” cost-cutting, or downsizing, technique.  This is a gross misrepresentation of the ZBB process.
     ZBB is applicable to the public sector (various levels of government), private sector (not-for-profit and commercial businesses), and the very private sector (personal finances).  Each sector is unique in its execution of ZBB, but the principle of aligning expenditure with purpose is consistent throughout.
     This installment of “The Third Degree” describes the ZBB process in each sector, compares it to “traditional” budgeting, and explores its advantages and disadvantages.  Alternative implementation strategies that facilitate matching the ZBB approach to an organization’s circumstances are also presented.
Personal Budgets
     An individual’s income and expenses are relatively predictable and simple to track.  A teenager’s allowance, a graduate student’s stipend, a professional’s salary, and a retiree’s pension are all stable sources of income.  Likewise, most expenses are routine – mortgage or rent, utilities, food, clothing, etc.  Effective budgeting allows an individual to meet these routine obligations while protecting against the unexpected.
     Zero-Based Budgeting (ZBB) assigns every dollar of an individual’s income to a specific purpose.  First, the essentials are deducted – housing, food, etc.  Then, “quality of life” expenses are deducted – those that are not critical to survival, but the deprivation of which may cause an individual’s budget discipline to falter.  Another way to think of this category of spending is as the rewards for discipline in creating and adhering to a planned budget.  This could include a daily visit to Starbucks, a weekly Ben & Jerry’s binge, or other treat that reinforces the budget discipline needed for the person to achieve his/her financial objectives.
     An additional category of discretionary spending may also be added.  Entertainment, travel, and various hobbies may be budgeted here.  This category is typically the first to be cannibalized when there is a shortfall or emergency expenditure.
     The remainder is divided among savings objectives, such as a vacation, down payment on a house, or investments, leaving a zero balance.  The amount allocated to each reflects the relative priority in the current budget period.  As these priorities change, the allocations can be adjusted to achieve a savings goal on a desired timeline.
     A schematic example of a personal zero-based budget is shown in Exhibit 1.  The number of entries will vary, depending on an individual’s circumstances.  For example, a mid-life professional may have several essential expenditures, while a teenager’s budget may have none, being split between discretionary spending and aspirational saving.  There may also be multiple sources of income, each accounted for on its own line.  Use of a spreadsheet minimizes calculation errors; formatting can provide visual cues that information is missing.  Though the preceding discussion has referenced individuals for simplicity, a personal budget could also be created for a family, club, or other group.
     While personal ZBB ends with zero, public- and private-sector ZBB begins with zero.  Once obligations (essentials) are met, a personal budget is based on the individual’s (or group’s) preferences; there is no subsequent judgment of the priorities chosen.  Government and commercial budgets, in contrast, are typically prepared by one group and reviewed and approved by another.  Preparers must justify each expenditure in the budget in order for reviewers to accept it.
     Public- and private-sector ZBB are very similar, bearing little resemblance to personal ZBB.  Zero-Based Budgeting in commercial enterprises is the topic to which we now turn, followed by a discussion of its use in Government that highlights key differences.
 
Business Budgets
     Enterprises of all sizes frequently fall into a routine of using incremental budgeting to determine spending levels for the next fiscal period.  Incremental budgeting applies a decidedly uncreative equation to establish a budget:
     This period’s budget  +/-  x %  =  Next period’s budget,
where “x” depends on the economic outlook, market conditions, executive’s confidence, and other indicators that often remain undefined.  Or, perhaps, a roll of an umpteen-sided die left over from the CEO’s days as a Dungeon Master (the Dungeons & Dragons type.  C’mon!).
     Traditional budgeting techniques like this answer first “How much should we spend?” and, only later, “What should we spend on?”  Prior to answering “How much?,” ZBB requires definition of what should be funded and how funding decisions will be made.
     Though it is unrealistic to expect past spending information never to enter the discussion (historical costs provide valuable inputs to estimates of future costs), ZBB is much more forward-looking than traditional budgeting processes.  It rejects the “We’ve always done this” argument, requiring an answer to “Why should we do this?”
     A four-step process is sufficient to define most ZBB implementations.  A description of each step is provided below.
(1) Define Decision Units.  A decision unit is a logical division of an organization in which expenditures are directed toward a common purpose.  Grouping expenditure “types” simplifies creation and management of budgets, though these groupings may differ from the divisions that are familiar to most.  Functional departments, business units, and product lines are divisions commonly used to manage an organization.  However, a one-to-one mapping of these divisions to decision units is unlikely.
     For example, a single production line may require several decision units to competently manage its budget.  These could align with individual products or entire product families.
Decision units may also be defined by functions served by a department.  For example, a production facility’s Engineering department may include Quality, Manufacturing, and Industrial decision units.  Similarly, Accounting department decision units might include Payables, Receivables, and Delinquents.
     It is important to note that both capital and expense budgets are represented by decision units.  Also, decision units defined for a product line may provide inputs to several departmental budgets, depending on the structure of the organization.  Whatever the set of decision units chosen, it is incumbent upon managers to structure budget requests such that information collection, presentation, and review are as efficient as possible.
 
(2) Generate Decision Packages.  Decision packages are the “building blocks” of budgets, where answers to the “What?,” “Why?,” and “How much?” questions are proposed.  A simple decision package template is shown in Exhibit 2.
     Each decision unit may require several decision packages to adequately depict the budgeting options available.  Each decision package contains the following information:
  • Objective (reason for expenditure)
  • Level of Effort (see discussion below)
  • Description of actions to be taken (What/How)
  • Costs to be incurred at this level of effort
  • Benefits to be gained at this level of effort
  • Performance metrics (supporting data)
  • Alternatives (alternate What/How)
     The information content of a decision package is mostly straightforward.  The Level of Effort, however, warrants further exploration.  It corresponds to a level of funding required to achieve the outcomes, or benefits, desired.  The first level of effort defined for each decision package is the minimum level.  The minimum level of effort includes only the core functions of the decision unit, the most basic reasons for its existence.  It may also be called the minimum level of service.
     Defining the minimum level of service is critical to successful budgeting, as it ensures that managers understand their fundamental responsibilities as fiduciaries of their organizations.  It also supports sound decisions regarding elimination and outsourcing of functions.  The minimum level may reflect changes in Operations proposed in other decision packages or decision units, but always addresses the highest priorities of the decision unit.
     The current level of effort is typically included as a baseline for comparison, though this should be done with caution.  Unless operational improvements demonstrate that the current level of service can be provided at lower cost, this decision package may do little more than feed managers’ status quo bias, becoming the default option.  When this occurs, a traditional budget is created, albeit with a veneer of objectivity provided by an ostensible ZBB process.
     Additional reduced levels of effort, at lower cost than the current level, may also be defined.  Similarly, multiple enhanced levels of service, in excess of the current level, may be considered.  The highest level of service proposed (largest budget) is sometimes called the fully funded option.
     Use of the terms “reduced” and “enhanced” is unfortunate.  These terms reference the current level of spending, a key attribute of traditional budgeting that ZBB intends to abandon.  As stated at the beginning of this section, it is unrealistic to expect current-period budgets to have no influence on next-period budgets, but these terms should be used – with caution – until better ones are devised.  Use of these terms may make a paradigm shift from traditional to Zero-Based Budgeting more difficult for those that are already resistant to change or simply distracted or overwhelmed.
     A graphical representation of a hypothetical decision unit’s potential levels of effort is shown in Exhibit 3.  This type of display is useful for summary presentations and reports.
(3) Rank Decision Packages and Create Budget Proposal.  All decision packages from all decision units are evaluated and prioritized to facilitate funding decisions.  To streamline this effort, managers responsible for multiple decision units (e.g. leaders of departments that perform multiple functions) should rank their own decision packages prior to entering them in the organizational “pool.”  Doing so makes it clear at all times which decision package from each subset is to next be scrutinized.  This process is presented graphically in Exhibit 4.  Only Engineering and Accounting departments, previously cited examples, are shown for simplicity.  A real organization is likely to have many more decision units filling the pool.
(4) Prepare Detailed Budgets.  Executives prepare pro forma financial documents to make operational decisions about future fiscal periods.  Anticipated revenues and other financial projections and objectives are used to approximate the next period’s budget.  With this limit in mind, the prioritized “stack” of decision packages are reviewed.  If the justifications are valid – that is, the expenditures are aligned with the organization’s objectives – decision packages are approved up to the budget limit set.  This is depicted by the “Budget” line in Exhibit 4D.
     The diagram in Exhibit 4D depicts a scenario in which all decision packages below the budget limit are approved and all those above are rejected.  While those above the budget limit will always be rejected, those below may or may not be approved.  For example, the first enhanced service package for delinquent accounts (AD-ENH1, #15) may be rejected (disputed justification, misaligned objectives, etc.).  This causes the Budget line to rise from #19 to, say, #21.  However, #20 (AD-ENH2) is rejected by default because the predecessor it is built upon (AD-ENH1) has been rejected.  For this reason, the Budget line ultimately rises to include, say, #22 (EI-CUR), avoiding service reductions in the Industrial Engineering group.  This Budget line shifting process example is depicted in Exhibit 5, where it can be seen that the final two enhanced service packages (EQ-ENH2, EI-ENH) remain unfunded.
     The final budget, as depicted in Exhibit 5, informs each decision unit of the decision packages that have approved.  From this, a detailed operating budget can be generated for each unit to manage expenditures throughout the upcoming fiscal period.
 
Government Budgets
     ZBB’s greatest, or at least earliest, claim to fame is Jimmy Carter’s implementation, in the 1970s, as Governor of Georgia and, subsequently, as President of the United States.  Or perhaps it is Ronald Reagan’s discontinuation in the early 1980s, beginning an era of ballooning federal spending that continues to this day.
     The process required to implement ZBB in government parallels that for businesses, with some key differences in characteristics:
  • As large and complex as corporate structure can be, it pales in comparison to government bureaucracy.  Myriad layers of administration ensures that those making funding decisions possess no real understanding of the implications of accepting or rejecting spending proposals.
  • “Justification,” in the government spending context, is a term used incredibly loosely.  In business or personal budgeting, it implies a cost/benefit analysis has been conducted, even if informally.  In government, the cost to the decision-maker is zero, while the benefit may be the support of a significant voting bloc, coveted committee appointment, or other source of “power” and influence.
  • Repercussions for misallocation of resources are nearly nonexistent in government.  While businesses and individuals must recover by reducing other spending, increasing sales, or finding other offsets, governments encounter few impediments to borrowing (or “printing”) money or increasing revenue through taxes and fees.  The source of difficulty caused by misallocation of resources may be difficult to identify, as governments are far more opaque than markets or personal spending habits.
     ZBB meets tremendous resistance in governments; it is difficult not to believe the worst possible reasons for this.  An earnest effort exposes the “dark underbelly” – waste, fraud, cronyism, and all manner of dirty deeds that make a political machine “work.”  When money = power, a reduced service package is anathema and considered by most a waste of time.  This attitude is common among those that present a slight slowing of spending growth as a budget cut.
 
Dis/Advantages of Zero-Based Budgeting
     Many times, an advantage to one is a disadvantage to another; they are two sides of the same coin.  The label applied depends on one’s perspective or the role one plays in a given scenario.  For this reason, characteristics of Zero-Based Budgeting are presented below without normative labels; the value of ZBB must be assessed by the reader.
  • ZBB ensures allocation of resources supports chosen objectives.
  • ZBB does not guarantee that priorities are ideally set; the human element, with all its faults, remains; disagreements may persist in the final budget.
  • ZBB can be time-consuming and resource-intensive when large amounts of data are collected and analyzed to generate a clear view of the organization’s position.
  • ZBB prevents arbitrary spending increases.
  • Poor management practices are not eliminated.  Workforce reductions, for example, may be justified by ZBB in the minds of some managers, when reassignment – temporary or permanent – would yield better results.
  • Strong executive will is needed to implement and sustain ZBB practices.  The increased workload of managers – real or perceived – fosters resistance; reduced funding of preferred projects intensifies it.  If executives’ commitment wavers, the crack in the foundation will be exploited until the edifice crumbles.
  • The learning curve requires expectation management.  The first planning cycle may not be executed flawlessly; a continuous improvement mindset is essential for successful implementation.
 
Implementation Strategies
     The most effective and most sustainable implementation is one that is customized to an organization’s unique circumstances.  Neglected or historically mismanaged units may be brought in line with organizational objectives prior to evaluating less problematic units, for example.  There are five basic strategies for ZBB implementation with seemingly endless variations possible.
     A small organization, with relatively few decision units, may choose an “All-In” strategy, where every unit conducts a complete ZBB process every fiscal period.  This can be a risky proposition for larger organizations; the opportunities for error, “lazy” management, and political maneuvering increase exponentially with the number of decision units.  These risks are in addition to the learning curve effects mentioned previously.  Most organizations benefit from using an incremental strategy, not to be confused with incremental budgeting.
     A “Top-Down” strategy considers expenditures at a divisional, business unit, or other high level.  Detailed analysis of expenditures may not be completed; traditional budgets are often key inputs for initial decisions.  Thus, it is more a portfolio review than a true ZBB process.  Units that are deemed hopelessly unprofitable or otherwise misaligned with organizational objectives are divested, or left to languish, as funds are diverted to more promising ventures.  Divestiture reduces the workload required to create a genuine Zero-Based Budget.
     At the opposite extreme, detailed analysis begins at the task level.  Nonvalue-added (NVA) activities are identified and eliminated or reduced, while efficiency gains are sought for value-added (VA) activities.  Analysis then proceeds to, say, department-level activities and continues through the organizational hierarchy, identifying worthwhile expenditures and alternative methods at each stage.  This “Bottom-Up” approach is highly compatible with lean initiatives; it is a continuous improvement strategy that may require several fiscal periods to progress through the entire hierarchy.
     A “Middle-Out” strategy is one in which the starting point is somewhere between the Top-Down and Bottom-Up strategies.  That is to say, it is a rather broad term.  This implementation strategy is most relevant to a large organization in which projections cause concern for a specific unit.  The situation of concern may be temporary in nature, but sufficiently impactful to warrant additional scrutiny.
     The first unit analyzed is the “Middle;” the “Out” follows only when the process is deemed valuable.  Successful implementation in the Middle fosters support for ZBB, increasing the likelihood that it will be adopted more broadly.  This approach can be an effective alternative to the “all-or-nothing” divestiture decisions common in the Top-Down approach.
     A “Checkerboard” strategy can be implemented in several ways.  The most basic is to conduct ZBB in one half of an organization’s decision units in one period and in the other half the following period.  Other, less literal, variations are also worthy of consideration.
     Both the proportion of units and the number of periods in a cycle can be varied.  To address concerns of a potentially overwhelming workload, a smaller proportion of units can be analyzed each period. This is a particularly attractive option in large organizations.
     The two-period cycle may also be extended to correspond with the proportion of units being analyzed (e.g. one third of units analyzed each period; each unit analyzed every third period).  As the planning horizon lengthens, however, the accuracy of projections declines; traditional budgeting practices may be used in the interim periods to compensate.  Reintroducing traditional methods may jeopardize the sustainability of the paradigm shift, as discussed previously.
     An adaptation of the Checkerboard approach can address concerns regarding workloads, learning curves, and planning horizons simultaneously.  Initiating ZBB, in Checkerboard fashion, with a three-period cycle limits the workload while the organization ascends the learning curve.  As each unit becomes proficient, its planning horizon can be reduced to two periods.  As ZBB becomes ingrained in management and culture, a transition to an All-In approach can be executed, minimizing opportunities for traditional thinking and practices to imperil the new paradigm.
     Hybrid strategies, such as combining the Checkerboard and Bottom-Up approaches, can be implemented, providing further flexibility.  Any number of hybrids could be conceived to address market conditions, workload concerns, or other matters that influence the decision to pursue or forego ZBB.
 
Final Thoughts
     ZBB is the most natural form of financial planning.  We’re all familiar with the “starving artist” and “poor college student” tropes.  What form of budgeting are they most likely to use?  It seems obvious that resource-limited individuals default to Zero-Based Budgeting.  They may not do it formally or even realize they are doing it, but “survival mode” is zero-based.
     The notion of survival mode applies equally to businesses and other organizations, as the “cash-strapped startup” trope reminds us.  An individual or organization that maintains this approach will never be subject to the excesses and the subsequent, reviled, cuts that so often become necessary when traditional budgeting practices are employed.
     The subject of alternatives, though mentioned, has not been discussed in detail.  This was less an oversight than an attempt to simplify the presentation of the decision package ranking process.  If an alternative method of providing a certain level of service is approved, it replaces the original decision package in the priority stack.  An example is shown in Exhibit 6.  If the cost of the alternative is significantly lower than the current method, the Budget line may shift, reflecting the impact its selection has on total cost.
     There is a substantial literature discussing ZBB, much of it discouraging its use, particularly in Government.   While the motivations of some authors are suspect, others present valid concerns more objectively.  Serious consideration of the pro and con arguments should be undertaken before passing judgment on ZBB.  If implementation is ultimately pursued, a thorough understanding of these arguments facilitates the selection of an appropriate strategy.
     Anti-ZBB rhetoric often focuses on case studies of poor, or failed, implementations.  Such case studies are not definitive proof of the method’s deficiencies, however.  More often, they serve as definitive proof of deficient planning and implementation and, thus, provide important lessons to improve future endeavors.
     Aspects of failed implementations commonly cited are diminished revenues, damage to brand and culture, and stunted growth.  These outcomes are sited to support claims of ZBB’s inferiority.  In reality, however, such outcomes demonstrate that the organization’s objectives and the linkage of expenditures to those objectives were not well understood.  Heeding the caveats provided in this treatise regarding learning curves and implementation strategies can prevent such outcomes or their recurrence.
     Consequences of alternative strategies, particularly continued use of traditional budgeting methods, should also be considered.  Imagine the impact of bankruptcy, precipitated by unchecked spending, on culture, brand, and growth!
     One anti-ZBB tirade asserts that “government agencies are unlikely to benefit from an annual complete ZBB process, as returns would decrease significantly each subsequent year compared to initial savings.”  Decreasing returns, in this formulation, provide compelling evidence that ZBB is achieving its intended purpose!  Perhaps government agencies would not benefit, but their constituents probably would.
     In this same work, the following claim is made:  “More so than federal agencies, private corporations can afford the costs associated with… ZBB…”  Revisit the “Government Budgets” section for a reality check.
     Despite its disturbing tone, the piece provides a useful summary of ZBB’s key characteristics.  It has been reproduced in Exhibit 7.  To further elucidate the contrast between ZBB and traditional budgeting, a comparison of planning sequences has been reproduced in Exhibit 8.
     Successful implementation of any strategy relies on the alignment of incentives to objectives.  For example, executive bonuses with a significant component related to ZBB implementation provide substantial motivation to ensure that all members of the organization receive sufficient training and guidance to ensure success.  Any policy that discourages “lazy” management – expending the least effort possible, simply maintaining the status quo – should be considered.
     To reiterate, the terminology used in ZBB is unfortunate, as it continues to reference past or current spending levels.  It doesn’t end there.  “Rightsizing,” when used correctly, is a reasonable description of ZBB’s purpose – to match expenditures to performance.  Unfortunately, this term has been tainted by its replacement of “downsizing” when that term became toxic.  Likewise, “reorganization” may be necessary to align responsibilities with logical decision units, but this term also fell victim to the buzzword wars waged in the business press.
     No matter the terms used or the implementation strategy chosen, successful Zero-Based Budgeting hinges on Executive commitment, incentive alignment, and continuous improvement.  As the same elements can be used to define sound management in any context, the controversy surrounding ZBB is puzzling.
 
     For additional guidance or assistance with Operations challenges, feel free to leave a comment, contact JayWink Solutions, or schedule an appointment.
 
References
[Link] “What is Zero-based Budgeting?”  Anjali J.; The Investors Book, 2022.
[Link] “Zero-Based Budgeting:  Zero or Hero?”  Mark Hopkins; Deloitte Development LLC, 2015.
[Link] “Zero-Based Budgeting.”  Peter A. Pyhrr.  In Handbook of Budgeting, 6ed.  William R. Lalli; Ed, John Wiley & Sons, Inc., 2012.
[Link] “Zero-Base Budgeting:  Modern Experiences and Current Perspectives.”  Shayne C. Kavanagh; Government Finance Officers Association, 2011.
[Link] “Five myths (and realities) about zero-based budgeting.”  Shaun Callaghan, Kyle Hawke, and Carey Mignerey; McKinsey & Company, 2014.
[Link] “Management Tools 2017:  An executive’s guide.”  Darrell K. Rigby; Bain & Company, Inc., 2017.
[Link] “Zero-Based Budgeting: What It Is and How to Use It.”  Julia Kagan; Investopedia, 2022
[Link] “What Is Zero-Based Budgeting?”  Lauren Schwahn; NerdWallet, 2020.

 
Jody W. Phelps, MSc, PMP®, MBA
Principal Consultant
JayWink Solutions, LLC
jody@jaywink.com
]]>
<![CDATA[Process Dominance – Identification and Management]]>Wed, 07 Sep 2022 14:30:00 GMThttp://jaywinksolutions.com/thethirddegree/process-dominance-identification-and-management     To be effective in any pursuit, one must understand its objectives and influences.  One influence, typically, has a greater impact on process performance than all others – the dominant characteristic of the process.  The five main categories of process dominance are worker, setup, time, component, and information.
     Processes require tools tailored to manage the dominant characteristic; this set of tools comprises a process control system.  The levels of Operations Management at which the tools are employed, or the skills and responsibility for process performance reside, differ among the types of dominance.
     This installment of “The Third Degree” explores categories of process dominance, tools available to manage them, and examples of processes with each dominant characteristic.  Responsibility for control of processes exhibiting each category of dominance will also be discussed in terms of the “Eight Analogical Levels of Operations Management.”
     The supplement shown in Exhibit 1 provides a summary of the five main process dominance categories, with examples of processes that typically exhibit each.  Process control tools in common use and the levels of Operations Management at which they are typically employed are also given.  Readers are encouraged to reference the summary table while reading the following discussion of process dominance and during process development activities.
     The lists of tools and processes provided in the supplement are representative, but not exhaustive.  A process development team can apply the concept of dominance to a unique system by recognizing similarities to established processes.  History of successful control of “traditional” processes can inspire confidence in the ability to create an effective control system when similar characteristics are encountered.  Analyzing the unique properties of a new system informs decisions to modify an existing control system or to develop new tools for monitoring and control purposes.
 
     Worker-dominant processes rely heavily on the aptitude of individuals to achieve quality and consistency.  Technical competence can be attained with training and assessments of various types.  Less obvious, and more difficult to measure, is an individual’s level of engagement.  Engagement is effected by a person’s satisfaction with the work, the environment (both physical and psychological), compensation, opportunity, and other factors.  When considered for a group, these factors influence assessments of morale.
     Management Controls, as a tool for worker-dominant process control, refers, primarily, to an organization’s policies that ensure fair and respectful treatment, commensurate compensation, equality of opportunity, and other influences on morale.  Management Controls are typically discussed as written policies and often generalized to “the Employee Handbook.”  However, unwritten “rules” and expectations are equally, if not more, important than documented policies.
     Its unwritten rules are the foundation of an organization’s culture and culture defines the terms of members’ “psychological contract.”  A psychological contract consists of unwritten, and often unspoken, expectations of employees and the employer; both positive and negative aspects can develop.  When in balance, disruptions are minimized and workplace relationships remain productive.
     Worker-dominated processes are controlled, primarily, at the Ground level, via self-management by individual workers (i.e. diligence, conscientiousness).  Crop Dusters assist by implementing improvements that enhance safety and productivity, contributing to sustained morale.  Drones are responsible for oversight, managing performance and expectations of both workers and higher-level managers.
 
     Setup-dominant processes are characterized by time-consuming or complex startups, followed by stable and reliable performance for large batch production or other long period.  They are made more efficient by means of setup-reduction efforts and tools, such as SMED (Single-Minute Exchange of Dies) and rapid inspection methods for first-piece verifications.
     Process startups require a skillset that differs from that required for efficient production.  Therefore, in some cases, it may be advantageous to dedicate a setup-focused individual to setup-dominant process startups to maximize repeatability and minimize downtime.
 
     Time-dominant processes are subject to “drifting” of important parameters.  Tool and equipment wear, temperature, chemical composition (i.e. ratios), and other gradual changes can effect long-term process performance.  Control of time-dominant processes relies on various types of equipment maintenance, periodic verifications of output, and responsive (minor) adjustments to compensate for any drift in process parameters.
     Responsibility for control of time-dominant processes is, almost exclusively, the realm of Crop DustersEngineering Controls, such as flow meters, pressure switches, relief valves, and thermistors are used to monitor process conditions and prevent hazardous conditions or catastrophic events between adjustments.  Monitoring and adjustment are the keys to time-dominant process control; both are defined in another Engineering Control – work instructions.
 
     Component-dominant processes require strict control of inputs to prevent malfunctions or defective output.  Receiving inspection and sorting can be costly and, therefore, should be last resorts or used only in emergency situations.  In-process poka-yoke devices are preferred, though high reject rates are detrimental to process performance.
     To prevent defective components from effecting a process in either of the above-described ways, two key control methods should be employed.  Supplier development assures that proper control systems are in place at supplier facilities, thereby obviating receiving inspection, sorting, process deviations, and product returns.
     The best control of component-dominant processes comes from applying the principles of “Robust Design.”  Robust Design minimizes the sensitivity of a process to variations in its inputs.  Therefore, less stringent specifications are required, reducing the reject rate and, consequently, cost of components.
     Control of component-dominant processes has been “assigned” to Drones and Hot Air Balloons.  At these levels, sufficiently isolated from day-to-day operations, solutions with broader implications can be pursued.  For example, improved supplier performance with respect to one component may carry over to other products; that is, other processes are improved without significant additional effort.  Future programs may also benefit from a partnership developed in this way, securing long-term prosperity for both parties.
 
     Information-dominant processes require control at all levels of Operations Management.  A modern economy runs on information that is timely, accurate, clear, and concise.  Information travels between all levels of Operations, suppliers, and customers, both current and potential.
     The extensive reach of information makes it difficult to overstate its importance.  In many processes, information is a product, or output, derived from several inputs of information and analysis.  This could scarcely be anything but an information-dominant process.
     Processes with physical outputs and service-centric processes require accurate information to provide the product or service expected.  Dominance, in these cases, is determined by the magnitude of the influence of information relative to any physical activity that takes place.  For example, order-picking is information-dominant because production of the physical items has already taken place.  Item identifications, locations, quantity, due date, and shipping address – the information content – outweighs the physical activity involved in successful execution of the process.
 
     Understanding process dominance helps focus a development team’s efforts on the most efficacious tools of process control.  Early identification of the dominant characteristic simplifies planning and resource allocation decisions.  Other influences cannot be ignored, but to ensure sustainable process performance, the dominant characteristic must be well-understood, closely monitored, and effectively controlled.
 
     For additional guidance or assistance with Operations challenges, feel free to leave a comment, contact JayWink Solutions, or schedule an appointment.
 
References
[Link] Juran’s Quality Handbook, 6ed.  Joseph M. Juran and Joseph A. De Feo; The McGraw-Hill Companies, 2010.

 
Jody W. Phelps, MSc, PMP®, MBA
Principal Consultant
JayWink Solutions, LLC
jody@jaywink.com
]]>
<![CDATA[Eight Analogical Levels of Operations Management]]>Wed, 24 Aug 2022 14:30:00 GMThttp://jaywinksolutions.com/thethirddegree/eight-analogical-levels-of-operations-management     Effective Operations Management requires multiple levels of analysis and monitoring.  Each level is usually well-defined within an organization, though they may vary among organizations and industries.  The size of an organization has a strong influence on the number of levels and the makeup and responsibilities of each.
     In this installment of “The Third Degree,” one possible configuration of Operations Management levels is presented.  To justify, or fully utilize, eight distinct levels of Operations Management, it is likely that an organization so configured is quite large.  Therefore, the concepts presented should be applied to customize a configuration appropriate for a specific organization.
     All functions of the eight levels of Operations Management described herein must be performed for an organization to be sustainable.  The number of levels it uses and the boundaries between them are driven by the available resources and the needs of the organization.
     To analogize the Eight Levels of Operations Management, various types of flying craft are used to symbolize the characteristics of each.  The operating altitude and speed or maneuverability of each represent, respectively, the amount of detail with which each deals and the rapidity of task-switching that may be required.  A summary is provided in the Operations Management Altitude Table of the supplement shown in Exhibit 1.
     Each Level is named for its associated flying craft; visual reference can be made to each using the Icon shown.  The Altitude cited is typical of the craft, but the number is less important than the qualitative message it is intended to impart, such as “much higher than previous level (don’t get mired in details)” or “very close to the situation (details are critical).”
     The People column provides examples of roles that are commonly involved in each level of Operations Management.  However, role definitions and, therefore, responsibilities, may differ among organizations.  The Focus and Purpose columns provide examples of the types of responsibilities typically associated with each level.  These are far from comprehensive; the responsibilities of Operations Managers can vary widely and in multiple dimensions.
     The “Altitude Table” is intended as a quick reference, reminder, or resource allocation decision support.  Thus, it provides examples and visual cues, but little detail.  To provide greater insight into each of the Eight Analogical Levels of Operations Management, more detailed, but still brief, descriptions follow.
 
Satellite
     The International Space Station (ISS) orbits Earth at an altitude of 245 miles.  Great height offers a wide field of view (FOV), but, when combined with a 17,000 mph speed of travel, details are difficult to discern.  A Board of Directors must monitor many trends, events, and other influences on the organization, but should not be involved in widget processing.
     Completing an orbit every 90 minutes or so, the ISS symbolizes the frequency at which global occurrences should be monitored in support of proactive business decisions.  This icon was chosen because of the presence of humans on board.  Many capabilities exist in the types of satellites we may often think of, but they are operated remotely.  To properly support the organization, the highest level of Operations Management must not simply “phone it in,” but be present and engaged in observation, analysis, and decision-making.

Spy Plane
     Analyzing the competitive landscape requires closing the distance substantially to the subject at hand.  At 80,000 ft, details remain obscured, particularly at 2,200 mph!  However, this combination allows high-level managers to stay abreast of market trends, emerging technologies, and actions taken by competitors.
     This icon was chosen because it represents the FOV, speed, and “above the radar” capability to be a “player” in a competitive market.  Also, because the SR-71 Blackbird is simply awesome.

Helicopter
     Closing the distance further, the focus becomes internal to the organization.  Portfolio management involves ensuring that all projects, product lines, and operations are sufficiently resourced to be successful.  It may be necessary to change course or reprioritize quickly as circumstances change in various segments of the organization.
     This icon was chosen to represent the maneuverability needed to support the organization at this level.  Although a 10,000 ft altitude is cited (maximum hover altitude), greater altitude can be reached for rapid travel (e.g. change of priority).  Much lower altitude can also be maintained to collect information or make observations.
            A combat helicopter, specifically, was chosen as the visual representation because the presence and movements of this level of management can be intimidating.  This is particularly true when observed hovering at low altitude near a specific project, department, etc.  It may lead one to wonder if missile fire (i.e. funding cuts or removal from the portfolio) is imminent.

Hot Air Balloon
     The low-altitude flight of a hot air balloon is much less menacing than that of a combat helicopter.  At the program management level, reallocations typically occur slowly and predictably as projects are completed or phased out.  Hot air balloons are propelled by the prevailing winds (i.e. directives from higher-level management).  As such, maneuverability is limited, but a skilled aeronaut can navigate these winds to bring optimal performance to the organization.

Drone
     A drone is extremely maneuverable within its operating range; it can change course and/or altitude rapidly, much like a helicopter.  It is much less menacing, however, because it is unlikely to carry anything ballistic.  To the contrary, the drone’s role includes observing, guiding, and, when necessary, prompting a team to ensure performance to expectations.  This is the level of project management, where the project manager may need to support several stakeholders in rapid succession and with varying levels of intervention.  We are not yet at the level of widget processing, but this is the highest level of Operations Management that is likely to be familiar with it.

Water Bomber
     Unlike the neighbor kid that terrorizes the sidewalk from the second-story window, a water bomber, a.k.a. airtanker, is an extremely important resource when things go wrong.  A water bomber is an airplane that drops large volumes of water or fire suppressant on a wildfire to extinguish the flames and quell its advancement.
The water bomber is a crucial component of the effort to minimize damage and prevent total, irrecoverable disaster.  Maintenance technicians, engineers, planners, and others are often engaged in metaphorical firefighting, resolving equipment and material issues, among others, as fast as humanly possible.  The water bomber icon is an excellent representation of the nature of these individuals’ activities in times of crisis.

Crop Duster
     Though it is highly unlikely that a water bomber would be converted to a crop duster in the aircraft context, it occurs regularly in the Operations Management context.  When no fire is blazing, technicians, engineers, and others develop and implement preventive measures to ensure operational performance.  Equipment reliability and quality assurance are key areas of interest. Individuals at this level are not responsible for processing widgets, but they are responsible for maintaining widget processing capabilities.
     Much of the continuous improvement effort also takes place at this level.  Analyzing performance, developing improvements, and testing require this hands-on level of Operations Management. A crop duster applies pesticides and fertilizer to ensure a healthy crop at harvest time (preventive measures).  The crop duster also tests different chemicals and application techniques to achieve the best result (continuous improvement).  Processing widgets requires the same types of effort as growing crops.

Ground
     At an altitude of 0, there is no flight.  Individuals at this level are the “front-line” employees, the widget processors; they are performing what the other levels are observing and supporting.  They are responsible for a well-defined set of tasks, often repetitive.  As such, they are most familiar with widget processing and invaluable sources of information.
     The ground level relies on the crop dusters and water bombers to ensure that they have the capability to perform at their best.  This dependency is symbolized by the choice of icons for this level – trees that may, at some time, require water bombing to save them.  This also represents the difficulty often experienced at this level to “see the forest for the trees.”  Many times, the forest is not presented to them; it may even be purposefully hidden from them, but that digression is a topic for another time.
 
     From this discussion, it should be clear that there is nothing magical about the number eight; more or fewer levels could be defined.  However, an eight-level framework is convenient for the presentation of the range of responsibilities that exist under the Operations Management “umbrella.”  Within each level, the example responsibilities and participants are sufficiently broad to capture the nature of typical roles, while also avoiding excessive overlap in levels that could be confusing.  In practice, there may be significant overlap, but in presentation, it is counterproductive.
The framework presented is also well-suited to the aircraft theme.  These icons are easily recognized and differentiated, facilitating comprehension of the framework while presenting opportunities for entertaining comparisons and extended analogies.
When Mars has been colonized, the framework may require expansion to include an intergalactic level of Operations Management.  Some icon candidates are shown in Exhibit 2.
     For additional guidance or assistance with terrestrial Operations challenges, feel free to leave a comment, contact JayWink Solutions, or schedule an appointment.
 
Jody W. Phelps, MSc, PMP®, MBA
Principal Consultant
JayWink Solutions, LLC
jody@jaywink.com
]]>
<![CDATA[FMEA – Vol. XI:  Commentary, Critique, and Caveats]]>Wed, 10 Aug 2022 14:30:00 GMThttp://jaywinksolutions.com/thethirddegree/fmea-vol-xi-commentary-critique-and-caveats     Standards and guidelines published by industry groups or standards organizations typically undergo an extensive review process prior to acceptance.  A number of drafts may be required to refine the content and format into a structure approved by a committee of decision-makers.
     As one might expect, the draft review and approval process is not consistent for every publication.  The number of drafts, time to review, and types of changes requested will vary.  Though each review is intended to be rigorous, errors often remain in the approved publication.  The content may also require interpretation to employ effectively.
     This is certainly true of the aligned AIAG/VDA FMEA Handbook.  In this installment of the “FMEA” series, the Handbook’s errors and omissions, opacities and ambiguities will be discussed.  Where possible, mistakes will be corrected, blanks filled in, and clarity provided in pursuit of greater utility of the Handbook for all FMEA practitioners.
     AIAG compiles errata documents to inform users of known errors within its publications.  The most recent version of the AIAG & VDA FMEA Handbook 1 Errata Sheet (English – June 2020) is 10 pages long.  It contains many corrections of terminology and formatting, many of which are simply “copy/paste” or “find/replace” errors.
     With an ostensibly rigorous review process in place, why did so many simple, blatant errors go unnoticed until after publication?  Many readers will not have the Errata Sheet at hand when learning the aligned method; some may not even be aware of its existence.  This puts those readers at risk of confusion, misapplication, and ineffective analysis, negating the expected benefits of the aligned approach.  It is disappointing that a highly-anticipated release of an update of this magnitude be so seemingly carelessly done.
     This discussion is not intended to be a line-by-line analysis of the Handbook; that would be redundant and futile.  Rather, it aims to highlight areas where clarification is needed or other improvement opportunities exist.
 
     The installments of the “FMEA” series that discuss the AIAG FMEA Handbook are written to parallel the presentation of information in the Handbook (mostly – more on that later).  This is done to simplify referencing both the original (the Handbook) and the companion resource (“The Third Degree”) concurrently, whether to learn the aligned method or to aid subsequent FMEA activities.
     The “FMEA” series also uses the copy/paste shortcut to maintain consistency, for example, between DFMEA and PFMEA discussions.  Therefore, it exhibits the same potential for error as the Handbook.  However, “The Third Degree” has no committee of reviewers whose responsibility it is to ensure the accuracy of the initial publication; it relies on the author’s proofreading and editing.  Reader feedback is appreciated to facilitate further correction and improvement.
     In addition to offering recommendations to improve the efficiency of analysis, the installments of this series that present visual AP tables (Vol. IX) and recommended formats (Vol. X) also contain some critique and caveats that would be appropriate to include here.  Readers are encouraged to review those remarks in the context of the full discussion rather than reiterating them here.  Critical remarks are intentionally limited in other installments of the series to maintain focus on the 7-Step Approach as presented in the Handbook.
     The Handbook discusses the use of a “foundation” or “baseline” FMEA as a starting point for analysis of a new product or process. This practice is similar to that described in “P is for Process,” where existing product and process information is stored for use across many programs.  The key difference is that the Handbook explicitly states that the intention is to modify the foundation FMEA to suit the new product or process, whereas “P is for Process” focuses on direct reuse.  Stated another way, the Handbook prescribes using the foundation FMEA to look for differences, while “P is for Process” looks for similarities between the new product or process and the existing.
     In this series, “FMEA” is expanded to Failure Modes [plural] and Effects Analysis.  Many sources, including the current and previous AIAG and VDA Handbooks, use the singular form of mode.  It may seem trivial, but it is an important reminder that a Focus Element may have multiple Failure Modes and that thorough, effective analysis requires remaining cognizant of this simple fact.
     Other terminology used in the series also may differ slightly from other sources.  Examples of interchangeable terms include:
  • Failure Mode, FM, failure mechanism.
  • Cause of Failure, FC, failure cause.
  • Effect of Failure, FE, failure effect.
  • Structure tree, function tree (structure tree with function information), failure tree (structure tree with failure information; not used in this series to avoid confusion with “fault tree” or FTA).
 
     A series of comments, points of concern, and suggestions are presented below.  Though there is some overlap, or applicability to more than one FMEA type, the remarks, again, approximately parallel the Handbook’s presentation order (i.e. Design FMEA, Process FMEA, FMEA-MSR).  Given that, the remarks are still somewhat disjointed, precluding a coherent, flowing discussion; therefore, a bullet-point format is used to reflect this.
  • Document all FE, FM, and FC before evaluating Severity (S); document all Controls before evaluating Occurrence (O) and Detection (D); document all Monitoring Controls and System Responses before evaluating Frequency (F), Monitoring (M), and S after MSR.  This prevents distraction by preoccupation with high ratings that could lead analysts to move to next steps before a thorough analysis is complete.
  • The logic behind the priority assigned to any combination of S, O, and D ratings in the AP Table is not provided in the Handbook.  This gives the appearance of being somewhat arbitrary and an attempt to remove judgment from FMEA.  This is unwise, as it is judgment that ensures the highest priority actions are taken first!
  • To prioritize actions with equal Action Priority (AP), return to the method used in classical FMEA, where S, O, D, and RPN (Risk Priority Number) are used to evaluate the relative urgency of actions to be taken.  See “Vol. III:  ‘Classical’ DFMEA” or “Vol. IV:  ‘Classical’ PFMEA” to review this process.
  • Columns are combined on the aligned form that were recommended to be split on the classical FMEA form (e.g. Functions and Requirements, Step 3, DFMEA).  This is driven, simply, by the space available.  The additional column entries of an aligned FMEA require such tradeoffs in formatting.
  • There are diagrams presented in the Handbook without reference or explanation in the text.  The value of these range from near zero to highly negative, as more complex diagrams may be subject to misinterpretation or cause confusion for the reader.  Either would result in a negative impact to learning and/or analysis.  For an example, see “Figure 3.1-1:  Demonstration of the process for narrowing Preparation.”  Preparation can be narrowed??  That sounds very helpful.  Tell me more!  Please?
  • Evaluation of the Severity of Effects of Failure is discussed in Step 5 in the Handbook, though the S column is in the Step 4 section of the FMEA form.  “The Third Degree” discusses Severity evaluation in Step 4 to maintain the flow of analysis and to discuss the topic in context.
  • Learning the aligned FMEA method and form is iterative – completion of later steps may be needed to fully understand earlier steps of this approach.  Like most endeavors, repetition is required to develop competency.  However, the “double-back” required by postponing Severity evaluations until Step 5 is a different thing altogether.  A new user could easily be distracted by the incomplete step, fruitlessly searching for information to complete Step 4 before continuing to Step 5.  Successful students often display this type of diligence; simply mentioning that it will be addressed in the next step would be sufficient to avoid derailing these readers.
  • Recommended changes to action status choices include: 
     1) Leave space blank instead of using “Open” status; it has no meaning.  How can the status of an action be “no action defined?”  A blank space conveys that it has not been defined; if it had been defined, it would be recorded here! 
     2) When status is “Not Implemented,” remove the action information from the form, place a “*” in the Remarks column, and provide justification for rejecting the recommended action in the FMEA Report
     3) Use “On Hold” status to identify actions that have been approved, but are not in progress.  Provide the justification for postponing the action in the Remarks (FMEA Report).  Possible reasons for placing an action “On Hold” include resource limitations, pending regulatory update, pending design change, etc.
  • The Handbook advocates the use of “4M” work elements – Machine, Man, Material, and EnvironMent.  The other two Ms typically found on an Ishikawa (Fishbone) diagramMethod and Measurement – are also mentioned as optional categories to consider, but “4M Type” remains on the forms.  All potential sources of failure should be considered, whether or not they fit the “M” pattern!
  • If a performance requirement is adopted from an external specification or standard, and is required to follow any changes in it, do not include the quantitative value on the FMEA form.  Doing so would require the FMEA to be revised each time the value is changed in the standard; simply referencing the “latest revision” would prevent this while maintaining compliance.  If the requirement will not automatically follow any specification updates, reference the applicable revision and/or quantitative value on the FMEA form.  Periodic reviews of the FMEA should be conducted irrespective of this recommendation.
  • The Handbook lists “unnecessary activity” as an example of process failure.  A waste, perhaps, but a failure?  If a machine operator whistles while s/he works, it is not a process failure; it is not even a waste!  The positive vibe may even improve performance and well-being among the whistler’s coworkers!  There is also mention of Prevention Controls being used to prevent “possible layout deficiencies of the production facility.”  These attempts to inject lean concepts are clumsy and inappropriate.  Beware the inclination to expand “analysis” and intervention into areas where it has no relevance or authority.
  • Product Special Characteristics are not recorded on the aligned DFMEA form and only during Optimization (Step 6) of the PFMEA.  Waiting until the final stage of analysis to bring attention to critical aspects of the product’s design seems terribly misguided and counterproductive.
  • Example form segments are shown throughout the Handbook; Appendix B is dedicated to “Step by Step Hints” using these form segments.  Whenever consulting these, also review the corresponding Standard Form and Errata Sheet to ensure use of the most current information.
  • Design and Process FMEAs are developed with an assumption that “all inputs are good;” that is, analysis focuses on that which is within control of the design or process team.  The Handbook states that FMEA-MSR, in contrast, must account for erroneous signals from external components.  The design team can define supplier performance requirements (e.g. 99.5% signal accuracy), but it cannot directly take action to improve the performance of supplied components.  The design team could define a mitigation strategy for the effects of erroneous signals, but this would be a potentially boundless expansion of scope!  Therefore, practitioners must exercise extreme caution when analysis enters this realm.  (See note above on expanding analysis beyond its relevance or authority.)
  • Some terminology used to describe FMEA-MSR failure scenarios could be confusing for readers; terms are not consistent with previous usage.  In Section 4.4, on Failure Analysis, timeline diagrams depict the occurrence of a “Fault,” followed by the occurrence of a “Failure Effect,” then a “hazardous event” that may or may not occur.  Throughout the series, only “failures” have been referenced; the use of “fault” is new.  Other terms may be clearer if aligned with usage in “Safety First!  Or is It?” where the Handbook’s “hazardous event” would be labeled an incident.  In the time between the occurrence of the Effect of Failure and the incident, a “hazardous condition,” or simply a hazard, exists.  This term better reflects the risk inherent in the situation and the potential for mitigation.  Also, it seems incongruous to refer to an incident as subsequent to the occurrence of an Effect of Failure.  The incident, or “hazardous event,” is likely to be the most salient Effect of Failure conceivable!
  • The Handbook’s presentation of AP for FMEA-MSR (Section 4.5.8) refers readers to previous chapters for discussion of “reducing risk first by S, then F, then, M.”  However, F (Frequency) and M (Monitoring) are not used in the previous chapters and no attempt is made to correlate them to O and D, respectively.  The subsequent descriptions of High, Medium, and Low priorities make no mention of lowering S, though it should always be considered.  MSR even offers two opportunities to lower S – one for the unmitigated Effect of Failure and one for S after MSR.  The latter is even neglected in the Optimization step of FMEA-MSR, though it directly impacts customer experience!
  • The Handbook allows DFMEA and PFMEA revision histories to serve as the historical records of product and process development, but requires the S, F, and M ratings of FMEA-MSR to remain unchanged after actions have been taken to reduce them.  This is inconsistent with established practice and could result in an illegible FMEA form as multiple rounds of development are executed with no maintenance of the Optimization section.  Inconsistent, unnecessary, and ill-advised.
 
     The preceding collection of remarks is certainly not comprehensive.  Errors that have not been identified in the Errata Sheet and other confusing or misleading elements that have not been discussed here remain in the Handbook.  These, and other deficiencies, must be navigated at least until the next edition is published.  The more cynical among us might suggest that AIAG’s catalog of onsite and virtual training sessions limits the organization’s incentive to ever produce a clear and coherent document.  But only the cynical.  Experienced practitioners will interpret, adapt, and share their FMEA wisdom as they have done for decades.
     Outside the automotive industry, there is little reason to adopt the new FMEA methodology.  Many of the supporting tools, such as structure trees, can be applied to classical FMEA, if desired.  The investment required to transition to the new approach may be unwarranted, unless the products of interest contain diagnostic systems that would benefit from the additional scrutiny of FMEA-MSR.  An online search yields presentations by both advocates and opponents of the aligned approach.  Readers are encouraged to consider both positions, whether to inform a decision on transition or to best utilize the new method when required.
 
     For additional guidance or assistance with Operations challenges, feel free to leave a comment, contact JayWink Solutions, or schedule an appointment.
 
     For a directory of “FMEA” volumes on “The Third Degree,” see Vol. I:  Introduction to Failure Modes and Effects Analysis.
 
References
[Link] “FMEA Handbook.”  Automotive Industry Action Group and VDA QMC, 2019.


Jody W. Phelps, MSc, PMP®, MBA
Principal Consultant
JayWink Solutions, LLC
jody@jaywink.com
]]>
<![CDATA[FMEA – Vol. X:  Suggested Formats for “Aligned” FMEA Forms]]>Wed, 27 Jul 2022 14:30:00 GMThttp://jaywinksolutions.com/thethirddegree/fmea-vol-x-suggested-formats-for-aligned-fmea-forms     The AIAG/VDA FMEA Handbook presents standard and alternate form sheets for Design, Process, and Supplemental FMEA.  The formats presented do not preclude further customization, however.  In this installment of the “FMEA” series, suggested modifications to the standard-format form sheets are presented.  The rationale for changes is also provided to facilitate practitioners’ development of the most effective documentation for use in their organizations and by their customers.
     In Exhibit 1, the Standard Form Sheet for each of the aligned FMEA types discussed in this series – Design (Vol. VI), Process (Vol. VII), and Supplemental for Monitoring and System Response (Vol. VIII) – is reproduced for reference and comparison.  Only the standard formats were discussed in the initial presentation of each aligned FMEA type to simplify the introduction of the method to new users.  Doing so also allows each installment to “parallel” the presentation in the Handbook, resulting in a more user-friendly supplement.
Design FMEA Form Sheet
            Modifications to the standard format are minor, intended to improve legibility of the form sheet without significantly changing the workflow of the analysis.  The modified DFMEA Form Sheet is shown in Exhibit 2.
     In similar fashion to previous installments, number “bubbles” have been placed on the form to highlight areas that have been modified.  The changes are described below:
(1)       The amount of information recorded in the header of the form, for Planning & Preparation (Step 1), is significantly reduced.  The displaced information should be contained in the FMEA Report, improving legibility of the information most relevant while conducting and reviewing the analysis.  Reducing the size of the header provides more space for analysis on each page of the form; this is more advantageous as product complexity increases.
     Highlighting of information fields is limited to the FMEA ID so a form can be quickly identified (e.g. filing and retrieval).  “FMEA ID” is a generic term used for all types of analysis; creating an “intelligent” identification scheme, as discussed in previous installments, allows correlation of analyses related by product family, manufacturing location, etc.  Dates are also labelled generically; the analysis to which it refers is made obvious by its inclusion on the form.
     To maintain an orderly arrangement of information contained in an extensive analysis, page number tracking has been added to the header.  Without this, a reviewer could be unwittingly missing a significant portion of the analysis, concluding that there is much less risk than actually exists in the product design.  Allocation of development resources needed to ensure a successful product could be delayed or omitted from project plans.
(2)       The “History/Change Authorization” column has been removed for the following reasons:
  • The Handbook identifies it as an optional entry, neglecting to elucidate its envisioned use.
  • Analysis history is best recorded in the FMEA Report; several iterations of development and analysis could easily overwhelm the form, obscuring information that remains relevant.  Change authorization is usually defined by standard practice (i.e. Design Responsible authorizes changes via Engineering Change process); including it here would be redundant.  More relevant to the analysis would be approvals of current designs when the analysis suggests action should be taken.  These, too, would be redundant, however, as this information is recorded in the Optimization (Step 6) section of the form.
  • Elimination of this column provides additional space for analysis, improving legibility of the form sheet.

     The first column label, “Issue #,” has been retained, but “Failure Chain #” or similar label could be substituted to maintain consistency of terminology.  Minor adjustments have been made to other column labels to improve legibility and maintain uniformity.
(3)       An additional “Issue #” column has been added to facilitate tracking a failure chain’s details through all six steps.  This is particularly helpful when the form is presented in stacked format.  Minor adjustments have been made to other column labels to improve legibility and maintain uniformity.
(4)       The “Remarks” column has been narrowed to discourage the inclusion of lengthy notes on the form sheet, saving space for analysis.  Its recommended use, instead, is to simply place a “flag,” such as an asterisk (*), in this column to signal the reader that the FMEA Report contains additional information.  Form sheet entries should be succinct, serving as a summary of analysis; detailed discussions should be contained in separate reports or addenda and included in the final FMEA package.
 
     The Alternate DFMEA Form Sheet, shown in Exhibit 3, moves the “Next Higher Level” Structure and Function Analysis to a separate row.  This format allows wider columns and, thus, improved legibility of other information.  However, the “waterfall” display of information is lost.  A waterfall display results when a single entry (row) in an early column is referenced by multiple rows in subsequent columns (e.g. a focus element has multiple failure modes, a failure mode has multiple causes, etc.).  A waterfall display adds information to the analysis, almost subconsciously (e.g. the complexity or potential weakness of a focus element), at a glance.  The loss of this additional information and the reduced area for analysis (failure chains) render this alternate format less desirable.
Process FMEA Form Sheet
     Modifications to the standard format are minor, intended to improve legibility of the form sheet without significantly changing the workflow of the analysis.  The modified PFMEA Form Sheet is shown in Exhibit 4.
     PFMEA Form Sheet modifications are described below:
(1)       The amount of information recorded in the header of the form, for Planning & Preparation (Step 1), is significantly reduced.  The displaced information should be contained in the FMEA Report, improving legibility of the information most relevant while conducting and reviewing the analysis.  Reducing the size of the header provides more space for analysis on each page of the form; this is more advantageous as process complexity increases.
     Highlighting of information fields is limited to the FMEA ID so a form can be quickly identified (e.g. filing and retrieval).  “FMEA ID” is a generic term used for all types of analysis; creating an “intelligent” identification scheme, as discussed in previous installments, allows correlation of analyses related by product family, manufacturing location, etc.  Dates are also labelled generically; the analysis to which it refers is made obvious by its inclusion on the form.
     To maintain an orderly arrangement of information contained in an extensive analysis, page number tracking has been added to the header.  Without this, a reviewer could be unwittingly missing a significant portion of the analysis, concluding that there is much less risk than actually exists in the process.  Allocation of development resources needed to ensure an efficient manufacturing process could be delayed or omitted from project plans.
(2)       The “History/Change Authorization” column has been removed for the following reasons:
  • The Handbook identifies it as an optional entry, neglecting to elucidate its envisioned use.
  • Analysis history is best recorded in the FMEA Report; several iterations of development and analysis could easily overwhelm the form, obscuring information that remains relevant.  Change authorization is usually defined by standard practice (i.e. Process Responsible authorizes changes via Engineering Change process); including it here would be redundant.  More relevant to the analysis would be approvals of current processes when the analysis suggests action should be taken.  These, too, would be redundant, however, as this information is recorded in the Optimization (Step 6) section of the form.
  • Elimination of this column provides additional space for analysis, improving legibility of the form sheet.

     The first column label, “Issue #,” has been retained, but “Failure Chain #” or similar label could be substituted to maintain consistency of terminology.  Minor adjustments have been made to other column labels to improve legibility and maintain uniformity.
(3)       An additional “Issue #” column has been added to facilitate tracking a failure chain’s details through all six steps.  This is particularly helpful when the form is presented in stacked format.  Minor adjustments have been made to other column labels to improve legibility and maintain uniformity.
(4)       The “Remarks” column has been narrowed to discourage the inclusion of lengthy notes on the form sheet, saving space for analysis.  Its recommended use, instead, is to simply place a “flag,” such as an asterisk (*), in this column to signal the reader that the FMEA Report contains additional information.  Form sheet entries should be succinct, serving as a summary of analysis; detailed discussions should be contained in separate reports or addenda and included in the final FMEA package.
 
     The AIAG/VDA Handbook offers four alternate PFMEA Form Sheet formats, shown in Exhibit 5.
     The disadvantages of each of the four alternate formats shown in Exhibit 5 outweigh the advantages they may offer.  In Form D (Exhibit 5A), the additional row used for Process Item definition reduces analysis space and sacrifices the “waterfall” presentation of information (see discussion in “Design FMEA Form Sheet” section, above).
     The division of functions and requirements into separate columns, as shown in Form E (Exhibit 5B), is recommended in classical FMEA (Vol. III, Vol. IV).  Doing the same in the aligned analysis is less advantageous, however.  Legibility suffers when adding columns to an already-crowded form.  A key advantage to the aligned format is the correlation of information between steps by numbering and color-coding; the additional columns makes this progression less intuitive.  Form F (Exhibit 5C) combines the methods used in Forms D and E, combining the disadvantages of both in a single form.
     Form G (Exhibit 5D) is the most drastic departure from the standard format.  “Next Higher Level” information is omitted from Structure and Function Analysis; the remaining columns in Steps 2, 3, and 4 have been reordered.  Thus, the advantage of the new method’s information flow and correlation between steps is lost.  An addition to the Risk Analysis (Step 5) section of the form is interesting, however.  The Handbook explicitly eschews the RPN calculations that classical FMEA relies on, but Form G includes three “reference” columns for similar calculations.  Perhaps the development team recognized that RPN and similar calculations can be useful after all.
     Reintroducing RPN to the recommended format was considered, but rejected for fear that transitioning practitioners would rely too heavily on it.  It could be a useful piece of additional information to be considered after the Action Priority has been determined.  Unfortunately, it could also be a crutch for those resistant to change or unwilling to evaluate the new method fairly.  The risk of misuse prompted its exclusion; perhaps it will be reintroduced after use of Action Priority tables no longer feels “new” or “unnatural.”
 
FMEA-MSR Form Sheet
     Modifications to the standard format are minor, intended to improve legibility of the form sheet without significantly changing the workflow of the analysis.  The modified FMEA-MSR Form Sheet is shown in Exhibit 6.
     Modifications to the Standard FMEA-MSR Form Sheet to derive the “Recommended Full FMEA-MSR Form Sheet” are described below:
(1)       The amount of information recorded in the header of the form, for Planning & Preparation (Step 1), is significantly reduced.  The displaced information should be contained in the FMEA Report, improving legibility of the information most relevant while conducting and reviewing the analysis.  Reducing the size of the header provides more space for analysis on each page of the form; this is more advantageous as the complexity of the diagnostic system increases.
     Highlighting of information fields is limited to the FMEA ID so a form can be quickly identified (e.g. filing and retrieval).  “FMEA ID” is a generic term used for all types of analysis; creating an “intelligent” identification scheme, as discussed in previous installments, allows correlation of analyses related by product family, manufacturing location, etc.  Dates are also labelled generically; the analysis to which it refers is made obvious by its inclusion on the form.
     To maintain an orderly arrangement of information contained in an extensive analysis, page number tracking has been added to the header.  Without this, a reviewer could be unwittingly missing a significant portion of the analysis, concluding that there is much less capability or risk than actually exists in the diagnostic system.  Allocation of development resources needed to ensure a successful product could be delayed or omitted from project plans.
(2)       The “History/Change Authorization” column has been removed for the following reasons:
  • The Handbook identifies it as an optional entry, neglecting to elucidate its envisioned use.
  • Analysis history is best recorded in the FMEA Report; several iterations of development and analysis could easily overwhelm the form, obscuring information that remains relevant.  Change authorization is usually defined by standard practice (i.e. Design Responsible authorizes changes via Engineering Change process); including it here would be redundant.  More relevant to the analysis would be approvals of current designs when the analysis suggests action should be taken.  These, too, would be redundant, however, as this information is recorded in the Optimization (Step 6) section of the form.
  • Elimination of this column provides additional space for analysis, improving legibility of the form sheet.

     The first column label, “Issue #,” has been retained, but “Failure Chain #” or similar label could be substituted to maintain consistency of terminology.  Minor adjustments have been made to other column labels to improve legibility and maintain uniformity.
(3)       An additional “Issue #” column has been added to facilitate tracking a failure chain’s details through all six steps.  This is particularly helpful when the form is presented in stacked format.  Minor adjustments have been made to other column labels to improve legibility and maintain uniformity.
(4)       The “Remarks” column has been narrowed to discourage the inclusion of lengthy notes on the form sheet, saving space for analysis.  Its recommended use, instead, is to simply place a “flag,” such as an asterisk (*), in this column to signal the reader that the FMEA Report contains additional information.  Form sheet entries should be succinct, serving as a summary of analysis; detailed discussions should be contained in separate reports or addenda and included in the final FMEA package.
 
     The Handbook offers no alternate format for the FMEA-MSR Form Sheet; however, one has been derived for inclusion here.  The “Full” form of Exhibit 6 includes an entire DFMEA, spanning three pages in its linear format; such a large document can be unwieldy.  In stacked format, it may be difficult to include all information for a single failure chain on one page.  The “Recommended Alternate FMEA-MSR Form Sheet,” shown in Exhibit 7, alleviates these concerns.
     To reduce the size of the FMEA-MSR form to two pages, consistent with Design and Process FMEA Form Sheets, the Optimization (Step 6) section of the DFMEA portion of the form has been removed.  This allows the DFMEA portion to occupy the first page, while the second page contains the Supplemental analysis.
     Removing the DFMEA Optimization information does not jeopardize the validity or effectiveness of the analysis because the information is redundant; it is recorded in the original DFMEA.  Also, improvements to the design resulting from DFMEA Optimization require the Supplemental analysis to be reviewed or repeated.  Any change in conditions will, therefore, be identified and evaluated during a subsequent iteration of analysis.  The reduced form is more legible, easier to use, and, therefore, likely to improve the efficacy of analysis.
  
All FMEA Types Form Sheets
     The FMEA Form Sheets presented here have been formatted for printing, when required, on B/A3 paper.  They can be reduced to A/A4 paper for more convenient printing, but legibility will suffer due to the amount of content (i.e number of columns) included in the form.  A JayWink Solutions logo has been added to identify each modified form to prevent accidental use when customized forms have not been approved by a customer.
     Software views are provided in the Handbook, but will not be presented or discussed in detail here, as they are dependent upon the software package chosen.  Software packages will vary in the display format used and output options available.  A common spreadsheet application was used to create the forms presented in this series in order to maintain maximum flexibility.

     For additional guidance or assistance with Operations challenges, feel free to leave a comment, contact JayWink Solutions, or schedule an appointment.
 
     For a directory of “FMEA” volumes on “The Third Degree,” see Vol. I:  Introduction to Failure Modes and Effects Analysis.
 
References
[Link] “FMEA Handbook.”  Automotive Industry Action Group and VDA QMC, 2019.


Jody W. Phelps, MSc, PMP®, MBA
Principal Consultant
JayWink Solutions, LLC
jody@jaywink.com
]]>
<![CDATA[FMEA – Vol. IX:  Visual Action Priority Tables]]>Wed, 13 Jul 2022 14:30:00 GMThttp://jaywinksolutions.com/thethirddegree/fmea-vol-ix-visual-action-priority-tables     No matter how useful or well-written a standard, guideline, or instruction is, there are often shortcuts, or “tricks,” to its efficient utilization.  In the case of the aligned AIAG/VDA Failure Modes and Effects Analysis method, one trick is to use visual Action Priority tables.
     In this installment of the “FMEA” series, use of visual tables to assign an Action Priority (AP) to a failure chain is introduced.  Visual aids are used in many pursuits to provide clarity and increase efficiency.  Visual AP tables are not included in the AIAG/VDA FMEA Handbook, but are derived directly from it to provide these benefits to FMEA practitioners.
     In Design and Process FMEA, Action Priority is comprised of three components:  Severity (S), Occurrence (O), and Detection (D), as described previously in the series (“Vol. VI:  DFMEA” and “Vol. VII:  PFMEA”).  Each Action Priority in DFMEA and PFMEA is assigned according to a single AP table, reproduced in Exhibit 1.
     Severity, Occurrence, and Detection are each scored on a 1 – 10 scale.  Every possible combination of S, O, and D scores can be viewed as a position within a cube of 10 units per side.  The AP table of Exhibit 1 is presented in cube form in Exhibit 2.
     In this form, information contained within the cube is not easily extracted.  An interactive program could be developed to section the cube (or navigate the original AP table) based on S, O, and D inputs.  The effort to do so may be excessive, however.  Alternatively, a set of static section views can be created for quick reference.  One such set is provided in the supplement shown in Exhibit 3.
     The sections of the “AP Cube” presented in Exhibit 3 are taken along the Severity axis, following the logical sequence of FMEA development.  Sections could also be taken along the Occurrence and Detection axes, should these be deemed useful.
     To use the sections in the supplement, simply locate the view corresponding to the failure chain’s Severity score, as indicated at the top of each view.  In that view, locate the column corresponding to the Occurrence score and the row corresponding to the Detection score.  The color of the square at the intersection of these identifies the Action Priority to be assigned to the failure chain.
     For rapid visual capture of the information needed, each Action Priority is identified by a color code, as follows (see Legend in supplement):
  • Green:  Low Priority (L)
  • Yellow:  Medium Priority (M)
  • Red:  High Priority (H)
Sections of the AP Cube are helpful visual aids, but do not preclude understanding the original (text) Action Priority table.
 
     In Supplemental FMEA for Monitoring and System Response (FMEA-MSR, see “Vol. VIII:  Monitoring and System Response”), the three components comprising the Action Priority are Severity (S), Frequency (F), and Monitoring (M).  The AP table for FMEA-MSR is reproduced in Exhibit 4.
     Construction of a cube and subsequent sectioning is done in the same manner as that for Design and Process FMEA.  The resulting visual AP tables are provided in the supplement shown in Exhibit 5.  Use of the supplement and the color code utilized are also consistent with Design and Process FMEA.
     The observant reader will quickly notice something amiss in the visual AP tables for FMEA-MSR – the S = 9 and S = 10 tables appear to be reversed.  The visual representation is consistent with the original (text) AP table (Exhibit 4), however.  This is a logical breakdown in the Supplemental FMEA; practitioners must be aware of it and extremely cautious when using the S = 10 portion of the table or visual aid.  Thorough justifications should be documented for Low and Medium APs assigned that were not available in the S = 9 table.  Use of the S = 9 table is less risky because it sets higher priorities than may otherwise be required.
 

     For additional guidance or assistance with Operations challenges, feel free to leave a comment, contact JayWink Solutions, or schedule an appointment.
 
     For a directory of “FMEA” volumes on “The Third Degree,” see Vol. I:  Introduction to Failure Modes and Effects Analysis.
 
References
[Link] “FMEA Handbook.”  Automotive Industry Action Group and VDA QMC, 2019.


Jody W. Phelps, MSc, PMP®, MBA
Principal Consultant
JayWink Solutions, LLC
jody@jaywink.com
]]>
<![CDATA[FMEA – Vol. VIII:  Monitoring and System Response]]>Wed, 29 Jun 2022 14:30:00 GMThttp://jaywinksolutions.com/thethirddegree/fmea-vol-viii-monitoring-and-system-response     As mentioned in the introduction to the AIAG/VDA aligned standard (“Vol. V:  Alignment”), the new FMEA Handbook, is a significant expansion of its predecessors.  A substantial portion of this expansion is the introduction of a new FMEA type – the Supplemental FMEA for Monitoring and System Response (FMEA-MSR).
     Modern vehicles contain a plethora of onboard diagnostic tools and driver aids.  The FMEA-MSR is conducted to evaluate these tools for their ability to prevent or mitigate Effects of Failure during vehicle operation.
     Discussion of FMEA-MSR is devoid of comparisons to classical FMEA, as it has no correlate in that method.  In this installment of the “FMEA” series, the new analysis will be presented in similar fashion to the previous aligned FMEA types.  Understanding the aligned Design FMEA method is critical to successful implementation of FMEA-MSR; this presentation assumes the reader has attained sufficient competency in DFMEA.  Even so, review of aligned DFMEA (Vol. VI) is highly recommended prior to pursuing FMEA-MSR.
     Following the precedent of DFMEA and PFMEA, the “Standard FMEA-MSR Form Sheet” (“Form H” in Appendix A of the AIAG/VDA FMEA Handbook) is color-coded, but otherwise deviates significantly.  As shown in Exhibit 1, the FMEA-MSR form contains information from DFMEA (Steps 1 – 6, in the upper portion of the stacked format).  The information needed for FMEA-MSR may not be readily available in the original DFMEA, however, as the control systems may have been deemed out of scope.  Therefore, the structure tree and original analysis may need to be expanded prior to initiating the new method of analysis. The lower portion of the stacked format, containing information from the Supplemental FMEA (Steps 5 – 6) represent this new method.  This format reinforces that FMEA-MSR is a supplemental analysis based on DFMEA.
     Like previous installments in this series, portions of the form are shown in close-up, with reference bubbles correlating to discussion in the text.  The goal is to facilitate learning this new analysis method by maintaining the links to the 7-step approach, DFMEA, form sections, and individual columns where information is recorded.
 
Conducting a Supplemental FMEA for Monitoring and System Response
     Supplemental Failure Modes and Effects Analysis for Monitoring and System Response is conducted in the same three stages and seven steps followed to complete a DFMEA or PFMEA.  A pictorial representation of the Seven Step Approach is reproduced in Exhibit 2.  Though there are few hints exclusive to FMEA-MSR, this summary diagram should be referenced as one proceeds through this presentation and while conducting an FMEA-MSR to ensure effective analysis.
System Analysis
1st Step – Planning & Preparation
     Developing a project plan, based on the Five Ts (see Vol. V), is once again recommended to provide structure for the analysis.  The context limitations (new or modified design or application) remain, as do the levels of analysis.  Supplemental analysis differs from DFMEA insomuch that FMEA-MSR is focused on vehicle systems that serve diagnostic, notification, and intervention functions during vehicle operation.  These systems are often referenced in the Risk Analysis (Step 5) section of the DFMEA form.
     To assist in scope definition, the Handbook proffers three basic questions; affirmative responses indicate the subject failure chain is appropriately in the scope of analysis.
1) “After completing a DFMEA on an Electrical/Electronic/Programmable Electronic System, are there effects that may be harmful to persons or involve regulatory noncompliance?”  Such Effects are typically assigned the highest Severity scores (S = 9 – 10).
2) “Did the DFMEA indicate that all of the causes which lead to harm or noncompliance can be detected by direct sensing and/or plausibility algorithms?”  This suggests the ideal Detection score (D = 1).
3) “Did the DFMEA indicate that the intended system response to any and all of the detected causes is to switch to a degraded operational state (including disabling the vehicle), inform the driver and/or write a Diagnostic Trouble Code (DTC) into the control unit for service purposes?”  The answer to this question is typically found in the Current Controls portion of the DFMEA, as mentioned above.
     Deriving the Supplemental analysis from the DFMEA suggests that the core team remains the same; in particular, Design Responsibility should remain with the same individual.  The extended team members require expertise in diagnostic systems and the vehicle functions they are used to monitor.
     Refer to “Vol. VI:  ‘Aligned’ DFMEA” and “Vol. III: ‘Classical’ DFMEA” for more information on the “Planning and Preparation (Step 1)” section of the FMEA-MSR form.
 
Continuous Improvement
     The “History/Change Authorization” column is carried over from DFMEA.  The manner of use in prior analyses should be continued per established organizational norms.
 
2nd Step – Structure Analysis
     In the Structure Analysis step, the system or element named in Step 1 is defined using a structure tree or block diagram.  Only those elements, identified in the DFMEA, with Causes of Failure that show potential for harm to persons or regulatory noncompliance (see question 1, Step 1, above) are in scope.  Analysis can be performed at vehicle level (OEM) or at a subsystem level (supplier).
     System interfaces may also be in scope.  When a control unit is in scope, but a sensor from which it receives a signal, or actuator to which it transmits a signal, is not, the connector that allows the device to communicate with the control unit (i.e. interface) should be included in the analysis.  The signal status (intermittent, lost, incorrect, etc.) can then be considered in analysis of the control unit and the connected device via this interface.  An example of this type of interface analysis is depicted in Exhibit 3, where an input/output connector links two structure trees.
     Contrary to the “assume all inputs are acceptable” philosophy of previous FMEAs, erroneous signals from elements outside the scope of responsibility of the design team should be included in the analysis.  The objective is to ensure safety and compliance, even when system inputs are corrupted.
     The Structure Analysis (Step 2) section of the FMEA-MSR form is a subset of the completed DFMEA.  Information can be transferred from the DFMEA and structure tree previously created or expanded to include diagnostic elements.
 
3rd Step – Function Analysis
     Once again drawing from the structure tree and DFMEA, the Function Analysis (Step 3) section of the FMEA-MSR form can be completed from these sources, as shown in Exhibit 4.  A P-Diagram could also be used to organize and visualize the diagnostic system elements.  Functions of interest include those associated with failure detection and response.  Elements may include hardware, software, and communication signals; interface elements may be included when the connected device is out of scope.
Failure Analysis and Risk Mitigation
4th Step – Failure Analysis
     For purposes of analysis, the diagnostic systems are assumed to function as intended.  That is, FMEA-MSR is conducted to evaluate the sufficiency of designed system responses, not the system’s reliability.  Causes of Failures of diagnostic systems (undetected faults, false alarms, etc.) can be included in the DFMEA, however.
     Supplemental FMEA-MSR requires consideration of three failure scenarios that differ in the occurrence of a hazardous event, detection of a failure, and effectiveness of failure response.  The first scenario involves a fault condition that causes malfunctioning behavior (Failure Mode) resulting in Effects of Failure, but no hazardous event.  No detection or response occurs, though a noncompliant condition may be created.  This scenario is depicted in Exhibit 5.
     The second scenario differs from the first in that a hazardous event occurs, as depicted in Exhibit 6.  Again, no detection or response occurs; this may be due to a slow or inoperable monitoring or intervention mechanism.  The time available for a system response to prevent a hazardous event is the Fault Handling Time Interval – the time elapsed between occurrence of a fault and a resultant hazardous event.  The minimum Fault Handling Time Interval is the Fault Tolerant Time Interval – the time elapsed between occurrence of a fault and Effect of Failure.  In Scenario 2, the monitoring system in place, if any, is insufficient to prevent the hazardous event.
     The third scenario incorporates fault detection and system response to mitigate Effects of Failure and prevent occurrence of a hazardous event.  In this scenario, depicted in Exhibit 7, the user experiences a partial loss or degradation of function (mitigated Effect of Failure) in lieu of the hazardous event.  System response time must be less than the Fault Tolerant Time Interval to ensure consistent performance in service.
     The final failure scenario can also be represented by the “hybrid failure chain” depicted in Exhibit 8.  The hybrid failure chain adds a chain parallel to the DFMEA failure chain; it includes monitoring for Causes of Failure, the response activated when a fault is detected, and the substitute, or mitigated, Effects of Failure.  In the Monitoring and System Response (MSR) scenario, analysis is shifted from the DFMEA failure chain to the MSR failure chain (lower portion of the stacked-format FMEA-MSR form).  An example hybrid failure chain, in structure tree format, is shown in Exhibit 9.  This includes a description of intended behavior (Failure Mode) and mitigated Effects.
     An Effect of Failure in FMEA-MSR can be a malfunctioning behavior, as in DFMEA; it can also be an intended behavior or designed response to detection of a Cause of Failure.  If the designed response (“intervention”) is effective, a “safe state” will result, albeit with some loss of function.  In Scenarios 1 and 2, the Failure Mode remains that in the DFMEA.  This is the failure chain recorded in the Failure Analysis (Step 4) section of the FMEA-MSR form, as shown in Exhibit 10.
     To complete the Failure Analysis section, assign a Severity (S) score to the Effect of Failure.  The same Severity criteria table is used in DFMEA and FMEA-MSR; it is reproduced in Exhibit 11.  Refer to Vol. VI for further guidance on assigning Severity scores and use of the criteria table.
5th Step – Risk Analysis
     Risk Analysis is the first step for which the lower (“new”) section of the FMEA-MSR is used.  Additional information is needed in the DFMEA (upper) section, however, creating two branches of analysis.  In DFMEA, identify the Prevention Controls that allow the diagnostic system to reliably detect Causes of Failure and initiate the system response within the Fault Tolerant Time Interval.  Also, identify the Detection Controls in use to verify the diagnostic system’s effectiveness; simulation and other development tests are often cited.
     Thus far, the discussion has focused on conducting DFMEA in a slightly different way than previously presented.  However, the remainder of the DFMEA section is completed as described in “Vol. VI: ‘Aligned’ DFMEA.”  The remainder of this presentation, therefore, can focus on the unique aspects of the Supplemental FMEA for Monitoring and System Response and completing the FMEA-MSR form.
     The Frequency (F) rating is comparable to the Occurrence score in DFMEA and PFMEA.  It is an estimate of the frequency of occurrence of the Cause of Failure in applicable operating conditions during the vehicle’s planned service life.  In the Frequency criteria table, shown in Exhibit 13, select the statement that best describes the likelihood of the Cause of Failure occurring; record the corresponding F rating in column B of the FMEA-MSR form (Exhibit 12).
     Note that the qualifier emphasized above – in applicable operating conditions – may be invoked to adjust the F rating.  The conditions in which this can be done are outlined in the note below the F table in Exhibit 13.  Stated generally, the Frequency rating may be reduced if the conditions in which the Cause of Failure that leads to the associated Effect of Failure exists only during a small portion of the time the vehicle is in operation.
     If the F rating is reduced for this reason, the relevant operating conditions are recorded in column A of the form – “Rationale for Frequency.”  Also identified here is any other information sources used to support the Frequency rating assigned.  Examples include Design and Process FMEAs (e.g. Prevention Controls), test results, field data, and warranty claims history.
     In column C, “Current Diagnostic Monitoring,” identify all elements of the diagnostic system that contribute to detection of the Cause of Failure, Failure Mode, or Effect of Failure by the system or the operator of the vehicle.  In the “Current System Response” column (D), describe the system’s reaction to a fault condition.  Include any functions that are partially or fully disabled, fault codes recorded, messages to driver, etc.  If no monitoring is in place, describe the hazardous condition created by the failure.
     To assess the effectiveness of the controls in detecting a fault condition and activating an appropriate and sufficient response to prevent a hazardous event, a Monitoring (M) rating is assigned.  To do this, consult the Monitoring criteria table, shown in Exhibit 14.  In each of the “Diagnostic Monitoring” and System Response” columns, select the statement that best describes the system’s performance.  Record the higher of the corresponding M ratings in column E of the FMEA-MSR form (Exhibit 12).
     There are three possible fault-monitoring scenarios, but only one requires significant depth of analysis.  The first scenario is one in which the diagnostic system is ineffective or none has been implemented.  In this case, M = 10 and no adjustment is made.
     The second scenario is the opposite of the first – the diagnostic system is reliable, consistently producing a mitigated Effect while preventing a hazardous event.  In this scenario, M = 1 and no adjustment is made.
     The third fault-monitoring scenario is “in between” or a hybrid of the first two.  This scenario involves a diagnostic system that is partially effective; that is, the fault condition may be, but will not always be detected.  The amount of adjustment in the M rating depends on the proportion of occurrences that the system is expected to detect; “diagnostic coverage” estimates are given in the “Diagnostic Monitoring” criteria descriptions (see Exhibit 14).  A demonstration of this scenario’s rating method is shown pictorially in Exhibit 15.
     In column F, describe the mitigated Effect of Failure resulting from the diagnostic system’s intervention after detecting the fault condition.  Record the Severity score associated with the mitigated Effect in column G.  In column H, copy the Severity score, from Step 4, corresponding to the original, unmitigated Effect of Failure.
 
     Using the lookup table in Exhibit 16, assign an Action Priority (AP) to the failure chain.  The three possible priorities have similar definitions to those in previously presented FMEAs:
  • H:  High Priority – actions must be taken to lower frequency and/or improve monitoring or acceptance of current controls justified and approved.
  • M:  Medium Priority – actions should be taken to lower frequency and/or improve monitoring or acceptance of current controls justified and approved.
  • L:  Low Priority – actions could be taken to lower frequency and/or improve monitoring, but justification and approval are not required to accept current controls.
     To determine the correct AP, the following guidelines have been established (see FMEA-MSR AP Table end notes):
  • If M = 1, use S after MSR (column G) to determine AP in FMEA-MSR and DFMEA.
  • If M ≠ 1, use original S (column H) to determine AP in FMEA-MSR and DFMEA.
Record the AP assigned in column J of the FMEA-MSR form (Exhibit 12).  See presentations in Vol. VI (DFMEA) and Vol. VII (PFMEA) for guidance on the use of the “Filter Code” column (K).
 
6th Step – Optimization
     In the Optimization step, improvement activities are identified to reduce risk, assigned to individuals for implementation, monitored for progress, and evaluated for effectiveness.  Relevant information is recorded in the Optimization section of the DFMEA form, shown in Exhibit 17.
     The Handbook asserts that the most effective sequence for Optimization is as follows:
  • Reduce the occurrence of Causes of Failure via design modification [lower Frequency (F), MSR Preventive Action].
  • Improve the ability to detect Causes of Failure or Failure Modes [lower Monitoring (M), Diagnostic Monitoring Action].
     Record the planned Optimization actions in columns L and M, respectively, of the FMEA-MSR form.  In column N, describe the way in which the system will react to a fault condition after the planned action is complete (see column D, Step 5).  In column P, describe the mitigated Effect of Failure produced after the planned action is complete (see column F, Step 5).  Evaluate the Severity of the new mitigated Effect and record its score in column R.
     Column S of the FMEA-MSR form (Exhibit 17) is used to identify the status of each action plan.  The notation used in DFMEA should also be used in FMEA-MSR; see Vol. VI for suggestions.
     The grouped “columns T” are used to record predicted values of F, M, and AP.  The original Severity score (from Step 4) is also repeated here because it may be needed to determine the proper Action Priority to assign.  The AP is determined according to the same guidelines set forth in Risk Analysis (Step 5).
 
Risk Communication
7th Step – Results Documentation
     The recommendations for preparation of an FMEA report following FMEA-MSR are identical to those presented in the discussion of the aligned Design FMEA.  Therefore, the reader is referred to “Vol. VI:  DFMEA” and the AIAG/VDA Handbook for details.
 
 
     A Supplemental FMEA for Monitoring and System Response is a worthwhile extension of the FMEA Handbook and of product development and analysis.  It reflects a recognition of the critical role that driver aids, communication, advanced safety systems, and other current and emerging technologies play in modern vehicle development.  Regulatory compliance, passenger safety, operator convenience, and many other aspects of customer experience rely on these technologies to meet evolving expectations.  Predictable performance and minimized risk are increasingly important as vehicles continue to become more sophisticated and customers more discerning.  As this trend continues, Monitoring and System Response may be the greatest source of competitive advantage currently available to automakers.
 
     For additional guidance or assistance with Operations challenges, feel free to leave a comment, contact JayWink Solutions, or schedule an appointment.
 
     For a directory of “FMEA” volumes on “The Third Degree,” see Vol. I:  Introduction to Failure Modes and Effects Analysis.
 
References
[Link] “FMEA Handbook.”  Automotive Industry Action Group and VDA QMC, 2019.


Jody W. Phelps, MSc, PMP®, MBA
Principal Consultant
JayWink Solutions, LLC
jody@jaywink.com
]]>
<![CDATA[FMEA – Vol. VII:  “Aligned” Process Failure Modes and Effects Analysis]]>Wed, 15 Jun 2022 14:30:00 GMThttp://jaywinksolutions.com/thethirddegree/fmea-vol-vii-aligned-process-failure-modes-and-effects-analysis     To conduct a Process FMEA according to AIAG/VDA alignment, the seven-step approach presented in Vol. VI (Aligned DFMEA) is used.  The seven steps are repeated with a new focus of inquiry.  Like the DFMEA, several system-, subsystem-, and component-level analyses may be required to fully understand a process.
     Paralleling previous entries in the “FMEA” series, this installment presents the 7-step aligned approach applied to process analysis and the “Standard PFMEA Form Sheet.”  Review of classical FMEA and aligned DFMEA is recommended prior to pursuing aligned PFMEA; familiarity with the seven steps, terminology used, and documentation formats will make aligned PFMEA more comprehensible.
     Like the DFMEA form presented in Vol. VI, the “Standard PFMEA Form Sheet” (“Form C” in Appendix A of the AIAG/VDA FMEA Handbook) correlates information between steps using numbered and color-coded column headings.  The aligned PFMEA form is reproduced, in stacked format, in Exhibit 1.  In use, information related to a single issue should be recorded in one row.
     As has been done in previous “FMEA” series entries, portions of the PFMEA form will be shown in close-up, with reference bubbles correlating to discussion in the text.  Not every entry in the form is discussed in detail; some are straightforward and self-explanatory.  Consult previous installments of the “FMEA” series to close any perceived gaps in the information presented.  If further guidance is needed, several options for contacting the author are available at the end of this article.
 
Conducting an ‘Aligned’ Process FMEA
     Failure Modes and Effects Analysis is conducted in three “stages” – System Analysis, Failure Analysis and Risk Mitigation, and Risk Communication.  These three stages are comprised of the seven-step process mentioned previously.  A graphical representation of the relationships between the three stages and seven steps is shown in Exhibit 2.  For each step, brief reminders are provided of key information and activities required or suggested.  Readers should reference this summary diagram as each step is discussed in this presentation and while conducting an FMEA.
System Analysis
1st Step – Planning & Preparation
     Classical FMEA preparations have often been viewed as separate from analysis.  Recognizing the criticality of effective planning and preparation, the aligned process from AIAG and VDA formally incorporate them in the 7-step approach to FMEA.  This is merely a change in “status,” if you will, for preparatory activities.  Thus, the discussion in “Vol. II:  Preparing for Analysis” remains valid, though introduction of the tools is dispersed among steps in the aligned approach.
     Specifically, the realm of appropriate context for FMEA remains the three uses cases defined, briefly described as (1) new design, (2) modified design, or (3) new application.  Within the applicable context, the FMEA team must understand the level of analysis required – system, subsystem, or component.
     The core and extended analysis team members are chosen in the same manner as before.  Defining customers, from subsequent workstations to end user, remains integral to thorough analysis. Doing so in advance facilitates efficient analysis, with all foreseeable Effects of Failure identified.

     To state it generally, the inputs needed to conduct an effective FMEA have not changed.  However, the aligned process provides a framework for organizing the accumulated information into a coherent project plan.  Using the Five Ts structure introduced in “FMEA – Vol. V:  Alignment,” project information is presented in a consistent manner.  The scope of analysis, schedule, team members, documentation requirements, and more are recorded in a standardized format that facilitates FMEA development, reporting, and maintenance.
     Key project information is recorded on the PFMEA form in the header section labeled “Planning & Preparation (Step 1),” shown in Exhibit 3.  The labels are largely self-explanatory, though some minor differences exist between the aligned and classical forms.  One such difference is the definition of the “Subject” (1) of analysis.  On this line, identify the process analyzed by name, location (e.g. line number, department, facility), and any commonly used “nickname” needed to differentiate it from similar processes.
     Key Date on the classical form has been replaced by “Start Date” (2) on the aligned form; both refer to the design freeze date.  “Confidentiality Level” (3) has been added to the form.  Two levels are suggested in the Handbook – “Proprietary” and “Confidential” – but no further guidance on their application is provided.  Discussions should take place within an organization and with customers to ensure mutually agreeable use of these designations and information security.
     Refer to the “FMEA Form Header” section of “Vol. IV:  ‘Classical’ Process Failure Modes and Effects Analysis” for additional guidance on completing this section of the form.
 
Continuous Improvement
     The first two columns in the body of the PFMEA form, shown in Exhibit 4, are not included in the discussion of the seven steps.  The first column, “Issue #” (A), is used to simply number entries for easy reference.  The purpose of column B, “History/Change Authorization,” is left to users’ interpretation.  Presumably, it is used to record management approval of process changes or acceptance of existing risk, though the Handbook offers no guidance on this topic.  In whatever fashion an organization chooses to use this portion of the form, it should be documented in training to ensure consistency and minimize confusion.
2nd Step – Structure Analysis
     Structure Analysis may be less intuitive in a process context than it is for a physical object.  The structure hierarchy, however, remains the same:  Effects of Failure, Failure Modes, Causes of Failure.
     The Structure Analysis section of the PFMEA form is shown in Exhibit 5.  In the first column (C), identify the Process Item, “the highest level of integration within the scope of analysis.”  That is the result achieved by successful completion of all Process Steps, where Effects of Failure would be noticed.  The Process Step (D) is the focus of the analysis – a process operation or workstation where a Failure Mode occurs.
     Each Process Step may be associated with several Process Work Elements (E), where the Causes of Failure lie.  The Handbook highlights four main types (“4M Categories”) of Process Work ElementMachine, Man, Material, and EnvironMent.  Each category is analyzed separately for each Process Step.
     To organize information needed for Structure Analysis, creating a process flow diagram (PFD) is a great place to start (see Commercial Cartography – Vol. II:  Flow Charts).  A single PFD may contain the scope of several PFMEAs, once the complexity of the system and subsystems are considered.  A complete PFD supports rational decisions (see Making Decisions – Vol. I:  Introduction and Terminology) to define a manageable scope for each analysis.
     The flow of information from process flow diagram to structure tree to PFMEA is shown in Exhibit 6.  Enter information in the PFMEA form proceeding across (left to right), then down, to ensure that each Process Work Element has been thoroughly considered before proceeding to the next Process Step.
3rd Step – Function Analysis
     Collection and organization of information to support Function Analysis can be done with a parameter diagram.  As shown in Exhibit 7, the basic layout of the process parameter diagram (P-Diagram) is the same as that of the product P-Diagram, with changes made to accommodate differing informational needs and terminology.
     The Function Analysis section of the PFMEA form, shown in Exhibit 8, is numbered and color-coded to maintain links to information in Step 2.  In the first column (F), describe the function of the Process Item at the level of integration (i.e. system, subsystem) chosen for the analysis.  Multiple functions or multiple customers may be served by a Process Item; be sure that all are included.
     In column G, describe the function of the Process Step and the Product Characteristic it is intended to spawn.  Quantitative specifications can be included, but there is a caveat.  Values defined elsewhere, such as a product drawing or process instruction, are subject to revision in the source document.  This could lead to obsolete information references in the PFMEA or frequent revisions to prevent it.
     Column H contains descriptions of Process Work Element functions and Process Characteristics they are intended to achieve.  These will correspond to the 4M Categories or other types of Process Work Elements defined in Step 2.
     Expanding the structure tree used in Step 2 to include descriptions of the function of each element allows direct transfer of information to the PFMEA form.  This information transfer is demonstrated pictorially in Exhibit 9.
     The Handbook establishes guidelines for function descriptions.  To ensure consistency and clarity, function descriptions should conform to the following:
  • a verb followed by a noun (“Do this to This.”)
  • use the verb’s base form
  • use present tense
  • use positive words
  • answers the question “What does it do?” when working from right to left
  • answers the question “How does it do it?” when working from left to right
     Product Characteristics are the requirements or specifications of the product design.  They may originate in legal or regulatory requirements, customer specifications, industry standards, or corporate governance (internal standards).  Product Characteristics are evaluated after the product is complete.
     Process Characteristics can be evaluated during process execution.  These are the controls that ensure that a process results in the Product Characteristics required.  They may originate from process specifications, work instructions, or other process verification documentation.
     Effort expended in Function Analysis is rewarded in the next step.  Clear, concise function descriptions expedite Failure Analysis by providing phraseology that can be duplicated while maintaining accuracy of meaning.  Diligence in this step results in a more efficient analysis overall.
 
Failure Analysis and Risk Mitigation
4th Step – Failure Analysis
     Failure Analysis is the heart of an FMEA, where a system’s failure network is established.  The links between Failure Modes, Effects of Failure, and Causes of Failure, in multiple levels of analysis, are made clear in this step.
     The definitions of Failure Mode, Effect of Failure, and Cause of Failure, in practice, remain the same as in classical FMEA.  Briefly, these are:
  • Failure Mode:  the manner in which the process could cause a Product Characteristic or intended function to be unrealized, described in technical terms.
  • Effect of Failure:  the result of a failure, as perceived by an internal or external customer.
  • Cause of Failure:  the reason a Failure Mode occurs, described in technical terms.
The relationships between a Failure Mode, its cause, and the resultant effect creates a failure chain, as shown in Exhibit 10.  Each link in the failure chain is the consequence of the existence of the one to its right in the diagram.  That is, the Effect of Failure is the consequence, or result, of the occurrence of the Failure Mode, which is, in turn, the consequence of the existence of the Cause of Failure.
     Each element is not limited to a single failure chain.  The occurrence of a Failure Mode may be perceived in several ways (Effects of Failure) or have multiple potential causes.  A Cause of Failure may also result in several potential Failure Modes, and so on.  Proper identification of each is critical to effective analysis.
     Each failure chain is documented in a single row of the PFMEA form, in the Failure Analysis section, shown in Exhibit 11.  The form maintains the relationships within the failure chain, as shown in Exhibit 10 and discussed above.  It also maintains the numbering and color-coding convention that links Failure Analysis to Function Analysis (Step 3) and Structure Analysis (Step 2).
 
     The Failure Mode is the Focus Element of the process failure chain and, typically, the focus of process owners.  Deviations are discovered in process-monitoring data and inspection reports before customers are effected by errors.  This is, at least, the goal – to contain any failures that could not be prevented.
     Process Failure Modes vary widely, as there are many types of processes that may be the subject of analysis.  Machined components may have a feature of incorrect size or location.  Printed circuit boards could have weak solder joints.  Parts could be assembled in the wrong orientation, or missing altogether.  Threaded fasteners could be tightened to an incorrect torque.  The possibilities are seemingly endless.
     Fortunately, only a finite subset of the endless potential Failure Modes are applicable to a given process.  When one is identified, its opposite, or “mirror-image” condition (e.g. high/low, long/short, etc.), if one exists, should also be considered.  Record all Failure Modes of the Process Step in column L of Exhibit 11.  These are the negatives of the Functions of the Process Step and Process Characteristics defined in Step 3.
     Aspects of a design that increase the frequency or likelihood of process failure should be discussed with the Design Responsible individual.  For example, a part feature may be difficult to machine correctly, or an assembly may lack sufficient access space to install a required component consistently and without damage.  Collaboration between Design and Process Responsible teams, such as Design for Manufacturability and Assembly (DFMA) studies, should take place “early and often” during product development to minimize these issues and their associated cost and delays.
 
     Effects of Failure must be considered for all customers, including:
  • Subsequent internal processes
  • Corporate management
  • OEM and supplier tiers
  • Regulatory agencies
  • End users (i.e. vehicle operators)
  • NGOs (Nongovernmental organizations), such as environmental or other advocacy groups.
An Effect of Failure can be expressed as the negative, or inverse, of the Function of the Process Item defined in Step 3 (i.e. “Doesn’t Do this to This.”), but need not be.  Examples include:
  • unable to assemble [e.g. missing part feature] (subsequent internal process)
  • vehicle will not start [e.g. battery discharged] (end user)
  • uncontrolled emissions [e.g. faulty filter] (regulatory agencies, NGOs)
  • reduced throughput/productivity [e.g. insufficient space to access components] (corporate management)
  • unable to connect [e.g. interface or connector mismatch] (OEM or next-tier supplier]
     If a process failure can create the same Effect as identified in the product’s DFMEA, the same wording and Severity score should be used in the PFMEA.  The diagram in Exhibit 12 demonstrates how this link between a product’s DFMEA and the analysis of a process required to produce it can manifest.
     If downstream impacts are not known, due to strict customer confidentiality, multiple-tier separation from users, or any other reason, Effects of Failure should be defined in terms of what is known.  That is, part drawings, process specifications, or other provided information should be used to describe the Effects.
     Record descriptions of Effects of Failure in column J of the PFMEA form (Exhibit 11).  Include all known Effects, identified by customer (OEM, Tier 1, end user, etc.), not only the “worst.”
 
     Causes of Failure are defined in terms of the 4M Categories or other Process Work Element types first identified in Structure Analysis (Step 2).  Deviations in the physical environment, machine settings, material usage, operator technique, or other aspects of the system could cause a process failure.  Identify those aspects that reside in the subject failure chain in column M (Exhibit 11).
 
     As is the case for the previous two steps, information can be transferred directly from the structure tree, were it created in advance, to the PFMEA form.  The Failure Analysis information transfer is shown in Exhibit 13.
     To complete the Failure Analysis step, each Effect of Failure must be evaluated and assigned a Severity (S) score.  To do this, consult the Severity criteria table, shown in Exhibit 14.  Select the column corresponding to the customer effected, then the criteria in that column that best describes the impact to that customer.  The left-most column in the row containing this description contains the Severity score for the Effect of Failure; enter the number in the PFMEA form (column K of Exhibit 11).
     If there is uncertainty or disagreement about the appropriate Severity score to assign an Effect of Failure (e.g. “Is it a 3 or a 4?”), select the highest score being considered to ensure that the issue receives sufficient attention as development proceeds.  Confidence in evaluations and scores typically increase as a design matures; concordance often follows.
     The last column of the “PFMEA Severity Criteria Table” (Exhibit 14) is left blank in the Handbook because it is not universally applicable.  An organization can record its own examples of Effects of Failure to be used as comparative references when conducting a new FMEA or to aid in training.  Examples cited for various Severity scores can provide great insight into an organization’s understanding, or lack thereof, of its customers’ perspectives.
 
5th Step – Risk Analysis
     The process development team conducts Risk Analysis to identify the controls used to prevent or detect failures, evaluate their effectiveness, and prioritize improvement activities.  Relevant information is recorded in the Risk Analysis section of the PFMEA form, shown in Exhibit 15.
     In column N, record the Process Prevention Controls incorporated to preclude activation of the failure chain.  There are three types of controls that can be implemented (poka yoke, engineering controls, and management controls); these are explained in The War on Error – Vol. II:  Poka Yoke (What Is and Is Not).  Cite only those expected to prevent the specific Cause of Failure with which they are correlated (i.e. in the same row on the form).
     Though not an exhaustive list of Process Prevention Controls, some examples follow:
  • processing guidelines published by an equipment manufacturer, material supplier, industry group, or other recognized authority
  • re-use of previously validated process design or equipment with history of successful service
  • error-proofing (see “The War on Error – Vol. II”)
  • work instructions
  • equipment maintenance
     The effectiveness of Process Prevention Controls is reflected in the Occurrence (O) score.  To assign an Occurrence score to each Cause of Failure, consult the Occurrence criteria table shown in Exhibit 16.  Select the Occurrence score that corresponds to the criteria that best describes the maturity of the prevention controls.  Consider the hierarchy of controls (see “The War on Error – Vol. II”) when assigning Occurrence scores; O = 1 should be reserved for frequently verified poka yoke devices.  Engineering controls typically receive mid-range scores (e.g. O = 2 – 5), while management controls have high scores (e.g. O = 6 – 9).
     If sufficient data is available to make reasonable predictions, a quantitative method of scoring can be used.  For this, substitute one of the alternate PFMEA Occurrence criteria tables, shown in Exhibit 17, for the table of Exhibit 16Occurrence criteria based on production volume are shown in Exhibit 17A, while time-based criteria are shown in Exhibit 17B.  The detailed criteria descriptions are unchanged; the qualitative summary terms (high, low, etc.) are simply replaced by “incidents per 1000” estimates or time intervals.
     If the frequency of occurrence of a Cause of Failure “falls between” two Occurrence scores, or there is disagreement about the correct frequency, select the higher Occurrence score.  Additional consideration in subsequent reviews is likely to resolve the matter more efficiently than extensive debate in early stages.
     The standard and alternate PFMEA Occurrence tables have a column left blank for organization-specific examples to be recorded.  These can be used as comparative references to facilitate future PFMEA development or as training aids.
 
     In column P of the Risk Analysis section of the PFMEA form, identify the Process Detection Controls in place to warn of the existence of the Failure Mode or Cause of Failure before the part reaches a customer.  Examples include visual inspection and end-of-line testing.
     Assess the effectiveness of current detection controls according to the Detection criteria table, shown in Exhibit 18.  Select the Detection (D) score that corresponds to the Detection Method Maturity and Opportunity for Detection descriptions that most accurately reflect the state of the controls and enter it in the PFMEA form.
     If there is a discrepancy between the Detection Method Maturity and Opportunity for Detection, for example, select the higher Detection score.  As a process matures, its controls evolve.  Review in subsequent development cycles is more efficient than dwelling on the matter in early stages.
     Like the S and O tables, the D criteria table has a column left blank for organization-specific examples.  Future PFMEA development and training may benefit from the experience captured here.

     Determining the priority of improvement activities in aligned FMEA is done very differently than the classical method.  For this purpose, the AIAG/VDA Handbook introduces Action Priority (AP), where a lookup table replaces the RPN calculation of classical FMEA.  The same AP Table is used for DFMEA and PFMEA; it is reproduced in Exhibit 19.
     The AP Table is used to assign a priority to improvement activities for a failure chain.  To do this, first locate the row containing the Severity score in the S column.  Then, in the O column, find the sub-row containing the Occurrence score, then the sub-row containing the Detection score in the D column.  In this sub-row, in the Action Priority (AP) column, one of three priorities is assigned:
  • H:  High Priority – actions must be taken to reduce risk or acceptance of the current process justified and approved.
  • M:  Medium Priority – risk should be reduced via improvement activities or acceptance of the current process justified and approved.
  • L:  Low Priority – improvement actions can be identified, but justification and approval are not required to accept the current process.
Record the Action Priority assigned on the PFMEA form (Exhibit 15).  Development of improvements and assignment of responsibility, based on the priorities assigned, takes place in the next step.
     The final column of the AP Table, “Comments,” is left blank.  Organization-specific protocols, historical projects, acceptance authority, or other information can be cited here to assist PFMEA teams in completing analyses.

     Column R (Exhibit 15) is reserved for Special Process Characteristics.  Though the text of the Handbook makes no reference to this portion of the Standard PFMEA form, it is to be used in similar fashion as in classical FMEA.  Special Characteristics do not, however, transfer from the DFMEA, as the aligned Standard DFMEA form does not include it.  See “Vol. IV:  ‘Classical’ PFMEA” for a brief discussion of Special Process Characteristics.

     Column S in Exhibit 15 is an optional entry.  “Filter Code” could be used in various ways.  Examples include:
  • As a substitute for the “Classification” column when transitioning an existing design from classical to aligned format.  This may be done to remain consistent with the aligned DFMEA, if this practice was followed in that analysis.
  • To assign categories to aid in resource management.  These could correspond to engineering specialties, such as manufacturing, industrial, controls, and so on.  Subspecialties, such as PLC, HMI, etc., could also be coded, if desired.
  • Any creative use that assists the PFMEA owner in managing and completing the analysis or allows future analysis teams to easily search for similar issues in previous process developments.
 
6th Step – Optimization
     In the Optimization step, improvement activities are identified to reduce process risk, assigned to individuals for implementation, monitored for progress, and evaluated for effectiveness.  Relevant information is recorded in the Optimization section of the PFMEA form, shown in Exhibit 20.
     Preventive Actions (column T) are preferred to Detection Actions (column U); it is far better to eliminate an issue than to develop a better reaction to it.  The Handbook asserts that the most effective sequence of implementation is as follows:
  • Eliminate or mitigate Effects of Failure via process modification [lower Severity (S), Preventive Action]
  • Reduce the Occurrence (O) of Causes of Failure via process modification [Preventive Action]
  • Improve the ability to detect Causes of Failure or Failure Modes [lower Detection (D), Detection Action]
Prioritizing improvements in Severity over Occurrence and Detection is consistent with the classical FMEA approach.
     The Handbook suggests five possible statuses, to be recorded in column V, for the actions defined:
  • Open:  “No action defined.”
  • Decision pending:  action defined, awaiting approval.
  • Implementation pending:  action approved, awaiting implementation.
  • Completed:  implementation complete, effectiveness documented.
  • Not implemented:  action declined, current process accepted.
      Column W (Exhibit 20) is reserved for Special Product Characteristics – requirements that the customer or design team have identified as particularly sensitive to manufacturing variation.  See “Vol. III:  ‘Classical’ DFMEA” for a brief discussion of Special Product Characteristics.
     The remaining columns in the Optimization section of the PFMEA form are, essentially, carryovers from classical FMEA, with predicted AP replacing predicted RPN.  Refer to Vol. IV for further discussion of these entries.
 
Risk Communication
7th Step – Results Documentation
     Much like Planning and Preparation, Results Documentation consists of tasks that have been performed for, but often considered separate from, classical FMEA.  The aligned approach formalizes Risk Communication as an essential component of analysis by incorporating it in the Seven-Step Approach.
     A significant portion of the content of an FMEA Report, as outlined in the Handbook, is contained in the PFMEA form.  For example, the scope of analysis, high-risk failures, action prioritization, status of actions, and planned implementation dates can be culled from the form.
     A comparison of the project plan, created in Step 1, with the execution and final status of the analysis may also be a useful component of the report.  Future analysis teams may be able to apply lessons learned from deviations revealed in this assessment.
     AIAG/VDA also suggest that a commitment to review and revise the PFMEA be included in the FMEA Report.  The discussion of classical PFMEA treated this as a separate endeavor, providing further evidence of the expanse of AIAG/VDA’s efforts to create a thorough and consistent analysis process.  For further discussion on the topic, see “Review and Maintenance of the PFMEA” in FMEA – Vol. IV:  “Classical” Process Failure Modes and Effects Analysis.
 
     A Process FMEA is a valuable development and communication tool.  It ensures that the impacts of a process on customers and other stakeholders are given proper consideration.  It ensures that legal and regulatory requirements are met.  It also creates a record of development activity that can be used to refine a process, develop a new process, or train others to develop successful processes.  Practitioners are encouraged to extract maximum value from FMEA and, in the effort, possibly discover a competitive advantage for their organization.
 
     For additional guidance or assistance with Operations challenges, feel free to leave a comment, contact JayWink Solutions, or schedule an appointment.
 
     For a directory of “FMEA” volumes on “The Third Degree,” see Vol. I:  Introduction to Failure Modes and Effects Analysis.
 
References
[Link] “Potential Failure Mode and Effects Analysis,” 4ed. Automotive Industry Action Group, 2008.
[Link] “FMEA Handbook.”  Automotive Industry Action Group and VDA QMC, 2019.

 
Jody W. Phelps, MSc, PMP®, MBA
Principal Consultant
JayWink Solutions, LLC
jody@jaywink.com
]]>
<![CDATA[FMEA – Vol. VI:  “Aligned” Design Failure Modes and Effects Analysis]]>Wed, 01 Jun 2022 14:30:00 GMThttp://jaywinksolutions.com/thethirddegree/fmea-vol-vi-aligned-design-failure-modes-and-effects-analysis     To differentiate it from “classical” FMEA, the result of the collaboration between AIAG (Automotive Industry Action Group) and VDA (Verband der Automobilindustrie) is called the “aligned” Failure Modes and Effects Analysis process.  Using a seven-step approach, the aligned analysis incorporates significant work content that has typically been left on the periphery of FMEA training, though it is essential to effective analysis.
     In this installment of the “FMEA” series, development of a Design FMEA is presented following the seven-step aligned process.  Use of an aligned documentation format, the “Standard DFMEA Form Sheet,” is also demonstrated.  In similar fashion to the classical DFMEA presentation of Vol. III, the content of each column of the form will be discussed in succession.  Review of classical FMEA is recommended prior to attempting the aligned process to ensure a baseline understanding of FMEA terminology.  Also, comparisons made between classical and aligned approaches will be more meaningful and, therefore, more helpful.
     The format of the aligned FMEA form is significantly different from that in classical FMEA.  It guides the user along the seven-step path, with the information required in each step organized in multiple columns.  Color-coding is used to correlate related information in each step.  The aligned “Standard DFMEA Form Sheet” (“Form A” in Appendix A of the AIAG/VDA FMEA Handbook) is reproduced in Exhibit 1.  For ease of presentation, the form is shown in a stacked format.  In use, however, corresponding information should be recorded in a single row.
     As has been done in previous installments of the “FMEA” series, portions of the form will be shown in close-up, with reference bubbles correlating to discussion in the text.  The goal is to facilitate learning the aligned process by maintaining the links between the 7-step approach, form sections, and individual columns where information is recorded.
 
Conducting an ‘Aligned’ Design FMEA
     Failure Modes and Effects Analysis is conducted in three “stages” – System Analysis, Failure Analysis and Risk Mitigation, and Risk Communication.  These three stages are comprised of the seven-step process mentioned previously.  A graphical representation of the relationships between the three stages and seven steps is shown in Exhibit 2.  For each step, brief reminders are provided of key information and activities required or suggested.  Readers should reference this summary diagram as each step is discussed in this presentation and while conducting an FMEA.
System Analysis
1st Step – Planning & Preparation
     Classical FMEA preparations have often been viewed as separate from analysis.  Recognizing the criticality of effective planning and preparation, the aligned process from AIAG and VDA formally incorporate them in the 7-step approach to FMEA.  This is merely a change in “status,” if you will, for preparatory activities.  Thus, the discussion in “Vol. II:  Preparing for Analysis” remains valid, though introduction of the tools is dispersed among steps in the aligned approach.
     Specifically, the realm of appropriate context for FMEA remains the three uses cases defined, briefly described as (1) new design, (2) modified design, or (3) new application.  Within the applicable context, the FMEA team must understand the level of analysis required – system, subsystem, or component.
     The core and extended analysis team members are chosen in the same manner as before.  Whereas classical FMEA defines four customers of concern, the aligned process focuses on two:  (1) assembly and manufacturing plants and (2) end users.  Defining customers in advance facilitates efficient, thorough analysis, with all foreseeable Effects of Failure identified.
     To state it generally, the inputs needed to conduct an effective FMEA have not changed.  However, the aligned process provides a framework for organizing the accumulated information into a coherent project plan.  Using the Five Ts structure introduced in “FMEA – Vol. V:  Alignment,” project information is presented in a consistent manner.  The scope of analysis, schedule, team members, documentation requirements, and more are recorded in a standardized format that facilitates FMEA development, reporting, and maintenance.
     Key project information is recorded on the DFMEA form in the header section labeled “Planning & Preparation (Step 1),” shown in Exhibit 3.  The labels are largely self-explanatory, though some minor differences exist between the aligned and classical forms.  One such difference is the definition of the “Subject” (1) of analysis.  On this line, identify the design analyzed by name, part number, and “nickname,” if commonly referred to by another identifier.  For example, an aircraft flight data recorder is commonly known as a “black box;” some automobiles have similar devices that may use the same moniker.
     Key Date on the classical form has been replaced by “Start Date” (2) on the aligned form; both refer to the design freeze date.  “Confidentiality Level” (3) has been added to the form.  Three levels are suggested in the Handbook – “Business Use,” “Proprietary,” and “Confidential” – but no further guidance on their application is provided.  Discussions should take place within an organization and with customers to ensure mutually agreeable use of these designations and information security.
     Refer to the “FMEA Form Header” section of “Vol. III:  ‘Classical’ Design Failure Modes and Effects Analysis” for additional guidance on completing this section of the form.
 
Continuous Improvement
     The first two columns in the body of the DFMEA form, shown in Exhibit 4, are not included in the discussion of the seven steps.  The first column, “Issue #” (A), is used to simply number entries for easy reference.  The purpose of column B, “History/Change Authorization,” is left to users’ interpretation.  Presumably, it is used to record management approval of design changes or acceptance of existing risk, though the Handbook offers no guidance on this topic.  In whatever fashion an organization chooses to use this portion of the form, it should be documented in training to ensure consistency and minimize confusion.
 
2nd Step – Structure Analysis
     In the Structure Analysis step, the design described in Step 1 is defined further by decomposing it into systems, subsystems, and components.  To visualize the scope of analysis, block diagrams and structure trees are used.  Only those elements over which the analysis team or FMEA owner (“Design Responsible”) exerts design control are within the scope of analysis, as identified by the boundaries of the diagrams.
     Interfaces may be in or out of scope, depending on the system in question.  Design responsibility must be clearly defined to prevent oversight of important performance characteristics and controls.
     Five “primary” interface types are delineated in the Handbook:  “physical connection,” “material exchange,” “energy transfer,” “data exchange,” and “human-machine.”  A sixth type, “physical clearance,” is mentioned separately, though it is of equal importance to the five primary types.
     In addition to the type, interface analysis must also define the strength and nature (e.g. positive/negative, advantageous/detrimental, etc.) of each interface, whether internal or external to the system.  Proper analysis and development of adequate controls requires a full complement of information.
     Structure Analysis is recorded on the DFMEA form in the section displayed in Exhibit 5.  In column C, the “highest level of integration” is identified as the “Next Higher Level.”  This is the highest level system within the scope of analysis, where the Effects of Failure will be noticed.  The “Focus Element,” named in column D, is the item in the failure chain for which Failure Modes, the technical descriptions of failures, are identified.  In column E, name the “Next Lower Level” of the structure hierarchy, where Causes of Failure will be found.
     Creation of a structure tree as a component of preparation for Structure Analysis is particularly helpful in populating the DFMEA form.  As can be seen in the example in Exhibit 6, the organization of information lends itself to direct transfer to the relevant section of the standard form.  Use of this tool can accelerate analysis and assure a comprehensive assessment of components.
     Enter information in the DFMEA form proceeding across (left to right), then down, to accommodate multiple Failure Modes of a single Focus Element.
 
3rd Step – Function Analysis
     Function Analysis is the next level in the FMEA information hierarchy; it closely parallels Structure Analysis.  As seen in Exhibit 7, the Function Analysis columns in the DFMEA form are numbered, labeled, and color-coded to create visual links to those in the Structure Analysis section.  In this section, the functions and requirements of the Focus Element and adjacent levels are recorded in the same pattern used in Step 2.  A Focus Element may serve multiple functions and have multiple requirements; each should be considered separately, recorded in its own row on the form to facilitate thorough analysis.
     Like the structure tree, information can be transferred directly from the function tree to the DFMEA form.  The function tree is constructed in the same manner as the structure tree in Step 2, replacing visual representations of components with descriptions of their contributions to the operation of the system.  An example function tree and information transfer to DFMEA are shown in Exhibit 8.
     Developing a parameter diagram can help organize analysis information and visualize influences on the system in operation.  Inputs and outputs, noise and control factors, functions, and requirements can be concisely presented in this format.  An example parameter diagram (P-Diagram) is shown in “Vol. II:  Preparing for Analysis;” another, from the AIAG/VDA FMEA Handbook, is shown in Exhibit 9.
Failure Analysis and Risk Mitigation
4th Step – Failure Analysis
     Failure Analysis is the heart of an FMEA, where a system’s failure network is established.  The links between Failure Modes, Effects of Failure, and Causes of Failure, in multiple levels of analysis, are made clear in this step.
     The definitions of Failure Mode, Effect of Failure, and Cause of Failure, in practice, remain the same as in classical FMEA.  Briefly, these are:
  • Failure Mode:  the manner in which the Focus Element could fail to meet a requirement or perform a function, described in technical terms.
  • Effect of Failure:  the result of a failure, as perceived by a customer.
  • Cause of Failure:  the reason the Failure Mode occurred, described in technical terms.
     The relationships between a Failure Mode, its cause, and the resultant effect creates a failure chain, as shown in Exhibit 10.  Each link in the failure chain is the consequence of the existence of the one to its right in the diagram.  That is, the Effect of Failure is the consequence, or result, of the occurrence of the Failure Mode, which is, in turn, the consequence of the existence of the Cause of Failure.
     Each element is not limited to a single failure chain.  The occurrence of a Failure Mode may be perceived in several ways (Effects of Failure) or have multiple potential causes.  A Cause of Failure may also result in several potential Failure Modes, and so on.
     Considering failure chains at multiple levels of analysis within a system defines the system’s failure network.  A Failure Mode at the highest level of integration becomes an Effect of Failure when analyzing the next lower level.  Similarly, a Cause of Failure is the Failure Mode in the next lower level of analysis.  This transformation, presented in Exhibit 11, continues through all levels of subsystem analysis to the component-level FMEAs.
     Each failure chain is documented in a single row of the DFMEA form, in the Failure Analysis section, shown in Exhibit 12.  The form maintains the relationships within the failure chain, as shown in Exhibit 10 and discussed above.  It also maintains the numbering and color-coding convention that links Failure Analysis to Function Analysis (Step 3) and Structure Analysis (Step 2), as shown in Exhibit 13.
     As is the case for the previous two steps, information can be transferred directly from the structure tree, were it created in advance, to the DFMEA form.  The Failure Analysis information transfer is shown in Exhibit 14.
     To complete the Failure Analysis step, the Effect of Failure must be evaluated and assigned a Severity (S) score.  To do this, consult the Severity criteria table, shown in Exhibit 15, and select the Severity score that corresponds to the applicable effect and criteria description.  Enter this number on the DFMEA form in the column labeled “Severity (S) of FE.”
     If there is uncertainty or disagreement about the appropriate Severity score to assign an Effect of Failure (e.g. “Is it a 3 or a 4?”), select the highest score being considered to ensure that the issue receives sufficient attention as development proceeds.  Confidence in evaluations and scores typically increase as a design matures; concordance often follows.
     The last column of the “DFMEA Severity Criteria Table” (Exhibit 15) is left blank in the Handbook because it is not universally applicable.  An organization can record its own examples of Effects of Failure to be used as comparative references when conducting a new FMEA or to aid in training.  Examples cited for various Severity scores can provide great insight into an organization’s understanding, or lack thereof, of the customer perspective.
 
5th Step – Risk Analysis
     The design team conducts Risk Analysis to identify the controls used to prevent or detect failures, evaluate their effectiveness, and prioritize improvement activities.  Relevant information is recorded in the Risk Analysis section of the DFMEA form, shown in Exhibit 16.
     In column F, record the Design Prevention Controls incorporated to preclude activation of the failure chain.  A vast array of prevention controls is available; however, it is likely that a very limited selection is applicable to any single Cause of Failure.  Cite only those expected to prevent the specific Cause of Failure with which they are correlated (i.e. in the same row on the form).
     Though not an exhaustive list of Design Prevention Controls, some examples follow:
  • design standard published by a manufacturer of purchased components, industry group, or other recognized authority
  • material specifications (e.g. ASTM)
  • re-use of previously validated design with history of successful service
  • use of safety factors
  • error-proofing (see “The War on Error – Vol. II”)
  • redundancy
  • electromagnetic shielding, thermal insulation, etc.
     The effectiveness of Design Prevention Controls is reflected in the Occurrence (O) score, recorded in column G of the DFMEA form.  To assign an Occurrence score to each Cause of Failure, consult the Occurrence criteria table shown in Exhibit 17.  Select the Occurrence score that corresponds to the criteria that best describes the design and application.
     If sufficient data is available to make reasonable predictions, a quantitative method of scoring can be used.  For this, substitute the alternate DFMEA Occurrence criteria table, shown in Exhibit 18, for the table of Exhibit 17.  The detailed criteria descriptions are unchanged; the “incidents per 1000” estimates simply replace the qualitative summary terms (high, low, etc.).
     If the frequency of occurrence of a Cause of Failure “falls between” two Occurrence scores, or there is disagreement about the correct frequency, select the higher Occurrence score.  Additional review in subsequent development cycles is likely to resolve the matter more efficiently than extensive debate in early stages.
     Both DFMEA Occurrence tables have a column left blank for organization-specific examples to be recorded.  These can be used as comparative references to facilitate future DFMEA development or as training aids.
 
     In column H of the Risk Analysis section of the DFMEA form, identify the Design Detection Controls in place to warn of the existence of the Failure Mode or Cause of Failure before the design is released to production.  Examples include endurance testing, interference analysis, designed experiments, proof testing, and electrical (e.g. “Hi-Pot”) testing.
     Assess the effectiveness of current detection controls according to the Detection criteria table, shown in Exhibit 19.  Select the Detection (D) score that corresponds to the Detection Method Maturity and Opportunity for Detection descriptions that most accurately reflect the state of the controls and enter it in column J.
     If there is a discrepancy between the Detection Method Maturity and Opportunity for Detection, for example, select the higher Detection score.  As a design matures, its controls evolve.  Review in subsequent development cycles is more efficient than dwelling on the matter in early stages.
     Like the S and O tables, the D criteria table has a column left blank for organization-specific examples.  Future DFMEA development and training may benefit from the experience captured here.
 
     Determining the priority of improvement activities in aligned FMEA is done very differently than the classical method.  For this purpose, the AIAG/VDA Handbook introduces Action Priority (AP), where a lookup table replaces the RPN calculation of classical FMEA.  The AP table is shown in Exhibit 20.
     The AP Table is used to assign a priority to improvement activities for a failure chain.  To do this, first locate the row containing the Severity score in the S column.  Then, in the O column, find the sub-row containing the Occurrence score, then the sub-row containing the Detection score in the D column.  In this sub-row, in the Action Priority (AP) column, one of three priorities is assigned:
  • H:  High Priority – actions must be taken to reduce risk or acceptance of the current design justified and approved.
  • M:  Medium Priority – risk should be reduced via improvement activities or acceptance of the current design justified and approved.
  • L:  Low Priority – improvement actions can be identified, but justification and approval are not required to accept the current design.
Record the Action Priority assigned in column K of the DFMEA form (Exhibit 16).  Development of improvements and assignment of responsibility, based on the priorities assigned, takes place in the next step.
     The final column of the AP Table, “Comments,” is left blank.  Organization-specific protocols, historical projects, acceptance authority, or other information can be cited here to assist DFMEA teams in completing analyses.
 
     Column L in Exhibit 16 is an optional entry.  “Filter Code” could be used in various ways.  Examples include:
  • As a substitute for the “Classification” column when transitioning an existing design from classical to aligned format.  The aligned DFMEA form does not have space reserved for special characteristic designations.
  • To assign categories to aid in resource management.  These could correspond to engineering specialties, such as structural, mechanical, electrical, human factors, and so on.  Subspecialties, such as lighting, haptics, etc., could also be coded, if desired.
  • Any creative use that assists the DFMEA owner in managing and completing the analysis or allows future analysis teams to easily search for similar issues in previous designs.
 
6th Step – Optimization
     In the Optimization step, improvement activities are identified to reduce risk, assigned to individuals for implementation, monitored for progress, and evaluated for effectiveness.  Relevant information is recorded in the Optimization section of the DFMEA form, shown in Exhibit 21.
     Preventive Actions (column M) are preferred to Detection Actions (column N); it is far better to eliminate an issue than to develop a better reaction to it.  The Handbook asserts that the most effective sequence of implementation is as follows:
  • Eliminate or mitigate Effects of Failure via design modification [lower Severity (S), Preventive Action]
  • Reduce the Occurrence (O) of Causes of Failure via design modification [Preventive Action]
  • Improve the ability to detect Causes of Failure or Failure Modes [lower Detection (D), Detection Action]
Prioritizing improvements in Severity over Occurrence and Detection is consistent with the classical FMEA approach.
     The Handbook suggests five possible statuses, to be recorded in column P, for the actions defined:
  • Open:  “No action defined.”
  • Decision pending:  action defined, awaiting approval.
  • Implementation pending:  action approved, awaiting implementation.
  • Completed:  implementation complete, effectiveness documented.
  • Not implemented:  action declined, current design accepted.
The example DFMEA form in the Handbook also shows a status of “planned;” a concise alternative to “implementation pending.”
     The remaining columns in the Optimization section of the DFMEA form are, essentially, carryovers from classical FMEA, with predicted AP replacing predicted RPN.  Refer to Vol. III for further discussion of these entries.
 
Risk Communication
7th Step – Results Documentation
     Much like Planning and Preparation, Results Documentation consists of tasks that have been performed for, but often considered separate from, classical FMEA.  The aligned approach formalizes Risk Communication as an essential component of analysis by incorporating it in the Seven-Step Approach.
     A significant portion of the content of an FMEA Report, as outlined in the Handbook, is contained in the DFMEA form.  For example, the scope of analysis, high-risk failures, action prioritization, status of actions, and planned implementation dates can be culled from the form.
     A comparison of the project plan, created in Step 1, with the execution and final status of the analysis may also be a useful component of the report.  Future analysis teams may be able to apply lessons learned from deviations revealed in this assessment.
     AIAG/VDA also suggest that a commitment to review and revise the DFMEA be included in the FMEA Report.  The discussion of classical DFMEA treated this as if it were a separate endeavor, providing further evidence of the expanse of AIAG/VDA’s efforts to create a thorough and consistent analysis process.  For further discussion on the topic, see “Review and Maintenance of the DFMEA” in FMEA – Vol. III:  “Classical” Design Failure Modes and Effects Analysis.
 
     A Design FMEA is a valuable development and communication tool.  It ensures that the impacts of a product design on customers and other stakeholders are given proper consideration.  It ensures that legal and regulatory requirements are met.  It also creates a record of development activity that can be used to refine a product, develop a derivative product, or train others to develop successful products.  Practitioners are encouraged to extract maximum value from FMEA and, in the effort, possibly discover a competitive advantage for their organization.
 
     For additional guidance or assistance with Operations challenges, feel free to leave a comment, contact JayWink Solutions, or schedule an appointment.
 
     For a directory of “FMEA” volumes on “The Third Degree,” see Vol. I:  Introduction to Failure Modes and Effects Analysis.
 
References
[Link] “Potential Failure Mode and Effects Analysis,” 4ed. Automotive Industry Action Group, 2008.
[Link] “FMEA Handbook.”  Automotive Industry Action Group and VDA QMC, 2019.

 
Jody W. Phelps, MSc, PMP®, MBA
Principal Consultant
JayWink Solutions, LLC
jody@jaywink.com
]]>
<![CDATA[FMEA – Vol. V:  Alignment]]>Wed, 18 May 2022 14:30:00 GMThttp://jaywinksolutions.com/thethirddegree/fmea-vol-v-alignment     Suppliers producing parts for automotive manufacturers around the world have always been subject to varying documentation requirements.  Each OEM (Original Equipment Manufacturer) customer defines its own requirements; these requirements are strongly influenced by the geographic location in which they reside.
     In an effort to alleviate confusion and the documentation burden of a global industry, AIAG (Automotive Industry Action Group) of North America and VDA (Verband der Automobilindustrie) of Germany jointly published the aligned “FMEA Handbook” in 2019.  Those experienced with “classical” FMEA (Vol. III, Vol. IV) will recognize its influence in the new “standard;” however, there are significant differences that require careful consideration to ensure a successful transition.
     A significant improvement to the typical presentation of classical FMEA is quickly revealed in the aligned standard.  Aspects of FMEA that were previously taken for granted, or understood through experience, are explicitly presented in the new Handbook.
     Discussion of the scope of analysis is expanded beyond defining a system, subsystem, or component FMEA to the type of risks to be considered.  As shown in Exhibit 1, FMEA is employed to investigate only technical risks.  Financial, time, and strategy risks are explicitly excluded from FMEA, though they remain important components of product and process development discussions.
     Limitations of FMEA that may require other analysis techniques to be employed are declared early in the presentation of the aligned standard.  Namely, FMEA is a qualitative single-point failure analysis technique.  Quantitative analysis and multi-point failures require other tools and techniques.  This discussion is relegated to the appendices in the previous AIAG Handbook.
     The aligned standard restates the requirement for management commitment of resources – time and expertise – to ensure successful FMEA development.  It is more explicit in its declaration that management is responsible for accepting the risks and risk mitigation strategies identified.  It does not point out, however, that management is also responsible for all risks that have not been identified.  Doing so would underline the value of training technical personnel in FMEA development to ensure thorough analysis and an accurate risk profile.
 
     Basic attributes desired of FMEA are often assumed to be understood, undermining previous Handbooks’ utility as an introduction to the methodology.  The aligned standard remedies this by defining the following four attributes sought:
  • Clear:  “[P]otential Failure Modes are described in technically precise, specific terms…” [emphasis added].  Normative statements and those with multiple interpretations are avoided.
  • True:  Accurate descriptions of Effects of Failure are provided; embellishment (“overstatement”) and trivialization (“understatement”) are eschewed.
  • Realistic:  “Reasonable” Causes of Failure are identified; “extreme events,” such as natural disasters, are not considered.  Foreseeable misuse (unintentional) is considered, but sabotage or other deliberate action is not.
  • CompleteAll potential Failure Modes and Causes of Failure are divulged.  Concern for creating a negative impression of a product or process does not justify withholding information.
 
The “New” Methodology
     The FMEA methodology prescribed in the AIAG/VDA aligned standard, in most regards, is not a radical departure from its predecessors, though important differences exist.  The steps followed and forms used will be familiar to practitioners of classical FMEA, though some terminology and formatting has changed.
     The AIAG/VDA FMEA Handbook defines a seven-step process for conducting an FMEA.  The seven steps are divided among three task categories, as shown in Exhibit 2.
     AIAG/VDA prescribes the “Five Ts” to guide FMEA planning discussions.  These topics should be discussed at the initiation of a project (i.e. in the “1st Step”).  The Five Ts are summarized below:
  • InTent:  Ensure that all members of the FMEA team understand its purpose and can competently contribute to its objectives.
  • Timing:  FMEA provides the greatest benefit when conducted proactively; that is, prior to finalizing or launching a product or process.  Recommended timing, aligned with APQP (Advanced Product Quality Planning) or MLA (Maturity Level Assurance) phases, is shown in Exhibit 3.
  • Team:  Identify the core and extended team members needed to successfully complete the FMEA.  Selecting members for a cross-functional team was discussed in Vol. II; a suggested roster is provided in the Handbook.
  • Tasks:  The 7 – Step FMEA Process, shown in Exhibit 2, provides a framework for ensuring all necessary tasks are performed.  Discussion can include detailed descriptions, assignment of responsibility, priority, etc.
  • Tools:  Identify the software package that will be used to document the FMEA and ensure that team members are proficient in its use.
 
     The first five steps of the FMEA process each provide the basis for the subsequent step.  That is, the information gathered in each step is used to complete the next step.  Each of the seven steps are discussed in greater detail in subsequent installments, each dedicated to one type of FMEA.
     The jointly-published FMEA Handbook is significantly expanded from its predecessors.  The information provided in this installment is only a preview and introduction to the approach presented by AIAG and VDA.  Upcoming installments of “The Third Degree” will distill the new Handbook into serviceable guides to aid experienced and novice FMEA practitioners alike.
 
     For additional guidance or assistance with Operations challenges, feel free to leave a comment, contact JayWink Solutions, or schedule an appointment.
 
     For a directory of “FMEA” volumes on “The Third Degree,” see Vol. I:  Introduction to Failure Modes and Effects Analysis.
 
References
[Link] “Potential Failure Mode and Effects Analysis,” 4ed. Automotive Industry Action Group, 2008.
[Link] “FMEA Handbook.”  Automotive Industry Action Group and VDA QMC, 2019.

 
Jody W. Phelps, MSc, PMP®, MBA
Principal Consultant
JayWink Solutions, LLC
jody@jaywink.com
]]>
<![CDATA[FMEA –Vol. IV:  “Classical” Process Failure Modes and Effects Analysis]]>Wed, 04 May 2022 14:30:00 GMThttp://jaywinksolutions.com/thethirddegree/fmea-vol-iv-classical-process-failure-modes-and-effects-analysis     Preparations for Process Failure Modes and Effects Analysis (Process FMEA) (see Vol. II) occur, in large part, while the Design FMEA undergoes revision to develop and assign Recommended Actions.  An earlier start, while ostensibly desirable, may result in duplicated effort.  As a design evolves, the processes required to support it also evolve; allowing a design to reach a sufficient level of maturity to minimize process redesign is an efficient approach to FMEA.
     In this installment of the “FMEA” series, how to conduct a “classical” Process FMEA (PFMEA) is presented as a close parallel to that of DFMEA (Vol. III).  Each is prepared as a standalone reference for those engaged in either activity, but reading both is recommended to maintain awareness of the interrelationship of analyses.
     The recommended format used to describe the classical PFMEA process is shown in Exhibit 1.  It is nearly identical to the DFMEA form; only minor terminology changes differentiate the two.  This is to facilitate sharing of information between analysis teams and streamline training efforts for both.  The form is shown in its entirety only to provide an overview and a preview of the analysis steps involved.  The form has been divided into titled sections for purposes of presentation.  These titles and divisions are not part of any industry standard; they are only used here to identify logical groupings of information contained in the FMEA and to aid learning.
     Discussion of each section will be accompanied by a close-up image of the relevant portion of the form.  The columns are identified by an encircled letter for easy reference to its description in the text.  The top of the form contains general information about the analysis; this is where the presentation of the “classical” Process FMEA begins.
 
FMEA Form Header
1) Check the box that best describes the scope of the PFMEA – does it cover an entire system, a subsystem, or a single component?  For example, analysis may be conducted on a Laundry system, a Wash subsystem, or a Spin Cycle component.
2) On the lines below the checkbox, identify the process system, subsystem, or component analyzed.  Include information that is unique to the process under scrutiny, including the specific technology used (e.g. resistance welding), product family, etc.
3) Process Responsibility:  Identify the lead engineer responsible for developing the process, and assuring and maintaining its performance.
4) Key Date:  Date of design freeze (testing complete, documented process approved by customer, etc.).
5) FMEA No.:  Provide a unique identifier to be used for FMEA documentation (this form and all accompanying documents and addenda).  It is recommended to use a coded system of identification to facilitate organization and retrieval of information.  For example, FMEA No. 2022 – J – ITS – C – 6 could be interpreted as follows:
            2022 – year of process launch,
            W – process family (e.g. welding),
            RES – process code (e.g. resistance),
            C – Component-level FMEA,
            6 – sixth component FMEA for the process “system.”
This is an arbitrary example; organizations should develop their own meaningful identification systems.
6) Page x of y.:  Track the entire document to prevent loss of content.
7) FMEA Date:  Date of first (“original”) FMEA release.
8) Rev.:  Date of release of current revision.

FMEA Form Columns
A – Process Step/Function
     List Process Steps by name or by description of purpose (Function).  Information recorded on a Process Flow Diagram (PFD) can be input directly in this column.  This list may consist of a combination of Process Step names and functional descriptions.  For example, a process name that is in wide use, but the purpose of which is not made obvious by the name, should be accompanied by a functional description.  This way, those that are not familiar with the process name are not put at a disadvantage when reviewing the FMEA.
B – Requirements
     Describe what a Process Step is intended to achieve, or the parameters of the Function (e.g. boil water requires “heat water to 100° C”)
C – Potential Failure Modes
     Describe how a Requirement could fail to be met.  A Process Step/Function may have multiple Failure Modes; each undesired outcome is defined in technical terms.  Opposite, or “mirror-image” conditions (e.g. high/low, long/short, left/right) should always be considered.  Conditional process failures should also be included, where applicable, though demand failures (when activated) and standby failures (when not in use) are more likely to delay processing than to effect a process’ performance.  Operational failures (when in use) require the greatest effort in analysis.
D – Potential Effects of Failure
     Describe the undesired outcome(s) from the customer perspective.  Effects of Failure may include physical damage, reduced performance, intermittent function, unsatisfactory aesthetics, or other deviation from reasonable customer expectations.  All “customers” must be considered, from internal to end user; packaging, shipping, installation and service crews, and others could be effected.
E – Severity (S)
     Rank each Effect of Failure on a predefined scale.  Loosely defined, the scale ranges from 1 – insignificant to 10 – resulting in serious bodily injury or death.  The suggested Severity evaluation criteria and ranking scale from AIAG’s “FMEA Handbook,” shown in Exhibit 7, provides guidelines for evaluating the impact of failures on both internal and external customers.  To evaluate nonautomotive designs, the criteria descriptions can be modified to reflect the characteristics of the product, industry, and application under consideration; an example is shown in Exhibit 8.
F – Classification
     Identify high-priority Failure Modes and Causes of Failure – that is, those that require the most rigorous monitoring or strictest controls.  These may be defined by customer requirements, empirical data, the lack of technology currently available to improve process performance, or other relevant characteristic.  Special characteristics are typically identified by a symbol or abbreviation that may vary from one company to another.  Examples of special characteristic identifiers are shown in Exhibit 9Special characteristics identified on a DFMEA should be carried over to any associated PFMEAs to ensure sufficient controls are implemented.
G – Potential Causes of Failure
     Identify the potential causes of the Failure Mode.  Like Failure Modes, Causes of Failure must be defined in technical terms, rather than customer perceptions (i.e. state the cause of the Failure Mode, not the Effect).  A Failure Mode may have multiple potential Causes; identify and evaluate each of them individually.
     Incoming material is assumed to be correct (within specifications); therefore, it is not to be listed as a Cause of Failure.  Incorrect material inputs are evidence of failures of prior processes (production, packaging, measurement, etc.), but their effect on the process should not be included in the PFMEA.  Also, descriptions must be specific.  For example, the phrase “operator error” should never be used, but “incorrect sequence followed” provides information that is useful in improving the process.
H – Prevention Controls
     Identify process controls used to prevent the occurrence of each Failure Mode or undesired outcome.  Process guidelines from material or equipment suppliers, simulation, designed experiments, statistical process control (SPC), and error-proofing (see “The War on Error – Vol. II”) are examples of Process Prevention Controls.
I – Occurrence (O)
     Rank each Failure Mode according to its frequency of occurrence, a measure of the effectiveness of the Process Prevention Controls employed.  Occurrence rating tables typically present multiple methods of evaluation, such as qualitative descriptions of frequency and quantitative probabilities.  The example in Exhibit 11 is from the automotive industry, while the one in Exhibit 12 is generalized for use in any industry.  Note that the scales are significantly different; once a scale is chosen, or developed, that elicits sufficient and appropriate responses to rankings, it must be used consistently.  That is, rankings contained in each PFMEA must have the same meaning.
     The potential Effects of Failure are evaluated individually via the Severity ranking, but collectively in the Occurrence ranking.  This may seem inconsistent, or counterintuitive, at first; however, which of the potential Effects will be produced by a failure cannot be reliably predicted.  Therefore, Occurrence of the Failure Mode must be ranked.  For a single Failure Mode, capable of producing multiple Effects, ranking Occurrence of each Effect would understate the significance of the underlying Failure Mode and its Causes.
J – Detection Controls – Failure Mode
     Identify process controls used to detect each Failure Mode.  Examples include various sensors used to monitor process parameters, measurements, post-processing verifications, and error-proofing in subsequent operations.
K – Detection Controls – Cause
     Identify process controls used to detect each Cause of Failure.  Similar tools and methods are used to detect Causes and Failure Modes.
L – Detection (D)
     Rank the effectiveness of all current Process Detection Controls for each Failure Mode.  This includes Detection Controls for both the Failure Mode and potential Causes of Failure.  The Detection ranking used for RPN calculation is the lowest for the Failure Mode.  Including the D ranking in the description for each Detection Control makes identification of the most effective control a simple matter.  Detection ranking table examples are shown in Exhibit 13 and Exhibit 14.  Again, differences can be significant; select or develop an appropriate scale and apply it consistently.
M – Risk Priority Number (RPN)
     The Risk Priority Number is the product of the Severity, Occurrence, and Detection rankings:  RPN = S x O x D.  The RPN column provides a snapshot summary of the overall risk associated with the design.  On its own, however, it does not provide the most effective means to prioritize improvement activities.  Typically, the S, O, and D rankings are used, in that order, to prioritize activities.  For example, all high Severity rankings (e.g. S ≥ 7) require review and improvement.  If a satisfactory process redesign cannot be developed or justified, management approval is required to accept the current process.
     Similarly, all high Occurrence rankings (e.g. O ≥ 7) require additional controls to reduce the frequency of failure.  Those that cannot be improved require approval of management to allow the process to operate in its existing configuration.  Finally, controls with high Detection rankings (e.g. D ≥ 6) require improvement or justification and management approval.
     Continuous improvement efforts are prioritized by using a combination of RPN and its component rankings, S, O, and D.  Expertise and judgment must be applied to develop Recommended Actions and to determine which ones provide the greatest potential for risk reduction.
     Requiring review or improvement according to threshold values is an imperfect practice.  It incentivizes less-conscientious evaluators to manipulate rankings, to shirk responsibility for improvement activities, process performance and, ultimately, product quality.  This is particularly true of RPN, as small changes in component rankings that are relatively easy to justify result in large changes in RPN.  For this reason, review based on threshold values is only one improvement step and RPN is used for summary and comparison purposes only.
N – Recommended Actions
     Describe improvements to be made to the process or controls to reduce risk.  Several options can be included; implementation will be prioritized according to evaluations of risk reduction.  Lowering Severity is the highest priority, followed by reducing frequency of Occurrence, and improving Detection of occurrences.
O – Responsibility
     Identify the individual who will be responsible for executing the Recommended Action, providing status reports, etc.  More than one individual can be named, but this should be rare; the first individual named is the leader of the effort and is ultimately responsible for execution.  Responsibility should never be assigned to a department, ad hoc team, or other amorphous group.
P – Target Completion Date
     Assign a due date for Recommended Actions to be completed.  Recording dates in a separate column facilitates sorting for purposes of status updates, etc.
Q – Actions Taken
     Describe the Actions Taken to improve the process or controls and lower the inherent risk.  These may differ from the Recommended Actions; initial ideas are not fully developed and may require adjustment and adaptation to successfully implement.  Due to limited space in the form, entering a reference to an external document that details the Actions Taken is acceptable.  The document should be identified with the FMEA No. and maintained as an addendum to the PFMEA.
R – Effective Date
     Document the date that the Actions Taken were complete, or fully integrated into the process.  Recording dates in a separate column facilitates FMEA maintenance, discussed in the next section.
S – Severity (S) – Predicted
     Rank each Effect of Failure as it is predicted to effect internal or external customers after the Recommended Actions are fully implemented.  Severity rankings rarely change; it is more common for a process change to eliminate a Failure Mode or Effect of Failure.  In such case, the PFMEA is revised, excluding the Failure Mode or Effect from further analysis.  Alternatively, the entry is retained, with S lowered to 1, to maintain a historical record of process development.
T – Occurrence (O) – Predicted
     Estimate the frequency of each Failure Mode’s Occurrence after Recommended Actions are fully implemented.  Depending on the nature of the Action Taken, O may or may not change.
U – Detection (D) – Predicted
     Rank the predicted effectiveness of all Process Detection Controls after Recommended Actions are fully implemented.  Depending on the nature of the Action Taken, D may or may not change.
     * Predictions of S, O, and D must be derived from the same scales as the original rankings.
V – Risk Priority Number (RPN) – Predicted
     Calculate the predicted RPN after Recommended Actions are fully implemented.  This number can be used to evaluate the relative effectiveness of Recommended Actions.
 
Review and Maintenance of the PFMEA
     Review of the PFMEA is required throughout the lifespan of the process.  As Recommended Actions are implemented, the results must be evaluated and compared to predictions and expectations.
     Performance of each Process Step or Function effected by Actions Taken must be reevaluated and new S, O, and D rankings assigned as needed.  Simply assuming that the predicted values have been achieved is not sufficient; the effectiveness of the Actions Taken must be confirmed.
     Once the effectiveness of Actions Taken has been evaluated, information from the Action Results section can be transferred to the body of the PFMEA.  Descriptions in the relevant columns are updated to reflect process development activity; new rankings replace the old.  New Recommended Actions can also be added to further improve the process’ risk profile.  The next round of improvements are prioritized according to the updated rankings and Recommended Actions.
     Maintenance of the PFMEA is the practice of systematically reviewing, updating, and reprioritizing activities as described above.  The PFMEA will undergo many rapid maintenance cycles prior to product launch.  Once the process is approved for continuous operation, PFMEA maintenance activity tends to decline sharply; it should not, however, cease, as long as the process remains in operation.  Periodic reviews should be used to evaluate the potential of new technologies, incorporate process and field data collected, and apply any knowledge gained since the process was approved.  Any process change must also initiate a review of the PFMEA sections pertaining to Process Steps or Functions effected by the revision.
 
     The PFMEA and all related documentation – diagrams and other addenda – remain valid and, therefore, require maintenance for the life of the process.  It serves as input to subsequent analyses and Quality documentation.  The PFMEA is an important component in the documentation chain of a successful product.  The investment made in a thoughtful analysis is returned manyfold during the lifecycle of the product, its derivatives, and any similar or related products.
 
     For additional guidance or assistance with Operations challenges, feel free to leave a comment, contact JayWink Solutions, or schedule an appointment.
 
     For a directory of “FMEA” volumes on “The Third Degree,” see Vol. I:  Introduction to Failure Modes and Effects Analysis.
 
References
[Link] “Potential Failure Mode and Effects Analysis,” 4ed. Automotive Industry Action Group, 2008.
[Link] Creating Quality.  William J. Kolarik; McGraw-Hill, Inc., 1995. 
[Link] The Six Sigma Memory Jogger II.  Michael Brassard, Lynda Finn, Dana Ginn, Diane Ritter; GOAL/QPC, 2002.
[Link] “FMEA Handbook Version 4.2.”  Ford Motor Company, 2011.

 
Jody W. Phelps, MSc, PMP®, MBA
Principal Consultant
JayWink Solutions, LLC
jody@jaywink.com
]]>
<![CDATA[FMEA –Vol. III:  “Classical” Design Failure Modes and Effects Analysis]]>Wed, 20 Apr 2022 14:30:00 GMThttp://jaywinksolutions.com/thethirddegree/fmea-vol-iii-classical-design-failure-modes-and-effects-analysis     In the context of Failure Modes and Effects Analysis (FMEA), “classical” refers to the techniques and formats that have been in use for many years, such as those presented in AIAG’s “FMEA Handbook” and other sources.  Numerous variations of the document format are available for use.  In this discussion, a recommended format is presented; one that facilitates a thorough, organized analysis.
     Preparations for FMEA, discussed in Vol. II, are agnostic to the methodology and document format chosen; the inputs cited are applicable to any available.  In this installment of the “FMEA” series, how to conduct a “classical” Design FMEA (DFMEA) is presented by explaining each column of the recommended form.  Populating the form columns in the proper sequence is only an approximation of analysis, but it is a very useful one for gaining experience with the methodology.
     The recommended format used to describe the classical Design FMEA process is shown in Exhibit 1.  There is no need to squint; it is only to provide an overview and a preview of the steps involved.  The form has been divided into titled sections for purposes of presentation.  These titles and divisions are not part of any industry standard; they are only used here to identify logical groupings of information contained in the FMEA and to aid learning.
     Discussion of each section will be accompanied by a close-up image of the relevant portion of the form.  The columns are identified by an encircled letter for easy reference to its description in the text.  The top of the form contains general information about the analysis; this is where the presentation of the “classical” Design FMEA begins.

FMEA Form Header
1) Check the box that best describes the scope of the DFMEA – does it cover an entire system, a subsystem, or a single component?
2) On the lines below the checkbox, identify the system, subsystem, or component analyzed.  Include relevant information, such as applicable model, program, product family, etc.
3) Design Responsible:  Identify the lead engineer responsible for finalizing and releasing the design.
4) Key Date:  Date of design freeze (design is “final”).
5) FMEA No.:  Provide a unique identifier to be used for FMEA documentation (this form and all accompanying documents and addenda).  It is recommended to use a coded system of identification to facilitate organization and retrieval of information.  For example, FMEA No. 2022 – J – ITS – C – 6 could be interpreted as follows:
            2022 – year of product launch,
            J – product family,
            ITS – product code,
            C – Component-level FMEA,
            6 – sixth component FMEA for the product.
This is an arbitrary example; organizations should develop their own meaningful identification systems.
6) Page x of y.:  Track the entire document to prevent loss of content.
7) FMEA Date:  Date of first (“original”) FMEA release.
8) Rev.:  Date of release of current revision.

FMEA Form Columns
A – Item/Function
     List (a) Items for hardware approach or (b) Functions for functional approach to conducting a Design FMEA.  A hardware approach can be useful during part-consolidation (i.e. design for manufacture or DFM) efforts; a functional approach facilitates taking a customer perspective.  A hardware approach is more common for single-function or “simple” designs with low part count, while a functional approach may be better suited to more complex designs with high part counts or numerous functions.  A hybrid approach, or a combination of hardware and functional approaches, is also possible.

B – Requirements
     Describe (a) an Item’s contribution to the design (e.g. screw is required to “secure component to frame”) or (b) a Function’s parameters (e.g. control temperature requires “45 ± 5° C”)
C – Potential Failure Modes
     Describe (a) the manner in which the Item could fail to meet a requirement or (b) the Function could fail to be properly executed.  An Item/Function may have multiple Failure Modes; each undesired outcome is defined in technical terms.  Opposite, or “mirror-image” conditions (e.g. high/low, long/short, left/right) should always be considered.  Conditional failures should also be included – demand failures (when activated), operational failures (when in use), and standby failures (when not in use) may have special causes that require particular attention.

D – Potential Effects of Failure
     Describe the undesired outcome(s) from the customer perspective.  Effects of Failure may include physical damage, reduced performance, intermittent function, unsatisfactory aesthetics, or other deviation from reasonable customer expectations.  All “customers” must be considered, from internal to end user; packaging, shipping, installation and service crews, and others could be effected.

E – Severity (S)
     Rank each Effect of Failure on a predefined scale.  Loosely defined, the scale ranges from 1 – insignificant to 10 – resulting in serious bodily injury or death.  The suggested Severity evaluation criteria and ranking scale from AIAG’s “FMEA Handbook” is shown in Exhibit 7.  To evaluate nonautomotive designs, the criteria descriptions can be modified to reflect the characteristics of the product, industry, and application under consideration; an example is shown in Exhibit 8.
F – Classification
     Identify high-priority Failure Modes and Causes of Failure – that is, those that require the most rigorous monitoring or strictest controls.  These may be defined by customer requirements, empirical data, the lack of technology currently available to improve design performance, or other relevant characteristic.  Special characteristics are typically identified by a symbol or abbreviation that may vary from one company to another.  Examples of special characteristic identifiers are shown in Exhibit 9.
G – Potential Causes of Failure
     Identify the potential causes of the Failure Mode.  Like Failure Modes, Causes of Failure must be defined in technical terms, rather than customer perceptions (i.e. state the cause of the Failure Mode, not the Effect).  A Failure Mode may have multiple potential Causes; identify and evaluate each of them individually.
H – Prevention Controls
     Identify design controls used to prevent the occurrence of each Failure Mode or undesired outcome.  Design standards (e.g. ASME Pressure Vessel Code), simulation, designed experiments, and error-proofing (see “The War on Error – Vol. II”) are examples of Design Prevention Controls.

I – Occurrence (O)
     Rank each Failure Mode according to its frequency of occurrence, a measure of the effectiveness of the Design Prevention Controls employed.  Occurrence rating tables typically present multiple methods of evaluation, such as qualitative descriptions of frequency and quantitative probabilities.  The example in Exhibit 11 is from the automotive industry, while the one in Exhibit 12 is generalized for use in any industry.  Note that the scales are significantly different; once a scale is chosen, or developed, that elicits sufficient and appropriate responses to rankings, it must be used consistently.  That is, rankings contained in each DFMEA must have the same meaning.
     The potential Effects of Failure are evaluated individually via the Severity ranking, but collectively in the Occurrence ranking.  This may seem inconsistent, or counterintuitive, at first; however, which of the potential Effects will be produced by a failure cannot be reliably predicted.  Therefore, Occurrence of the Failure Mode must be ranked.  For a single Failure Mode, capable of producing multiple Effects, ranking Occurrence of each Effect would understate the significance of the underlying Failure Mode and its Causes.

J – Detection Controls – Failure Mode
     Identify design controls used to detect each Failure Mode.  Examples include design reviews, simulation, mock-up and prototype testing.

K – Detection Controls – Cause
     Identify design controls used to detect each Cause of Failure.  Examples include validation and reliability testing and accelerated aging.

L – Detection (D)
     Rank the effectiveness of all current Design Detection Controls for each Failure Mode.  This includes Detection Controls for both the Failure Mode and potential Causes of Failure.  The Detection ranking used for RPN calculation is the lowest for the Failure Mode.  Including the D ranking in the description for each Detection Control makes identification of the most effective control a simple matter.  Detection ranking table examples are shown in Exhibit 13 and Exhibit 14.  Again, differences can be significant; select or develop an appropriate scale and apply it consistently.
M – Risk Priority Number (RPN)
     The Risk Priority Number is the product of the Severity, Occurrence, and Detection rankings:  RPN = S x O x D.  The RPN column provides a snapshot summary of the overall risk associated with the design.  On its own, however, it does not provide the most effective means to prioritize improvement activities.  Typically, the S, O, and D rankings are used, in that order, to prioritize activities.  For example, all high Severity rankings (e.g. S ≥ 7) require review and improvement.  If an appropriate redesign cannot be developed or justified, management approval is required to accept the current design.
     Similarly, all high Occurrence rankings (e.g. O ≥ 7) require additional controls to reduce the frequency of failure.  Those that cannot be improved require approval of management to allow the design to enter production.  Finally, controls with high Detection rankings (e.g. D ≥ 6) require improvement or justification and management approval.
     Continuous improvement efforts are prioritized by using a combination of RPN and its component rankings, S, O, and D.  Expertise and judgment must be applied to develop Recommended Actions and to determine which ones provide the greatest potential for risk reduction.
     Requiring review or improvement according to threshold values is an imperfect practice.  It incentivizes less-conscientious evaluators to manipulate rankings, to shirk responsibility for improvement activities and, ultimately, product performance.  This is particularly true of RPN, as small changes in component rankings that are relatively easy to justify result in large changes in RPN.  For this reason, review based on threshold values is only one improvement step and RPN is used for summary and comparison purposes only.
N – Recommended Actions
     Describe improvements to be made to the design or controls to reduce risk.  Several options can be included; implementation will be prioritized according to evaluations of risk reduction.  Lowering Severity is the highest priority, followed by reducing frequency of Occurrence, and improving Detection of occurrences.

O – Responsibility
     Identify the individual who will be responsible for executing the Recommended Action, providing status reports, etc.  More than one individual can be named, but this should be rare; the first individual named is the leader of the effort and is ultimately responsible for execution.  Responsibility should never be assigned to a department, ad hoc team, or other amorphous group.
 
P – Target Completion Date
     Assign a due date for Recommended Actions to be completed.  Recording dates in a separate column facilitates sorting for purposes of status updates, etc.
Q – Actions Taken
     Describe the Actions Taken to improve the design or controls and lower the design’s inherent risk.  These may differ from the Recommended Actions; initial ideas are not fully developed and may require adjustment and adaptation to successfully implement.  Due to limited space in the form, entering a reference to an external document that details the Actions Taken is acceptable.  The document should be identified with the FMEA No. and maintained as an addendum to the DFMEA.

R – Effective Date
     Document the date that the Actions Taken were complete, or fully integrated into the design.  Recording dates in a separate column facilitates FMEA maintenance, discussed in the next section.

S – Severity (S) – Predicted
     Rank each Effect of Failure as it is predicted to effect the customer after the Recommended Actions are fully implemented.  Severity rankings rarely change; it is more common for a design change to eliminate a Failure Mode or Effect of Failure.  In such case, the DFMEA is revised, excluding the Failure Mode or Effect from further analysis.  Alternatively, the entry is retained, with S lowered to 1, to maintain a historical record of development.

T – Occurrence (O) – Predicted
     Estimate the frequency of each Failure Mode’s Occurrence after Recommended Actions are fully implemented.  Depending on the nature of the Action Taken, O may or may not change.

U – Detection (D) – Predicted
     Rank the predicted effectiveness of all Design Detection Controls after Recommended Actions are fully implemented.  Depending on the nature of the Action Taken, D may or may not change.
     * Predictions of S, O, and D must be derived from the same scales as the original rankings.

V – Risk Priority Number (RPN) – Predicted
     Calculate the predicted RPN after Recommended Actions are fully implemented.  This number can be used to evaluate the relative effectiveness of Recommended Actions.
 
Review and Maintenance of the DFMEA
     Review of the DFMEA is required throughout the product development process.  As Recommended Actions are implemented, the results must be evaluated and compared to predictions and expectations.
     Performance of each Item or Function effected by Actions Taken must be reevaluated and new S, O, and D rankings assigned as needed.  Simply assuming that the predicted values have been achieved is not sufficient; the effectiveness of the Actions Taken must be confirmed.
     Once the effectiveness of Actions Taken has been evaluated, information from the Action Results section can be transferred to the body of the DFMEA.  Descriptions in the relevant columns are updated to reflect design modifications and new rankings replace the old.  New Recommended Actions can also be added to further improve the design’s risk profile.  The next round of improvements are prioritized according to the updated rankings and Recommended Actions.
     Maintenance of the DFMEA is the practice of systematically reviewing, updating, and reprioritizing activities as described above.  The DFMEA will undergo many rapid maintenance cycles during product development.  Once the design is approved for production, DFMEA maintenance activity tends to decline sharply; it should not, however, cease, as long as the product remains viable.  Periodic reviews should take place throughout the product lifecycle to evaluate the potential of new technologies, incorporate process and field data collected, and apply any knowledge gained since the product’s introduction.  Any design change must also initiate a review of the DFMEA sections pertaining to Items or Functions effected by the revision.
 
     The DFMEA and all related documentation – diagrams and other addenda – remain valid and, therefore, require maintenance for the life of the product.  It serves as input to the Process FMEA, other subsequent analyses, Quality documentation, and even marketing materials.  The DFMEA is an important component in the documentation chain of a successful product.  The investment made in a thoughtful analysis is returned manyfold during the lifecycles of the product, its derivatives, and any similar or related products.
 
     For additional guidance or assistance with Operations challenges, feel free to leave a comment, contact JayWink Solutions, or schedule an appointment.
 
     For a directory of “FMEA” volumes on “The Third Degree,” see Vol. I:  Introduction to Failure Modes and Effects Analysis.
 
References
[Link] “Potential Failure Mode and Effects Analysis,” 4ed. Automotive Industry Action Group, 2008.
[Link] Product Design.  Kevin Otto and Kristin Wood.  Prentice Hall, 2001.
[Link] Creating Quality.  William J. Kolarik; McGraw-Hill, Inc., 1995. 
[Link] The Six Sigma Memory Jogger II.  Michael Brassard, Lynda Finn, Dana Ginn, Diane Ritter; GOAL/QPC, 2002.
[Link] “FMEA Handbook Version 4.2.”  Ford Motor Company, 2011.

 
Jody W. Phelps, MSc, PMP®, MBA
Principal Consultant
JayWink Solutions, LLC
jody@jaywink.com
]]>
<![CDATA[FMEA – Vol. II:  Preparing for Analysis]]>Wed, 06 Apr 2022 14:30:00 GMThttp://jaywinksolutions.com/thethirddegree/fmea-vol-ii-preparing-for-analysis     Prior to conducting a Failure Modes and Effects Analysis (FMEA), several decisions must be made.  The scope and approach of analysis must be defined, as well as the individuals who will conduct the analysis and what expertise each is expected to contribute.
     Information-gathering and planning are critical elements of successful FMEA.  Adequate preparation reduces the time and effort required to conduct a thorough FMEA, thereby reducing lifecycle costs, as discussed in Vol. I.  Anything worth doing is worth doing well.  In an appropriate context, conducting an FMEA is worth doing; plan accordingly.
     To verify that there is an appropriate context for analysis, consider the three basic FMEA use cases:
1) Analysis of a new technology, service, process, or product design.
2) Analysis of modifications to an existing product, service, or process.
3) Analysis of an existing product, service, process, or technology placed in service in a new environment or application or with new or modified requirements.
     Each use case also defines a generalized scope of analysis.  In use case 1, the scope includes the entire product, service, process, or technology in development.  For use case 2, analysis is limited to the modifications and new characteristics, interactions, or interfaces created by them.  Analysis in use case 3 focuses on interactions with a new environment, requirements created by a new application, or other influences of new operating parameters.
     A detailed definition of scope should be drafted during analysis planning.  The product, process, etc., modifications, or new operating parameters to be reviewed should be clearly defined to ensure a proper analysis.
     Clearly defining the scope accelerates the identification of requisite expertise to conduct the FMEA.  This, in turn, expedites the identification of individuals to be invited to join a cross-functional analysis team.  The team may consist of “core” members and “extended” team members.  Core members, including the engineer or project manager with overall responsibility for the FMEA, participate in all analysis activities.  Extended team members, in contrast, join the discussions for portions of the analysis to which their specialized knowledge and experience is particularly helpful; they are then dismissed to their regular duties.  The core team members are “full-timers,” while the extended team members are “part-timers” with respect to the FMEA.
     Examples of the roles or departments that team members may be recruited – or conscripted! – from are shown in Exhibit 1.  Team members should be individuals, identified by name, membership type (e.g. core or extended), and expertise.  Individuals must be accountable for the analysis; responsibilities assigned to departments are too easily shirked!
     Additional guidance on the selection of team members can be found in Making Decisions – Vol. IV:  Fundamentals of Group Decision-Making.  Adapting the models presented in the section titled “Who should be in the decision-making group?” to the FMEA context can help build an efficient analysis team.
     Once the team has been convened, its collective expertise can be applied to the remaining preparation steps, the first of which is to identify customers.  AIAG has identified four major customers that must be considered throughout the analysis:
1) Suppliers – design decisions, process requirements, and quality standards effect suppliers’ ability to reliably deliver compliant components and services.
2) OEM manufacturing and assembly plants – the confluence of all components, functions, and interfaces that must be seamlessly compiled into an end product.
3) Regulators – failing to comply with regulatory requirements could doom any product or service offering or the provider’s operations.
4) End users – the individuals or organizations that will directly engage with a product or service.
     Considering each of these customers will lead to a more comprehensive and accurate inventory of functional requirements, failure modes, and effects.  A thorough analysis is required to develop effective controls, corrective actions, and performance predictions.
 
     Functional presentations of the information gathered during preparations are required for it to add value to the analysis.  Block diagrams, parameter (P) diagrams, schematics, bills of materials (BOMs), and interface diagrams are all valid presentation tools that support FMEA.
     Block diagrams can be developed in several formats; two examples are shown in Exhibit 2.  The purpose of a block diagram is to provide a graphical representation of the interactions of components within the scope of analysis.  As shown in the generic black box model in Exhibit 3, interactions may include transfers of material, energy, or information; an example application is shown in Exhibit 4.
     The block diagram shown in Exhibit 5 is the result of expanding the black box model of Exhibit 4 to a level of detail that can be transferred to a Design FMEA (DFMEA).  The expected magnitude of interactions – force, torque, current, etc. – and types of information to be transmitted are the functional requirements recorded in the FMEA.
     A parameter diagram, or P-Diagram, can be used to compile information describing the controlled and uncontrolled inputs to a design and its desired and undesired outputs.  This will identify control factors, noise, and “error states,” or failure modes, that can be imported into a DFMEA.  See Exhibit 6 for an example P-Diagram.
     Any information source available can be used to support an FMEA, including other analyses that have been conducted independently.  For example, design for manufacturability, assembly, service, etc. (DFX) studies, ergonomic evaluations, and reliability data from similar products will contain information relevant to the DFMEA.
     Prior to conducting a Process FMEA (PFMEA), a detailed Process Flow Diagram (PFD) should be created, covering the entire scope of analysis.  It is often at this stage that the analysis team realizes that the previously chosen scope of analysis is too broad, poorly defined, or otherwise ill-suited to an efficient PFMEA process.  Adjusting the scope and supporting documentation at this stage is much less costly than a poorly-executed PFMEA.
     Process Flow Diagrams can also be created in various formats, ranging from a simple flow chart (See Commercial Cartography – Vol. II:  Flow Charts) to a detailed input-process-output model.  An example PFD is shown in Exhibit 7.  A D•I•P•O•D Process Model can be used to supplement a simple flow chart or to populate a more-detailed diagram.  The DFMEA, historical data from similar products or processes, and other analyses should also be used in support of a PFMEA process.
 
     Preparation for Failure Modes and Effects Analysis need not be limited to the activities and tools discussed here.  Anything that supports a thorough and accurate analysis should be employed.  The broader range of reliable information used, the more successful the analysis, as can be judged by the flattened lifecycle cost curve shown in Vol. I.
 
     For additional guidance or assistance with Operations challenges, feel free to leave a comment, contact JayWink Solutions, or schedule an appointment.
 
     For a directory of “FMEA” volumes on “The Third Degree,” see Vol. I:  Introduction to Failure Modes and Effects Analysis.
 
References
[Link] “Potential Failure Mode and Effects Analysis,” 4ed. Automotive Industry Action Group, 2008.
[Link] Product Design.  Kevin Otto and Kristin Wood.  Prentice Hall, 2001.

 
Jody W. Phelps, MSc, PMP®, MBA
Principal Consultant
JayWink Solutions, LLC
jody@jaywink.com
]]>
<![CDATA[FMEA – Vol I:  Introduction to Failure Modes and Effects Analysis]]>Wed, 23 Mar 2022 14:30:00 GMThttp://jaywinksolutions.com/thethirddegree/fmea-vol-i-introduction-to-failure-modes-and-effects-analysis     Failure Modes and Effects Analysis (FMEA) is most commonly used in product design and manufacturing contexts.  However, it can also be helpful in other applications, such as administrative functions and service delivery.  Each application context may require refinement of definitions and rating scales to provide maximum clarity, but the fundamentals remain the same.
     Several standards have been published defining the structure and content of Failure Modes and Effects Analyses (FMEAs).  Within these standards, there are often alternate formats presented for portions of the FMEA form; these may also change with subsequent revisions of each standard.
     Add to this variety the diversity of industry and customer-specific requirements.  Those unbeholden to an industry-specific standard are free to adapt features of several to create a unique form for their own purposes.  The freedom to customize results in a virtually limitless number of potential variants.
     Few potential FMEA variants are likely to have broad appeal, even among those unrestricted by customer requirements.  This series aims to highlight the most practical formats available, encouraging a level of consistency among practitioners that maintains Failure Modes and Effects Analysis as a portable skill.  Total conformity is not the goal; presenting perceived best practices is.
     Automotive Industry Action Group (AIAG) and Verband der Automobilindustrie (VDA) have begun consolidation by jointly publishing the “FMEA Handbook” in 2019.  This publication aligns North American and German automotive industry practice in a step towards global harmonization of FMEA requirements.  In this series, we will attempt to distill the new handbook into user-friendly guides for both new and transitioning practitioners.
     To ensure clarity of the inevitable references and comparisons to previous FMEA guidelines, we will review “classical” FMEA before discussing the new “aligned” standard in detail.  Implementation of FMEA, in North America at least, has been driven primarily by the automotive industry.  That influence will be evident here, as the presentation of classical FMEA is derived, in large part, from previous editions of the AIAG FMEA Handbook.

Costs of FMEA Implementation
     The financial impact of conducting an FMEA is difficult to accurately predict.  It is dependent upon the product and processes involved and the management styles of those responsible for overseeing them.  Likewise, only the initial cost of not conducting and FMEA – zero – can be known in advance.
     We do know, however, at least qualitatively, how the cost of conducting FMEA compares to foregoing analysis.  To be more precise, we can compare the lifecycle cost of a product with a well-executed FMEA to one with poorly-executed or eschewed analysis.  This comparison is depicted in Exhibit 1; the green line represents the cost curve of a hypothetical product lifecycle when FMEA are well-executed.  The red line represents a comparable product lifecycle without well-executed FMEA.

     As can be seen in Exhibit 1, there are costs incurred in the product and process development stages when FMEA is conducted in earnest.  This early investment flattens the curve in later stages, however.  In contrast, the cost benefit of neglecting FMEA in the early stages is eclipsed by the costs incurred as issues are discovered during launch.  The lack of proactive analysis continues to incur additional costs throughout production and beyond; even after a product is retired, lingering liabilities may continue to plague the manufacturer.  In the well-executed case, lessons learned may be applied to new programs, effectively lowering the cost of the FMEA effort.
     Note that the “neglecting FMEA” scenario is shown to have non-zero cost in the development stages; even “box-checking exercises” incur some cost.  Also, an alternative way to conceive of lowering the cost of FMEA may be more intuitive for some readers:  applying lessons learned to future programs increases the return on the investment made in FMEA implementation.
     When greater effort is expended on proactive FMEA, the lifecycle cost of a product can be estimated earlier in the cycle and with greater reliability.  Without proactive analysis, the total cost of a program will not be known until the product is retired and all outstanding issues are resolved.
     Proper FMEA implementation can be threatened by the flawed logic that endangers all proactive efforts.  In a cost-cutting frenzy, an unenlightened manager may divert resources from proactive FMEA efforts.  This failure to recognize the benefits will shift the subject program from the green line to the red line in Exhibit 1, increasing total cost.  As proactive efforts – FMEA or others – become more effective, the need for them becomes less salient to many decision-makers.  Practitioners can counter this by making reference to these efforts and quantifying, to the extent possible, the costs avoided throughout the product lifecycle as a result of the commitment to early and ongoing analysis.
 
Other Reasons for FMEA
     Direct costs of neglecting to conduct a proper FMEA were discussed first for one simple reason:  direct costs get the most attention.  Now that I have yours, there are other reasons to consider investing in proactive analysis.  Ultimately, each can be associated with costs, but the links are not always obvious.  These indirect costs, however, can also be “make or break” factors for a product or company.
     Customer satisfaction or, more broadly, customer experience, is determined, in large part, in the product development stage.  Launching a product without incorporating “the voice of the customer” or the end-user perspective can lead to various perceived failures.  Customer experience deficiencies exist in addition to “traditional” failures, such as physical damage or product malfunction.  Issues ranging from minor inconvenience or dissatisfaction to physical injury and property damage can be prevented with proper FMEA implementation.
     Failures that cause physical injury or property damage can result in extensive legal and financial liabilities.  The great expense of conducting a product recall is only the beginning of such a nightmare scenario.  The ability to present a considered evaluation of product risks and evidence of mitigation efforts can reduce awarded damages and other claims against a manufacturer.
     Analysis in the process development stage can lead to improvements for both customers and the producer.  Reducing process variation generates increased customer satisfaction by providing a more consistent product.  Producer costs are simultaneously reduced by minimizing scrap, rework, and testing.
     Other producer benefits may include the identification of potential productivity and ergonomic improvements.  “Lean wastes,” such as excessive handling and transportation, or overprocessing may also be identified as causes of reduced product performance.  Eliminating these issues prior to series production improves quality and customer satisfaction while increasing efficiency.
     Proactive analysis facilitates maintaining a project schedule with a smoother launch and target market introduction dates achieved.  The costs of neglecting analysis in early stages of the product lifecycle cannot be recovered.  Future performance can be improved, but recovering from a damaged reputation may be far more difficult.  Costs will continue to accrue as discounting or other promotional means may be necessary to regain a position in the market.
     Other benefits may also accrue.  The value of generating a deeper understanding of a product or process is difficult to quantify.  The value of developing confidence among executives in a product’s potential, however, is often quantified in budgets and salaries.  Financial or otherwise, proactive analysis pays dividends.
 

     The FMEA series will cover a broad range of related topics.  If there is a specific topic you would like to see covered, or question answered, feel free to send suggestions.
 
     For additional guidance or assistance with Operations challenges, feel free to leave a comment, contact JayWink Solutions, or schedule an appointment.
 
Jody W. Phelps, MSc, PMP®, MBA
Principal Consultant
JayWink Solutions, LLC
jody@jaywink.com
 
Directory of “FMEA” entries on “The Third Degree.”
Vol. I:  Introduction to Failure Modes and Effects Analysis (23Mar2022)
Vol. II:  Preparing for Analysis (6Apr2022)
Vol. III:  “Classical” Design Failure Modes and Effects Analysis (20Apr2022)
Vol. IV:  “Classical” Process Failure Modes and Effects Analysis (4May2022)
Vol. V:  Alignment (18May2022)
Vol. VI:  "Aligned" Design Failure Modes and Effects Analysis (1Jun2022)
Vol. VII:  "Aligned" Process Failure Modes and Effects Analysis (15Jun2022)
Vol. VIII:  Monitoring and System Response (29Jun2022)
Vol. IX:  Visual Action Priority Tables (13Jul2022)
Vol. X:  Suggested Formats for “Aligned” FMEA Forms (27Jul2022)
Vol. XI:  Commentary, Critique, and Caveats (10Aug2022)

Related post:  “P” is for “Process” (FMEA) (27Jun2018)
]]>
<![CDATA[Managerial Schizophrenia and Workplace Cancel Culture]]>Wed, 09 Mar 2022 15:30:00 GMThttp://jaywinksolutions.com/thethirddegree/managerial-schizophrenia-and-workplace-cancel-culture     Destructive behaviors existed in organizations long before they were given special names.  The term “cancel culture” is not typically associated with business environments, but its pernicious effects are prevalent.  Unlike a boycott, cancel culture destroys an organization from within, through covert and fraudulent actions.
     Cancel culture effects all levels of an organization, but “managerial schizophrenia” is a common precursor and potent ingredient.  Adverse behaviors signal abandonment of cultural and professional norms, the subsequent failures of collaboration, and the resultant degradation in group performance.  Combatting these intertwined organizational cancers requires commitment from all levels of management and revised methods of oversight.
Workplace Cancel Culture
     Cancel culture, as a concept, is most commonly associated with public figures, such as politicians, “journalists” and pundits, comedians, and podcasters.  However, it has also existed in many workplaces, unnamed, far longer than it has been the subject of public debate.  The attempted “cancellation” of a public figure gets a lot of attention, as purveyors of all forms of media use it to their advantage.  Workplace cancellations are far more damaging – to individuals and organizations – and more common, yet receive little, if any, attention from anyone not directly affected.
     Dictionary.com defines cancel culture as “the phenomenon or practice of publicly rejecting, boycotting, or ending support for particular people or groups because of their socially or morally unacceptable views or actions.”  This definition is rather generous, as overwrought public outrage is often due to a mere difference of opinion and is not genuine, but purposeful.  The public drama is leveraged to suppress or discredit differing, though often legitimate, viewpoints.
     The generosity of dictionary.com and the lack of reference to professional pursuits renders the generic definition of “cancel culture” unsatisfying for our discussion of workplace issues.  To remedy its shortcomings, the following definition is proposed:

Workplace Cancel Culture – the phenomenon or practice of resisting or misrepresenting the efforts, results, or viewpoint of a colleague in order to achieve or maintain a favorable position in the organization.

     “Office politics,” another longstanding malady of the workplace, involves currying favor with executives and other attempts by individuals to complete a zero-sum “game” on the positive side of the ledger.  Such zero-sum games include the apportionment of bonuses, promotions, plush assignments, larger offices, and the like.
     Workplace cancel culture goes beyond office politics; rather than simply trying to advance themselves, individuals begin to attack others, attempting to damage the reputations and future prospects of perceived rivals.  The logic seems to be this:  if person A is perceived as less competent or effective than previously thought, person B will be perceived as relatively more competent or effective, irrespective of person B’s actual performance.  If discrediting person A is easier for person B to achieve than is improving his/her own performance, this is the route person B chooses.  This logic is, of course, terribly flawed; these individuals are playing a zero-sum game when none exists!
     This flawed logic is applied in various situations at and between all levels of organizations.  Peers may be competing for a promotion, transfer, or simply a higher status within a stable group.  A promotion-seeker may also attempt to create a vacancy by discrediting the incumbent of the position s/he desires.  Conversely, a manager may protect his/her own career trajectory by interfering with that of a promising subordinate.
     To do this, an ethically-challenged manager has several mechanisms available to him/her.  Misrepresenting a subordinate’s performance or “cultural fit” to other managers and influential people establishes justification for his/her eventual dismissal, eliminating the threat to the manager’s progression within the organization or other aspirations.  Terminating an employee is the ultimate form of “deplatforming,” to use the current vernacular, in the workplace context.
     Subjecting an individual to unreasonable expectations or standards of performance lends credibility to the manager’s reports of “unsatisfactory performance.”  An "accurate" report of “failed to meet expectations” obfuscates the reality of the situation; willfully unaware decision-makers will support the corrupt manager’s chosen course of action.
     If a modicum of subtlety is desired, a manager that feels threatened by a high-performing subordinate may fabricate justifications for the denial of raises, bonuses, high-visibility assignments, or other perks.  Such actions are often less salient to upper management while encouraging high-performers to leave the organization.  Credit for an individual’s results may also be shifted to another, less threatening, individual, further limiting the high-performer’s prospects and increasing the incentive to seek opportunities in a higher-integrity environment.
     Once workplace cancel culture is allowed to develop in an organization, it creates a vicious cycle in which only those who specialize in cancel culture and office politics can thrive.  High-performing, inquisitive individuals that reject the status quo and “we’ve always done it this way” mentality are driven out.  How this happens and what must be done to counter it will be discussed further in a later section.
 
Managerial Schizophrenia
     According to dictionary.com, schizophrenia is “a state characterized by the coexistence of contradictory or incompatible elements.”  Managerial schizophrenia refers to the coexistence of contradictory or incompatible elements in the context of a hierarchical organizational structure; it is akin to cognitive dissonance.  Schizophrenic managers espouse inclusion, respect, creativity, meritocracy, and earnest communication while acting in contradiction to these ideals.
     Managers’ schizophrenia can vary by degree; in its mildest form, a manager condones the deviant behaviors of others without actively participating oneself.  The passive form is unlikely to persist; untreated cancers become more malignant.
     Schizophrenic managers often exhibit behaviors that signify his/her social dominance orientation (SDO) – the belief that his/her position is evidence of his/her superiority or entitlement – such as ostracism, isolation, or other mistreatment of colleagues.  Unconsidered rejection of novel ideas and obfuscation, withholding, or distortion of information are tools of self-protection in common use that discourage creativity, diminish respect, and erode trust.
     When schizophrenic managers flourish, it is a harbinger of a developing cancel culture within the organization.  This is so because managerial schizophrenia is a potent ingredient of workplace cancel culture and a self-feeding monster with no other development path available to it.
 
How Workplace Cancel Culture Develops
     The simplest explanation of the growth of workplace cancel culture is “monkey see, monkey do.”  As subordinates recognize the actions that have propelled their managers’ careers, they identify these destructive behaviors as keys to their own success and longevity.  The focus then shifts from collaboration, learning, and performance to ingratiation to superiors (the “coattails” method of advancement) and sabotage of “competitors,” a.k.a. peers.
     As broken windows theory tells us, ignoring bad behavior encourages its escalation.  A corrupt manager’s position and lack of intervention by upper management reinforces his/her belief that his/her decisions are sound and actions and behaviors are appropriate.  This is the basis for motivated reasoning, wherein information is reframed or selectively accepted to maintain consistency between one’s decisions and beliefs.  In other words, decisions that support the decision-maker’s beliefs or desired outcomes are rationalized, or justified, by shaping information into a narrative that reinforces the decision.
     Over time, the aberrant behavior becomes easier to mask, justify, and perpetrate on the unsuspecting.  As free-thinkers and high-performers are driven out, the organization becomes more aligned, more schizophrenic.  Thus, the vicious cycle repeats and the monster continues to feed.
 
     The preceding discussions of managerial schizophrenia and workplace cancel culture have overlapped.  As mentioned at the outset, they are intertwined; however, some clarification may be in order.  Managerial schizophrenia is an individual phenomenon; it affects individual managers independent of the behavior of others.  When it spreads to the degree of a septic infection, managerial schizophrenia becomes a powerful building block of workplace cancel culture.
     Workplace cancel culture is a more generalized term.  It implies the existence of managerial schizophrenia as well as other aberrant behaviors.  These may include accepting an amount of credit or blame that is uncommensurate with one’s efforts or results.  That is, taking more credit than deserved for a favorable outcome, or blaming others for underperformance.
     Accusations of inappropriate behavior are common tools of cancellers.  Even unsubstantiated claims of impropriety can irreparably harm a person’s reputation; it is one of the most treacherous tactics that can be employed.  A lack of repercussions for false claims is a clear sign that a dangerous, toxic culture has metastasized in an organization.
     In summary, any tactic or scheme that a devious mind could fabricate to suppress or silence opposing viewpoints, curry favor with superiors, damage the credibility of potential challengers, or in any way elevate oneself at the expense of others or the organization that is tolerated by the organization’s leadership falls under the umbrella of workplace cancel culture.
 
What to Do About Workplace Cancel Culture
     Any discussion about how to counter managerial schizophrenia and workplace cancel culture should center on three key points:
  1. For workplace cancel culture to be eliminated, managerial schizophrenia must be eradicated.
  2. It is not unethical to pursue advancement, but how one pursues it is of great import.
  3. It is much easier to develop a culture than it is to change one.
Point (1) encapsulates much of the previous discussion and, therefore, requires no additional comment here.  Point (2) should be obvious, though it bears repeating.
     Point (3) is well-known, but frequently ignored by leaders to the great detriment of their organizations and the individuals within.  A desired culture should be actively developed; otherwise, neglect will allow one, usually far less desirable, to develop on its own.  An organization’s strongest personalities will define the culture; when coupled with weak characters, the negative elements are bound to prevail.
     Preventing workplace cancel culture from taking root requires the active participation of all levels of management.  The CEO must set standards and expectations that filter down through the organization; intensive follow-up is required to ensure that standards of conduct are upheld with consistency.
     Credit must be accurately apportioned and performance appropriately rewarded.  Whether on a project-by-project basis, in an annual review, or other assessment scheme, validation mechanisms should be in place to ensure that an employee’s reported performance reflects a fair assessment.  For example, input should be sought from those with whom the person interacts regularly to get a complete picture of the person’s contribution; a person’s “direct supervisor” is often least aware of the person’s true performance or value to the organization.  A well-executed evaluation process minimizes “coattailing” and self-serving cancellations, while helping to retain high-potential employees such as creative thinkers and innovators.
     Leaders should require evidence of wrongdoing or inappropriate behavior, such as harassment, before taking action against an accused employee.  The motivations of the accuser should also be explored to eliminate personal gains through false claims.  Likewise, cases of “stolen merit” – taking undeserved credit to advantage oneself over a colleague – must be dealt with firmly to maintain trust in the organization and its leadership.  To limit these cases, leaders should vehemently, repeatedly, debunk the zero-sum fallacy – the belief that the success of another diminishes one’s own position or value.
     Finally, the choice of supervisors, managers, and leaders determines an organization’s ability to sustain a healthy culture.  Every member of an organization has a responsibility to perform his/her duties ethically, to act with integrity.  The burden increases, however, as one rises in the organization, gaining authority and responsibility; most importantly, responsibility for the fair treatment of subordinates or “direct reports.”
     As discussed in Sustainability Begins with Succession Planning, a number of questions should be answered to determine a person’s suitability to a position of authority, such as “Does this person set a desirable example of attitude, teamwork, etc.?”  A candidate’s technical ability is usually much easier to assess than his/her character.  For example, does the candidate exhibit a social dominance orientation or does s/he embrace servant leadership?  If the answer to a single question can predict an individual’s performance in a leadership role and, by extension, the success of the entire organization, it is probably this one.
 
     Managerial schizophrenia and workplace cancel culture are manifestations of a principal-agent problem.  The interests of the individual (agent) are in conflict with those of the organization (principal); the agent is expected to act in the best interest of the principal, but is incentivized to act in his/her own best interest instead.  Some will dismiss it as “human nature” or “survival instinct,” but justifications make the behaviors no less destructive of organizations or individuals.
     An organization that allows a toxic culture to persist is like a tree that rots from the center.  Despite the decay in its core, the tree’s bark looks surprisingly healthy, even as it falls through the roof of your house.
 
     For additional guidance or assistance with Operations challenges, feel free to leave a comment, contact JayWink Solutions, or schedule an appointment.
 
References
[Link] “On Protoplasmic Inertia.”  Gary S. Vasilash.  Automotive Design and Production, July 2002.
[Link] “When and why leaders put themselves first:  Leader behavior in resource allocations as a function of feeling entitled.”  David de Cremer and Eric van Dijk.  European Journal of Social Psychology, January 2005.
[Link] “Being Ethical is Profitable.”  Alankar Karpe.  ProjectManagement.com, August 3, 2015.
[Link] “How Workplace Fairness Affects Employee Commitment.”  Matthias Seifert, Joel Brockner, Emily C. Bianchi, and Henry Moon.  MIT Sloan Management Review, November 5, 2015.
[Link] “The Unwitting Accomplice:  How Organizations Enable Motivated Reasoning and Self-Serving Behavior.”  Laura J. Noval and Morela Hernandez.  Journal of Business Ethics, September 21, 2017.
[Link] “Putting an End to Leaders’ Self-Serving Behavior.”  Morela Hernandez.  MIT Sloan Management Review, October 18, 2017.
[Link] “Why People Believe in Their Leaders – or Not.”  Daniel Han Ming Chng, Tae-Yeol Kim, Brad Gilbreath, and Lynne Andersson.  MIT Sloan Management Review, August 17, 2018.
[Link] “New Ways to Gauge Talent and Potential.”  Josh Bersin and Tomas Chamorro-Premuzic.  MIT Sloan Management Review, November 16, 2018.
[Link] “Does your company suffer from broken culture syndrome?”  Douglas Ready.  MIT Sloan Management Review, January 10, 2022.

 
Jody W. Phelps, MSc, PMP®, MBA
Principal Consultant
JayWink Solutions, LLC
jody@jaywink.com
]]>
<![CDATA[Making Decisions – Vol. IX:  Decision Trees]]>Wed, 23 Feb 2022 15:30:00 GMThttp://jaywinksolutions.com/thethirddegree/making-decisions-vol-ix-decision-trees     Thus far, the “Making Decisions” series has presented tools and processes used primarily for prioritization or single selection decisions.  Decision trees, in contrast, can be used to aid strategy decisions by mapping a series of possible events and outcomes.
     Its graphical format allows a decision tree to present a substantial amount of information, while the logical progression of strategy decisions remains clear and easy to follow.  The use of probabilities and monetary values of outcomes provides for a straightforward comparison of strategies.
Constructing a Decision Tree
     In lieu of abstract descriptions and generic procedures, decision tree development will be elucidated by working through examples.  To begin, the simple example posed by Magee (1964) of the decision to host a cocktail party indoors or outdoors, given there is a chance of rain, will be explored.
     In this example, there is one decision with two options (indoors or outdoors) and one chance event with two possible outcomes (rain or no rain, a.k.a. “shine”).  This combination yields four possible results, as shown in the payoff table of Exhibit 1.  It could also be called an “outcome table,” as it only contains qualitative descriptions; use of the term “payoff table” becomes more intuitive with the introduction of monetary values of outcomes.
     While the payoff table is useful for organizing one’s thoughts pertaining to the potential results of a decision, it lacks the intuitive nature of a graphical representation.  For this, the information contained in the payoff table is transferred to a decision tree, as shown in Exhibit 2.
     Read from left to right, a decision tree presents a timeline of events.  In this case, there is the decision (indoors or outdoors), represented by a square, followed by a chance event (rain or shine), represented by a circle, and the anticipated outcome of each combination (“disaster,” “real comfort,” etc.), represented by triangles.  The square and circles are nodes and each triangle is a leaf; each are connected by branches.  A summary of decision tree elements is provided in Exhibit 3.
     The decision tree of Exhibit 2 provides an aesthetically pleasing presentation of the cocktail party host’s dilemma.  However, beyond organizing information, its descriptive nature has done little to assist the host in making the required decision.
 
     The power of the decision tree becomes evident when the financial implications of decisions are presented for analysis and comparison.  To demonstrate this, we modify the cocktail party example to be more than a purely social gathering.  Let’s say it is a fundraising event where attendees’ generosity is directly linked to their satisfaction with the event (i.e. comfort level).

     This hypothetical fundraising scenario results in the payoff table of Exhibit 4 and the decision tree of Exhibit 5 that present anticipated amounts to be raised in each situation.  Clearly, this is an important decision; the collection potential ranges from $30,000 to $100,000.  However, the host is no closer to a rational decision, left to clutch a rabbit’s foot or make the decision with a dartboard.
     What information would help the host decide?  The probability of rain, naturally!  The local meteorologist has forecast a 60% chance of rain during the fundraiser; this information is added to the decision tree, as shown in Exhibit 6.  The best decision is still not obvious, but we are getting close now!
     To compare the relative merits of each option available, we calculate expected values.  The expected value (EV) of an outcome is the product of its anticipated monetary value (payoff) and the probability of its occurrence.  For example, a rainy outdoor event has an anticipated payoff of $30,000 and a 0.60 probability of occurrence.  Therefore, EV = $30,000 x 0.60 = $18,000.  Likewise, the expected value of an outdoor event with no rain is EV = $100,000 x 0.40 = $40,000.  The EV of a chance node is the sum of its branches’ EVs; thus EVout = $18,000 + $40,000 = $58,000 for an outdoor event.  The expected value of an indoor event is calculated in the same way; EVin = $62, 000.
     Expected value calculations are shown to the right of the decision tree in Exhibit 7.  The calculations are not typically displayed; they are included here for instructional purposes.
     The EV of a decision node, or its position value, is equal to that of the preferred branch – the one with the highest payoff or lowest cost (if all are negative).  Hence, the position value of this decision is $62,000 and the event will be held indoors.  Two hash marks are placed through the “outdoors” branch to signify that it has not been selected; this is called “pruning” the branch.  The completed, pruned decision tree is shown in Exhibit 7.
     The process of calculating expected values and pruning branches is performed from right to left.  Reflecting the reversal of direction, this process is called “rolling back” or “folding back” the decision tree, or simply “rollback.”
 
     Use of a decision tree is advantageous for relatively simple situations like the example above.  Its value only increases as the decision environment becomes more complex.  Again, this is demonstrated by expanding the previous example.  In addition to the indoor/outdoor decision, consideration will also be given to a public fundraiser as well as the private, “invitation-only” event previously presented.
     While public events tend to extract smaller individual donations, increased attendance can offset this, though attendees’ generosity remains linked to their comfort.  An expanded payoff table, presented in Exhibit 8, includes fundraising estimates for a public event.  (This format was chosen because a three-dimensional table is graphically challenging; tabular representation of longer decision chains become confusing and impractical.)
     The decision tree, shown in Exhibit 9, now presents two decisions and one chance event.  The upper decision branch consists of the previous decision tree of Exhibit 7, while the lower branch presents the public event information. In this scenario, the private cocktail party is abandoned (its branch is pruned); instead, a public event will be held.  The position value of the root node – the first decision to be made – is equal to the EV of the preferred strategy.  The path along the retained branches, from root node to leaf, defines the preferred strategy.

     Expanding further the fundraising event example provides a more realistic decision-making scenario, where decisions and chance events are interspersed.  This example posits that the fundraiser is being planned for the political campaign of a candidate that has not yet received a nomination.  Pundits have assigned a 50% probability that the candidate will prevail over the primary field to receive the nomination.  Given this information, the campaign manager must decide if an event venue and associated materials should be reserved in advance.  From experience, the campaign manager believes that costs will double if event planning is postponed until after the nomination is secured.
     The decision tree for the pre-nomination reservation decision is presented in Exhibit 10.  The format has been changed slightly from previous examples in order to present some common attributes.  Larger trees, in particular, benefit from reducing the “clutter” created by the presentation of information along the branches.  One way to do this is to align information in “columns,” the headings of which clarifies a more succinct branch label than would otherwise be possible.  In this example, the headings provide a series of questions to which “yes” or “no” responses provide all the information required to understand the tree.  Reading the tree is easier when it is not crowded by the larger labels of “indoors,” “private,” “nominated,” etc.
     The upper right portion of Exhibit 10 contains the same tree as Exhibit 9, though it has been rearranged.  The tree in Exhibit 10 is arranged such that the topmost branches represent “yes” responses to the heading questions.  While this is not required, many find decision trees organized this way easier to read and follow the logic of the chosen strategy.
     The example of Exhibit 9 utilized a single monetary value, the total funds raised, to make decisions.  It could be assumed that this is a net value (expenses had been deducted prior to constructing the tree) or that expenses are negligible.  In this regard, Exhibit 10, again, presents a more realistic scenario where expenses are significant and worthy of explicit analysis.  The cost of hosting each type of event is presented in its own column and requires an additional calculation during rollback.  The position value of the decision node is no longer equal to the EV of the subsequent chance node as in previous examples.  To determine the position value of the indoors/outdoors decision, the cost of the preferred venue must be deducted from the EV of its branch.
     A noteworthy characteristic of this example is the atypical interdependence of the value of a branch.  Notice that the cost of the “no event” branch following a decision to reserve a venue is $16,000.  This cost was determined by first rolling back the upper portion of the tree.  The cost of the preferred branch in the “nominated” scenario is $16,000; therefore, this is the cost to be incurred, whether or not an event is held, because it follows the decision to reserve a venue.  The cost of the preferred venue is simply duplicated on the “no event” branch.  It is important not to overlook this cost; it reduces the EV of the “Nominated?” chance node by $8,000 ($16,000 cost x 0.50 probability).
     Another interesting characteristic of the campaign fundraiser decision, as seen in Exhibit 10, is that the position value of the reservation decision is the same, regardless of the decision made.  The example was not intentionally designed to achieve this result; values and probabilities were chosen arbitrarily.  It is a fortunate coincidence, however, as it provides additional realism to the scenario and allows us to explore more fully the role of this, or any, decision-making aid.
     An inexperienced decision-maker may, upon seeing equal EVs at the root decision, throw up his/her hands in frustration, assuming that the effort to construct the decision tree was wasted.  This, of course, is not true; you may have realized this already.  A theme running throughout the “Making Decisions” series is that decision-makers are aided, not supplanted, by the tools presented.  Expected value is only one possible decision criterion; further analysis is needed.
     Notice that the “no” branch of the “Reserve?” decision has been pruned in Exhibit 10.  Why? A thorough answer requires a discussion of risk attitudes, introduced in the next section.  For now, we will approach it by posing the following question:  Given the expected value of a reservation is equal to that of no reservation, is it advisable to make the reservation anyway?  Consider a few reasons why it is:
  • Demonstrated optimism in the candidate’s eventual nomination may provide a psychological edge to the campaign.
  • A secured reservation reduces the campaign staff’s workload and time pressure post-nomination, should it be received.
  • A secured reservation reduces uncertainty.  For example, the cost of securing a venue “at the last minute” may be underestimated as circumstances evolve.
  • A secured reservation may provide the candidate leverage should the nomination not be received.  The eventual nominee may be less prepared to host an event of his/her own, for example.
  • A refund, partial or full, may be negotiated, should the event be cancelled, raising the EV of the reservation.
      The points above include noneconomic, or nonmonetary, factors that are relevant to the decision.  With experience, decision-makers become more aware of noneconomic factors that influence an organization’s success and more capable of effectively incorporating them in decision strategies.
 
Advanced Topics in Decision Tree Analysis
     The discussion of decision tree analysis could be expanded far beyond the scope of this post.  Only experienced practitioners should attempt to incorporate many of the advanced elements, lest the analysis go awry.  This section serves to expose readers to opportunities for future development; detailed discussions may appear in future installments of “The Third Degree.”  Eager readers can also consult references cited below or conduct independent research on topics of interest.
 
     Risk attitudes, mentioned in the previous section, influence decisions when analysis extends beyond the decision tree.  The strategy described in the example – reserving a venue for an indoor public event (see Exhibit 10) – is a risk averse strategy; it seeks to avoid the risks associated with foregoing a reservation.  Some of these risks were mentioned in the justification for the equal-EV decision presented and include economic and noneconomic factors.
     If the campaign manager has a risk seeking attitude, s/he is likely to reserve an outdoor venue for a public event, despite the lower expected value of such a decision.  A risk seeking decision-maker will pursue the highest possible payoff, irrespective of its expected value or the risks involved.  In the example, this corresponds to the $140,000 of contributions anticipated at a public outdoor event.
     Risk neutral decision-makers accept the strategy with the highest expected value without further consideration of the risks involved.  This option does not exist in our example; further consideration was required to differentiate between two strategies with equal expected values.
 
     If a decision-maker is accustomed to using utility functions, expected values can be replaced with expected utilities (EU) for purposes of comparing strategies.  A concept related to both risk attitude and expected utility is the certainty equivalent (CE).  A decision’s CE is the “price” for which a decision-maker would transfer the opportunity represented by the decision to another party, foregoing the upside potential to avoid the downside risk.
 
     The examples presented have assumed a short time horizon; the time that elapses between decisions and/or chance events is not sufficient to influence the strategy decision.  Decisions made on longer time horizons are influenced by the time value of money (TVM).  The concept of TVM is summarized in the following way:  an amount of money received today is worth more than the same amount received on any future date.  Thus, expected values change over long time horizons; the extent of this change may be sufficient to alter the preferred strategy.
 
     There are numerous software tools available to construct decision trees; many are available online and some are free.  The output of some will closely resemble the decision trees presented here, using conventional symbols and layout.  Others differ and may be more difficult to read and analyze.  While software-generated decision trees are often aesthetically pleasing, suitable for presentation, those drawn freehand while discussing alternatives with colleagues may be the most useful.
     Use of the venerable spreadsheet is also a viable option.  Generating a graphical presentation of a decision tree in a spreadsheet can be tedious, but it offers advantages unavailable in other tools.  First and foremost, the ubiquity of spreadsheets ensures a high probability that anyone choosing to engage in decision tree analysis already has access to and familiarity with at least one spreadsheet program.  With sufficient experience, one can forego the tedium of generating graphics until a formal presentation is required.  Such a spreadsheet might look like that in Exhibit 11, which shows the campaign fundraiser example in a non-graphical format.  Each branch of the decision tree is shown between horizontal lines; pruned branches are shaded.  Expected values of chance nodes are labeled “EV” and those of decision nodes are labeled “position value” to differentiate them at a glance without use of graphic symbols.
     Calculations and branch selections/pruning can be automated in the spreadsheet.  A significant advantage of the spreadsheet is the ability to quickly perform simple sensitivity analysis.  Several scenarios can be rapidly explored by adjusting payoffs and probabilities and allowing the spreadsheet to recalculate.  More sophisticated analysis tools are also available in most spreadsheet programs; only experienced users should attempt to utilize them for this purpose, however.
 
Decision Tree Tips in Summary
     To conclude this introduction, the final section consists of brief reminders and tips that facilitate effective decision tree analysis.
     The example decision trees presented are symmetrical, but this need not be the case.  Likewise, decisions and chance events need not be binary; three, four, or more options or potential outcomes may be considered.  However, the rule remains:  the potential outcomes of a chance event must be mutually exclusive (only one can occur) and collectively exhaustive (probabilities sum to 1, or 100%).
     The examples presented considered positive position values.  However, EVs and position values could be negative.  For example, there may be multiple options for facility upgrades to maintain regulatory compliance.  None will increase revenue, so all EVs are negative.  In such case, the preferred branch would be the one reflecting the lowest cost.
     All relevant costs and only relevant costs should be included in EV calculations.  Sunk costs and any others that occur regardless of the strategy selected should be excluded.  Taxes and interest charges may be relevant, as are opportunity costs and noneconomic factors.
     Construct the decision tree from left to right; roll back from right to left.  Format it to be easily expanded when new alternatives are identified or new events or decisions need to be included in the analysis.
     To compact a decision tree for presentation, branches can be hidden, allowing the position value of the origin decision to represent the hidden branches.  Also, consider combining branches when events occur together or are otherwise logically coupled.  Branches should only be combined when doing so does not eliminate a viable alternative strategy.
     Reviews of decision trees should take place as events unfold and after the final outcome has occurred.  Adjustments may not be possible to the strategy under review, but estimates of probabilities and payoffs may be improved for future decisions.
 
     Use of decision trees for organizational decision-making is consistent, analytical, and transparent.  However, decision tree analysis exhibits the same weakness as all other decision-making aids – it is dependent upon reliable estimates and sound judgment to be effective.  Review of past decisions and outcomes is critical to increasing the quality of an organization’s decisions.
 
 
     For additional guidance or assistance with decision-making or other Operations challenges, feel free to leave a comment, contact JayWink Solutions, or schedule an appointment with a “strategy arborist.”
 
     For a directory of “Making Decisions” volumes on “The Third Degree,” see Vol. I:  Introduction and Terminology.
 
References
[Link] “Decision Trees for Decision Making.”  John F. Magee.  Harvard Business Review, July, 1964.
[Link] “Decision Tree Primer.”  Craig W. Kirkwood.  Arizona State University, 2002.
[Link] “Decision Tree Analysis:  Choosing by Projecting ‘Expected Outcomes.’”  Mind Tools Content Team.
[Link] “Decision Tree: Definition and Examples.”  Stephanie Glen, September 3, 2015.
[Link] “What is a Decision Tree Diagram.”  LucidChart.
[Link] Mathematical Decision Making:  Predictive Models and Optimization.  Scott P. Stevens.  The Great Courses, 2015.

 
Jody W. Phelps, MSc, PMP®, MBA
Principal Consultant
JayWink Solutions, LLC
jody@jaywink.com
]]>
<![CDATA[Making Decisions – Vol. VIII:  The Pugh Matrix Method]]>Wed, 09 Feb 2022 16:00:00 GMThttp://jaywinksolutions.com/thethirddegree/making-decisions-vol-viii-the-pugh-matrix-method     A Pugh Matrix is a visual aid created during a decision-making process. It presents, in summary form, a comparison of alternatives with respect to critical evaluation criteria.  As is true of other decision-making tools, a Pugh Matrix will not “make the decision for you.”  It will, however, facilitate rapidly narrowing the field of alternatives and focusing attention on the most viable candidates.
     A useful way to conceptualize the Pugh Matrix Method is as an intermediate-level tool, positioned between the structured, but open Rational Model (Vol. II) and the thorough Analytic Hierarchy Process (AHP, Vol. III).  The Pugh Matrix is more conclusive than the former and less complex than the latter.
     Typical practice of “The Third Degree” is to present a tool or topic as a composite, building upon the most useful elements of various sources and variants.  The Pugh Matrix Method is a profound example of this practice; even the title is a composite!  Information about this tool can be found in the literature under various headings, including Pugh Matrix, Decision Matrix, Pugh Method, Pugh Controlled Convergence Method, and Concept Selection.
     This technique was developed in the 1980s by Stuart Pugh, Professor of Design at Strathclyde University in Glasgow, Scotland.  Although originally conceived as a design concept selection technique, the tool is applicable to a much broader range of decision contexts.  It is, therefore, presented here in more-generic terms than in Pugh’s original construction.
 
The Pugh Matrix Method
     A schematic diagram of the Pugh Matrix is presented in Exhibit 1.  To facilitate learning to use the matrix, column and row numbers are referenced [i.e. col(s). yy, row(s) xx] when specific areas are introduced or discussed.  Column and row numbers may differ in practice, as the matrix is expanded or modified to suit a specific decision.  Instructions and guidelines for completing the Pugh Matrix, based on the schematic of Exhibit 1, are given below.
     The first step of any decision-making process is to clearly define the decision to be made.  A description of the situation faced – problem, opportunity, or objective – clarifies the purpose of the exercise for all involved.  This description should include as much detailed information as is necessary for participants to effectively evaluate alternatives.  A concise statement that encapsulates this information should also be crafted to be used as a title for summary documentation.  Enter this title in the Decision box above the matrix.
     Below the matrix, in the Notes boxes, collect information as the matrix is constructed that is needed to “follow” the analysis and understand the decision rationale.  Space is limited to encourage the use of shorthand notes only; some typical contents are suggested in the schematic (Exhibit 1).  Detailed discussions and explanations, including the long-form decision description, should be documented separately.  The Pugh Matrix is typically a component of an analysis report where these details are also recorded.  Detailed information on the alternatives evaluated should also be included.
 
     A well-defined decision informs the development of a list of relevant evaluation criteria or specifications.  Each criterion or specification should be framed such that a higher rating is preferred to a lower one.  Ambiguous phrasing can lead to misguided conclusions.  For example, evaluators may interpret the criteria “cost” differently.  Some may rate higher cost with a higher score, though the reverse is intended.  A criterion of “low cost” or “cost effectiveness,” while referring to the same attribute and data, may communicate the intent more clearly.  Compile the list of criteria in the Evaluation section of the matrix (col. 1, rows 4 - 8).
     Next, the criteria are weighted, or prioritized, in the Criteria Weight column (col. 2, rows 4 - 8).  A variety of weighting scales can be used; thus it is critical that the scale in use be clearly defined prior to beginning the process.  A 1 – 10 scale offers simplicity and familiarity and is, therefore, an attractive option.  And 1 – 10 scale exemplar is provided in Exhibit 2.  The universal scale factor descriptions may be employed as presented or modified to suit a particular decision environment or organization.  Specify the scale in use in the Criteria Weight box (col. 2, row 2).
     With the relevant criteria in mind, it is time to brainstorm alternatives.  Before entering the alternatives in the matrix, the list should be screened as discussed in Project Selection – Process, Criteria, and Other Factors.  The matrix is simplified when alternatives that can quickly be determined to be infeasible are eliminated from consideration.  Enter the reduced list in the Alternatives section of the matrix (cols. 3 - 6, row 3).  The first alternative listed (col. 3, row 3) is the baseline or “datum”, typically chosen to satisfy one of the following conditions:
  • The incumbent (i.e. current, existing) product, process, etc.
  • A competitive product, technology, etc.
  • The most fully-defined or most familiar alternative, if no incumbent or other “obvious” choice exists.
  • Random choice, if no incumbent exists and alternatives cannot otherwise be sufficiently differentiated prior to analysis.
     Above each alternative identification, an additional cell is available for a “shortcut” reference.  This shortcut reference can take many forms, including:
  • A sketch representing a simple design concept (e.g. sharp corner, round, chamfer).
  • A single word or short phrase identifying the technology used (e.g. laser, waterjet, EDM, plasma).
  • A symbol representing a key element of the alternative (e.g. required PPE or recycling symbols for each type of material waste generated).
  • Any other reference that is relevant, simple, and facilitates analysis.
     If appropriate shortcut references are available, add them to the matrix (cols. 3 - 6, row 2) to accompany the formal alternative identifications.  Do not add them if they are not helpful reminders of the details of the alternatives, are ambiguous, misleading, or distracting, or otherwise fail to add value to the matrix.  They are not required in the matrix and should only be added if they facilitate the evaluation process.
 
     The next step in the process is to select an evaluation scale; 3-, 5-, and 7-point scales are common, though others could be used.  The scale is centered at zero, with an equal number of possible scores above and below.  The 3-point scale will have one possible score above (+1) and one possible score below (-1) zero.  The 5-point scale will have two possible scores on each side of zero (+2, +1, 0, -1, -2) and so on.  Larger scales can also be constructed; however, the smallest scale that provides sufficient discrimination between alternatives is recommended.  A positive score indicates a preference to the baseline and a negative score indicates that an alternative is less preferred to the baseline, or disfavored, with respect to that criterion.  Larger numbers (when available in the scale selected) indicate the magnitude of the preference.  Specify the scale in use in the Evaluation Scale box (col. 2, row 2).
     Complete the Evaluation section of the matrix by rating each alternative.  Conduct pairwise comparisons of each alternative and the baseline for each criterion listed, entering each score in the corresponding cell (cols. 4 - 6, rows 4 - 8).  By definition, the baseline alternative scores a zero for each criterion (col. 3, rows 4 - 8).
 
     Once all of the alternatives have been scored on all criteria, the Summary section of the matrix can be completed.  The expanded Summary section presented in Exhibit 1 allows detailed analysis and comparisons of performance while remaining easy to complete, requiring only simple arithmetic.
     To populate the upper subsection of the Summary, tally the number of criteria for which each alternative is equivalent to (score = 0), preferred to (score > 0), and less preferred than (score < 0) the baseline.  Enter these numbers in the # Criteria = 0 (cols. 4 - 6, row 9), # Criteria > 0 (cols. 4 - 6, row 10), and # Criteria < 0 (cols. 4 - 6, row 11) cells, respectively.  The baseline’s tallies will equal the number of criteria, zero, and zero, respectively.
     The lower subsection of the Summary displays the weighted positive (preferred) and negative (disfavored) scores for each alternative.  For each criterion for which an alternative is preferred, multiply its criteria weight by the evaluation score; sum the products in the Weighted > 0 cell for each alternative (cols. 4 - 6, row 12).  Likewise, sum the products of the weights and scores of all criteria for which an alternative is disfavored and enter the negative sum in the Weighted < 0 cell for that alternative (cols. 4 - 6, row 13).  Again, the baseline receives scores of zero (col. 3, rows 12 - 13).
 
     The final portion of the matrix to be populated is the Rank section, which includes the Total Score (cols. 3 - 6, row 14) and Gross Rank (cols. 3 - 6, row 15) of each alternative.  The Total Score is calculated by summing the products of weights and scores for all criteria or simply summing the positive and negative weighted scores.  Again, the definition of baseline scoring requires it to receive a zero score.  Other alternatives may earn positive, negative, or zero scores.  A positive Total Score implies the alternative is “better than,” or preferred to, the baseline, while a negative score implies it is “worse than” the baseline, or disfavored.  A Total Score of zero implies that the alternative is equivalent to the baseline, or exhibits similar overall performance.  Any two alternatives with equal scores are considered equivalent and decision-makers should be indifferent to the two options until a differentiating factor is identified.
     Finally, each alternative is assigned a Gross Rank.  The alternative with the highest Total Score is assigned a “1,” the next highest, a “2,” and so on.  Alternatives with equal Total Scores will be assigned equal rank to signify indifference.  The next rank number is skipped; the lowest rank equals the number of alternatives.  The exception occurs where there is a tie at the lowest score, in which case the lowest rank is equal to the highest ranking available to the equivalent alternatives.  The following examples illustrate the method of ranking with equal scores:
  • Of six alternatives, three are scored equally at second rank.  The Gross Rank of these six alternatives is, therefore, [1–2–2–2–5–6].
  • Of six alternatives, the two lowest-scoring alternatives are equivalent.  The Gross Rank of these six alternatives is, therefore, [1–2–3–4–5–5].
The hierarchy of alternative preferences is called Gross Rank because further analysis may lead decision-makers to modify the rank order of alternatives or “break ties.”  This will be discussed in more detail later.
 
     Review the Summary section of the matrix to validate the ranking of alternatives and to “break ties.”  The number of preferred and disfavored characteristics can influence the priority given to equally-scored alternatives.  Also, the magnitude of the weighted positive and negative scores may sufficiently differentiate two alternatives with equal Total Scores and, therefore, equal Gross Rank.  Alternatives with weighted scores of [+5, -3] and [+3, -1] each receive a Total Score of +2.  However, further consideration may lead decision-makers to conclude that the additional benefits represented by the higher positive score of the first alternative do not justify accepting the greater detriment of its higher negative score.  Thus, the second alternative would be ranked higher than the first in the final decision, despite its lower positive score.  [See “Step 5” of The Rational Model (Vol. II) for a related discussion.]
     It is also possible for all alternatives considered to rank below the baseline (i.e. negative Total Score).  That is, the baseline achieves a rank of “1.”  If the baseline alternative in this scenario is the status quo, the do nothing option is prescribed.  If the do nothing option is rejected, a new matrix is needed; this is discussed further in “Iterative Construction,” below.
 
     Conducting sensitivity analysis can increase confidence in the rankings or reveal the need for adjustments.  This can be done in similar fashion to that described for AHP (Vol. III).  Common alterations include equal criteria weights (i.e. = 1, or no weighting) and cost-focus (weighting cost significantly more than other criteria).  If there was difficulty reaching consensus on criteria weights, the influence of the contentious criteria can be evaluated by recalculating scores for the range of weights initially proposed.  Likewise, the impact of zero-score evaluations, resulting from disagreement about an alternative’s merits relative to the baseline, can be similarly explored.  No definitive instruction can be provided to assess the results of sensitivity analysis; each decision, matrix, and environment is unique, requiring decision-makers to apply their judgment and insight to reach appropriate conclusions.
 
Iterative Construction
     A completed Pugh Matrix provides decision-makers with an initial assessment of alternatives.  However, the matrix may be inconclusive, incomplete, or otherwise unsatisfactory.  As mentioned above, there may be decisions to be made within the matrix.  The necessity of minor adjustments may be easily communicated without further computations, while others may warrant constructing a new matrix with modifications incorporated.  Thus, a thorough analysis may require an iterative process of matrix development.
     There are many changes that can be made for subsequent iterations of matrix construction.  Everything learned from previous iterations should be incorporated in the next, to the extent possible without introducing bias to the analysis.  A number of possible modifications and potential justifications are presented below.
  • Refine the decision description.  If the evaluation process revealed any ambiguity in the definition of the decision to be made, clarify or restate it.  The purpose of the matrix must be clear for it to be effective; a “narrowed” view may be needed to achieve this.
  • Refine criteria definitions.  If the framing of any evaluation criterion has caused difficulty, such as varying interpretation by multiple evaluators, adjust its definition.  Consistent interpretation is required for meaningful evaluation of alternatives.
  • Add or remove criteria.  Discussion and evaluation of alternatives may reveal relevant criteria that had been previously overlooked; add them to the list.  Criteria that have failed to demonstrate discriminatory power can be removed.  This may occur when one criterion is strongly correlated with another; the alternatives may also be legitimately indistinguishable in a particular dimension.  Criteria should not be eliminated hastily, however, as results of another iteration may lead to a different conclusion.  Therefore, a criterion should be retained for at least two iterations and removed only if the results continue to support doing so.  New alternatives should also be evaluated per any eliminated criterion to validate its continued exclusion from the matrix.
  • Modify the Criteria Weighting Scale.  Though included for completeness, modifying the criteria weighting scale is, arguably, the least helpful adjustment that can be made.  It is difficult to conceive of one more advantageous than the “standard” 1 – 10 scale; it is recommended as the default.
  • Review Criteria Weights.  If different conclusions are reached at different steps of the analysis and review, criteria weighting may require adjustment.  That is, if Evaluation Tallies, Total Scores, and sensitivity analysis indicate significantly different preferences, the Criteria Weights assigned may not accurately reflect the true priorities.  Criteria Weights should be revised only with extreme caution, however; bias could easily be introduced, supporting a predetermined, but suboptimal, conclusion.
  • Add or remove alternatives.  Discussion and evaluation may lead to the discovery of additional alternatives or reveal opportunities to combine favorable characteristics of existing alternatives to create hybrid solutions.  Add any new alternatives developed to the matrix for evaluation.  Alternatives that are comprehensively dominated in multiple iterations are candidates for elimination.
  • Select a different baseline.  Comparing alternatives to a different baseline may interrupt biased evaluations, improving the accuracy of assessments and rankings.  Varied perspectives can also clarify advantages of alternatives, facilitating final ranking decisions.
  • Modify “shortcut” references.  If shortcut references do not provide clarity that facilitates evaluation of alternatives, modify or remove them.  It is better for them to be absent than to be confusing.
  • Refine the Evaluation Scale.  Implementing an evaluation scale with a wider range (replacing a 3-point scale with a 5- or 7-point scale, for example) improves the ability to differentiate the performance of alternatives.  Increased discriminatory power allows the capture of greater nuance in the evaluations, reducing the number of equivalent or indifferent ratings and creating a discernible hierarchy of alternatives.
     Additional research may also be needed between iterations.  Decision-making often relies heavily on estimates of costs, benefits, and performance as assessed by a number of metrics.  Improving these estimates may be necessary for meaningful comparison of alternatives and a conclusive analysis.
 
Pugh Matrix Example
     To demonstrate practical application of the Pugh Matrix Method, we revisit the hypothetical machine purchase decision example of Vol. III.  The AHP example presented was predicated on a prior decision (unstated, assumed) to purchase a new machine; only which machine to purchase was left to decide.  The detailed decision definition, or objective, was “Choose source for purchase of new production machine for Widget Line #2.”  For the Pugh Matrix example, the premise is modified slightly; no prior decision is assumed.  The decisions to purchase and which machine to purchase are combined in a single process (this could also be done in AHP).  To so formulate the decision, it is defined as “Determine future configuration of Widget Line #2.”
     Evaluation criteria are the same as in the AHP example, weighted on a 1 – 10 scale.  Criteria weights are chosen to be comparable to the previous example to the extent possible.  The evaluation criteria and corresponding weights are presented in Exhibit 3.
     The alternatives considered are also those from the AHP example, with one addition:  maintaining the existing widget machine.  The cost and performance expectations of each alternative are presented in Exhibit 4.  Maintaining the existing equipment is the logical choice for baseline; its performance and other characteristics are most familiar.  Consequently, estimates of its future performance are also likely to be most accurate.
     “Shortcut” reference images are inserted above the alternative (source company) names.  The performance summary is sufficiently brief that it could have been used instead to keep key details in front of evaluators at all times.  For example, the shortcut reference for the baseline could be [$0.8M_50pcs/hr_6yrs].
     To evaluate alternatives, an “intermediate” scale is chosen.  Use of a 5-point scale is demonstrated to provide balance between discrimination and simplicity.  The scoring regime in use is presented in Exhibit 5.
     Each alternative is evaluated on each criterion and the scores entered in the Evaluation section of the example matrix, presented in Exhibit 6.  Alternative evaluations mirror the assessments in the AHP example of Vol. III to the extent possible.  There should be no surprises in the evaluations; each alternative is assessed a negative score on Cost and positive scores on Productivity and Service Life.  This outcome was easily foreseeable from the performance summary of Exhibit 4.  After populating the Evaluation section, the remainder of the matrix is completed with simple arithmetic, as previously described.
     A cursory review of the Summary section reveals interesting details that support the use of this formulation of the Pugh Matrix Method to make this type of decision.  First, the three new machine alternatives are equal on each of the score tallies.  Without weighting, there would be nothing to differentiate them.  Second, the Jones and Wiley’s machines have equal negative weighted scores.  This could be of concern to decision-makers, particularly if no clear hierarchy of preferences is demonstrated in the Gross Rank.  Were this to be the case, repeating the evaluations with a refined scale (i.e. 7-point) may be in order.
     Finally, the Pugh Matrix Method demonstrated the same hierarchy of preferences (Gross Rank) as did AHP, but reached this conclusion with a much simpler process.  This is by no means guaranteed, however; the example is purposefully simplistic.  As the complexity of the decision environment increases, the additional sophistication of AHP, or other tools, becomes increasingly advantageous.
     Sensitivity analysis reveals that the “judgment call” made to score Wiley’s Productivity resulted in rankings that match the result of the AHP example.  Had it been scored +2 instead of +1, the ranks of Wiley’s and Acme would have reversed.  Again, a refined evaluation scale may be warranted to increase confidence in the final decision.
 
Variations on the Theme
     As mentioned in the introduction, the Pugh Matrix Method is presented as a composite of various constructions; every element of the structure presented here may not be found in another single source.  Also, additional elements that could be incorporated in a matrix can be found in the literature.  Several possible variations are discussed below.
     In its simplest form, the Summary section of the Pugh Matrix contains only the Evaluation Tallies, as weights are not assigned to criteria.  Evaluation of alternatives is conducted on a +/s/- 3-point “scale,” where an alternative rated “+” is preferred to baseline, “-“ is disfavored relative to baseline, and “s” is the same as, or equivalent to, baseline in that dimension.  Refining the evaluation scale consists of expanding it to ++/+/s/-/-- or even +++/++/+/s/-/--/---. Larger scales inhibit clarity for at-a-glance reviews.  Until the number of criteria and/or alternatives becomes relatively large, the value of this “basic” matrix is quite limited.  A basic Pugh Matrix for the example widget-machine purchasing decision is presented in Exhibit 7.  As mentioned in the example, a coarse evaluation scale and the absence of criteria weighting result in three identically scored alternatives.  The matrix has done little to clarify the decision; it reinforces the decision to buy a new machine, but has not helped determine which machine to purchase.
     Features not present in the basic matrix were chosen for inclusion in the recommended construction.  Some features included (shown in “Pugh Matrix Example,” above), and their benefits, are:
  • Expanded Summary section
    • Greater insight into relative performance of alternatives.
  • 5-Point Numerical Evaluation Scale
    • At-a-glance clarity.
    • Compatibility with and simplicity in use of spreadsheet for calculations.
    • Balance between discrimination and simplicity.
  • 1 – 10 Criteria Weight Scale
    • Familiarity and simplicity of scale.
    • Greater discriminatory power of matrix.
 
     The Summary section in our matrix could be called the “Alternative Summary” to differentiate it from a “Criteria Summary.”  The purpose of a Criteria Summary, ostensibly, is to evaluate the extent to which each requirement is being satisfied by the available alternatives.  Our example analysis, with Criteria Summary (cols. 9 – 14, rows 4 – 13), is shown in the expanded matrix presented in Exhibit 8.  It is excluded from the recommended construction because of its potential to be more of a distraction than a value-added element of the matrix.  While the Evaluation Tallies may provide an indication of the quality of the alternatives offered, it is unclear how to use the weighted scores or Total Scores to any advantage (i.e. is a score of 40 outstanding, mediocre, or in between?).  If decision-makers do perceive value in a Criteria Summary, it is a simple addition to the matrix; Evaluation Tallies, weighted scores, and Total Scores are analogous to those calculated in the Alternatives Summary.
     The use of primary and secondary criteria is also optional.  Refer to “Project Selection Criteria” in Project Selection – Process, Criteria, and Other Factors for a related discussion.  In the Project Selection discussion, primary criteria were called “categories of criteria,” while secondary criteria was shortened to, simply, “criteria.”  Though the terminology used is slightly different, either set of terms is acceptable, as the concept and advantages of use are essentially identical.  Organization, summary, and presentation of information can be facilitated by their use.  For example, reporting scores for a few primary criteria may be more appropriate in an Executive Summary than several secondary criteria scores.  However, this method is only advantageous when the number of criteria is large.  Reviewers should also beware the potential abuse of this technique; important details could be masked – intentionally or unintentionally hidden – by an amalgamated score.
 
     An alternate criteria-weighting scheme prescribes the sum of all criteria weights equal unity.  This is practical only for a small number of criteria; larger numbers of criteria require weights with additional significant digits (i.e. decimal places).  The relative weights of numerous criteria quickly become too difficult to monitor for the scores to remain meaningful.  The 1 – 10 scale is easy to understand and consistently apply.
     Nonlinear criteria weighting can significantly increase the discriminatory power of the matrix; however, it comes at a high cost.  Development of scoring curves can be difficult and the resulting calculations are far more complex.  A key advantage of the Pugh Matrix Method – namely, simplicity – is lost when nonlinear scoring is introduced.
 
     The final optional element to be discussed is the presentation of multiple iterations of the Pugh Matrix Method in a single matrix.  An example, displaying three iterations, is presented in Exhibit 9.  Features of note include:
  • Results of multiple iterations are shown side by side by side for each alternative.
  • Choice of baseline (“datum”) for each iteration is clearly identified (dark-shaded columns).
  • Eliminated alternatives are easily identified (light-shaded columns).
  • Eliminated criteria are easily identified (light-shaded rows).
  • Graphical presentation of summary scores is useful for at-a-glance reviews.
This presentation format is useful for a basic matrix (i.e. no criteria weighting, 3-point evaluation scale).  As features are added to the matrix, however, it becomes more difficult and less practical – at some point, infeasible – to present analysis results in this format.
Final Notes
     Confidence in any tool is developed with time and experience.  The Pugh Matrix Method is less sophisticated than other tools, such as AHP (Vol. III), and, thus, may require a bit more diligence.  For example, the Pugh Matrix lacks the consistency check of AHP.  Therefore, it could be more susceptible to error, misuse, or bias; the offset is its simplicity.  A conscientious decision-making team can easily overcome the matrix’s deficiency and extract value from its use.
     The Pugh Matrix is merely a decision-making aid and like any other, it is limited in power.  The outcome related to any decision is not necessarily an accurate reflection of any decision-making aid used.  It cannot overcome poor criteria choices, inaccurate estimates, inadequate alternatives, or a deficiency of expertise exhibited by evaluators.  “Garbage in, garbage out” remains true in this context.
     It is important to remember that “the matrix does not make the decision;” it merely guides decision-makers.  Ultimately, it is the responsibility of those decision-makers to choose appropriate tools, input accurate information, apply relevant expertise and sound judgment, and validate and “own” any decision made.
 
     For additional guidance or assistance with decision-making or other Operations challenges, feel free to leave a comment, contact JayWink Solutions, or schedule an appointment.
 
     For a directory of “Making Decisions” volumes on “The Third Degree,” see Vol. I:  Introduction and Terminology.
 
References
[Link] “How To Use The Pugh Matrix.”  Decision Making Confidence.
[Link] “What is a Decision Matrix?”  ASQ.
[Link] “Pugh Matrix.”  CIToolkit.
[Link] “The Systems Engineering Tool Box – Pugh Matrix (PM).”  Stuart Burge, 2009.
[Link] “The Pugh Controlled Convergence method: model-based evaluation and implications for design theory.”  Daniel Frey, et al;  Research in Engineering Design, 2009.
[Link] “Decide and Conquer.”  Bill D. Bailey and Jan Lee; Quality Progress, April 2016.
[Link] Farris, J., & Jack, H. (2011, June), Enhanced Concept Selection for Students Paper presented at 2011 ASEE Annual Conference & Exposition, Vancouver, BC. 10.18260/1-2--17895
[Link] Takai, Shun & Ishii, Kosuke. (2004). Modifying Pugh’s Design Concept Evaluation Methods. Proceedings of the ASME Design Engineering Technical Conference. 3. 10.1115/DETC2004-57512.
[Link] Kremer, Gül & Tauhid, Shafin. (2008). Concept selection methods - A literature review from 1980 to 2008. International Journal of Design Engineering. 1. 10.1504/IJDE.2008.023764.
[Link] “Concept Selection.”  Design Institute, Xerox Corporation, September 1, 1987
[Link] The Lean 3P Advantage.  Allan R. Coletta; CRC Press, 2012.

 
Jody W. Phelps, MSc, PMP®, MBA
Principal Consultant
JayWink Solutions, LLC
jody@jaywink.com
]]>
<![CDATA[Project Selection – Process, Criteria, and Other Factors]]>Wed, 26 Jan 2022 16:00:00 GMThttp://jaywinksolutions.com/thethirddegree/project-selection-process-criteria-and-other-factors     Committing resources to project execution is a critical responsibility for any organization or individual.  Executing poor-performing projects can be disastrous for sponsors and organizations; financial distress, reputational damage, and sinking morale, among other issues, can result.  Likewise, rejecting promising projects can limit an organization’s success by any conceivable measure.
     The risks inherent in project selection compels sponsors and managers to follow an objective and methodical process to make decisions.  Doing so leads to project selection decisions that are consistent, comparable, and effective.  Review and evaluation of these decisions and their outcomes also becomes straightforward.
     Project selection is a context-specific example of decision-making.  However, its complexity, ubiquity, and criticality warrant a focused treatment separate from the Making Decisions series.
     An effective selection process includes project justification. Adjacent to this process is the prioritization of selected projects and termination decisions regarding ongoing projects, though these topics are often considered in isolation, if at all.  Selection, prioritization, execution, and termination are elements of project portfolio management; successful organizations conduct these activities in concert.
 
Project Selection Process
     An effective project selection process typically proceeds in three phases:  Initiation, Configuration, and Analysis.  Each phase and the steps that comprise them are described in this section.

Initiation
     The initiation phase of project selection is exploratory in nature, where members of the organization consider possible projects.  (It should not be confused with initiating in project management, where project execution is commenced.)  The initiation phase of project selection consists of two steps:  Generate Ideas, and Screen.

Generate Ideas.  The organization brainstorms potential projects, compiling a list for consideration, sometimes called a “project register.”  Project ideas can originate from a multitude of sources; most will germinate within the organization, but suggestions from external partners should also be considered.  Potential sources of project ideas include:
  • Product Engineering
  • Operations Management
  • Quality Management
  • Facility Management
  • Production personnel
  • Sales & Marketing
  • Customers
  • Suppliers
  • Community members
     As suggested by the diversity of sources of project ideas, the objectives of potential projects can vary widely.  General categories of potential project objectives include:
  • technological innovation
  • new product introduction
  • product enhancement (e.g. new feature)
  • increased productivity
  • cost reduction
  • quality improvement
  • sustainability, reduced environmental impact
  • increased customer satisfaction
  • warranty claim reduction
  • Good Will, “positive press,” or community standing
  • legal or regulatory compliance
Specific objectives that fall under these generic headings are too numerous to contemplate.  The list is provided only to spur ideation and is not comprehensive.

Screen.  Once a list of potential projects has been generated, a preliminary evaluation, or screening, can be conducted.  Another term used for this step is feasibility study or, in the vernacular, “sanity check.”  The purpose of screening is to eliminate from consideration those projects that “obviously” cannot be pursued at the present time.  This assessment is based, in large part, on preliminary estimates of resource requirements or expected results.  Screening criteria may include:
  • budget (i.e. funding required)
  • staffing (number or expertise) required
  • schedule (i.e. time required)
  • pending obsolescence of product, process, facility, or equipment
  • strategy alignment
  • regulatory requirements
     Project ideas that fail to pass the screening stage may remain on the project register, or backlog, to be revisited when conditions are more favorable.  Those that are deemed too fanciful, blatantly self-serving, or politically motivated may be stricken from the register to conserve resources that future review would consume and maintain a realistic and professional project register.
 
Configuration
     The configuration phase of project selection is where the organization defines how projects will be evaluated, compared, and selected or rejected.  It consists of three steps:  Choose Criteria, Choose Selection Method, and Evaluate Project Proposals.

Choose Criteria.  Selection criteria can be chosen from many options; they must align with the nature of the project and its objectives for the selection process to be effective.  Criteria selection will discussed further in the Project Selection Criteria section below.

Choose Selection Method.  Selection method options are also numerous.  Simple or small-scale projects may be sufficiently evaluated with a straightforward cost/benefit analysis, while larger, complex projects may require more sophisticated evaluations with input from a larger number of people.
     Some group decision-making techniques are discussed in Making Decisions – Vol. V.  The Analytic Hierarchy Process (AHP) (Making Decisions – Vol. III) and the Rational Model (Making Decisions – Vol. II) are also presented in previous posts.  A hybrid approach could also be used.  For example, the Delphi Method (see Making Decisions – Vol. V) could be used to determine the criteria and weighting factors to be used in AHP.
     The organization is free to choose or create a project selection method.  Whatever the choice, it is critical that the selection method be well-defined, objective, “tamper-resistant,” and clearly communicated to minimize the possibility – or suspicion – of bias or “gaming.”

Evaluate Project Proposals.  In this step, values are determined for each of the selection criteria for each project under consideration.  These are often presented in monetary terms to simplify investment justification, but need not be.  Other values that can be compared before and after project execution or between projects are equally valid.  Examples include first time yield (FTY), transactions per hour, equipment utilization, and so on.  These values will likely be translated to monetary terms at some point, but are perfectly acceptable for initial evaluations and comparisons.
 
Analysis
     The project selection process concludes with the analysis phase, where decisions are finalized.  This phase is completed in three steps:  Analyze Proposals, Analyze Risk, and Update Project Portfolio.

Analyze Proposals.  Using the selection method chosen, score, compare, or otherwise analyze each project proposal according to the criteria values determined above.  If a clear favorite is not identified, the selection method may need to be refined.  If sufficient capacity is available, refinement may be eschewed and two “tied” projects selected for execution.

Analyze Risk.  Any risks not fully accounted for in the chosen selection criteria and method should be considered before finalizing selection decisions.  Also, the risk of foregoing projects that were not selected – particularly if rejected by a slim margin – should also be considered.  Overriding selections made by the defined method must be justified and approved at a high level.  Justifications for overriding a selection decision may include:
  • Actions by competitors and the resulting market position each may occupy.
  • Confidence in estimates or probability of success.
  • Potential damage caused by project failure (financial, reputational, etc.).
  • The organization’s resistance to change.
  • The influence of uncontrollable factors.
Update Project Portfolio.  When the final selections have been made, the organization’s project portfolio should be updated.  The project(s) chosen for immediate execution are added to “active projects,” while those postponed are recorded in the project backlog.  The active projects must be prioritized to streamline resource allocation decisions; this is discussed further in the Other Project Selection Factors section below.
 
Project Selection Criteria
     Evaluation and selection criteria are chosen in the Configuration phase of the project selection process.  An organization can choose as many, or as few, criteria as are relevant to the projects it expects to execute.  Criteria based on unconventional metrics could also be used, so long as they are objective and can be utilized consistently.  All criteria, whether commonplace or unique, should be reviewed in light of project performance and results obtained.
     Potential selection criteria are numerous; many can be grouped in broad categories to organize information and facilitate configuration of the selection process.  Common categories and criteria include:
  • Financial Criteria
    • Return on Investment (ROI)
    • Net Present Value (NPV)
    • Internal Rate of Return (IRR)
    • Economic Value Added (EVA)
    • Loss Function
  • Operational Criteria
    • First Time Yield (FTY) or other quality metric
    • Equipment Downtime/Availability
    • Productivity
  • Resource Criteria
    • budget (i.e. funding required)
    • schedule (i.e. time required)
    • staffing required
  • Sustainability/Environmental Criteria
    • raw material usage
    • emissions, waste generated
    • energy consumption
  • Experiential Criteria
    • new employee onboarding
    • recent graduate experience
    • new project manager experience
    • knowledge to be gained (technology, process, equipment, software, etc.)
  • Relational Criteria
    • employee morale, turnover
    • Customer Satisfaction (e.g. Net Promoter Score [NPS])
  • Replicability (potential to leverage results in other contexts)
  • Good Will generation
      To simplify the decision model, several of the example criteria could be consolidated in financial metrics.  That is, operational, environmental, and other criteria could be defined in monetary terms and included in financial calculations.  Social pressure may preclude this practice, however; the significance of some criteria transcend financial metrics.  Also, choosing among projects with similar financial expectations is facilitated by the discrimination afforded by independent criteria.
 
Other Project Selection Factors
     A project selection process should be as objective as possible to achieve the most favorable results with the greatest efficiency.  However, this does not preclude the need for sound judgment and deep insight when making portfolio decisions, including project prioritization.
     The first level of prioritization is achieved via the project selection process described above.  High-priority projects are added to the “active” list, while others are relegated to the backlog.  Within the list of active projects, another level of prioritization must be defined.  The urgency of a new project may cause it to be prioritized above one already in progress.
     Limited resources may require the ongoing project to be placed on hold.  Review of project performance and updated forecasts may even result in project termination, freeing resources to support other projects in the portfolio.  The decision to terminate a project or place it on hold should not be made helter-skelter, however.  Incomplete projects incur costs without delivering benefits; restarting a stalled project can require more effort than it would have previously required to complete it.
     Other perils also lurk within portfolio management and project selection processes.  Self-serving, self-justifying, politically-motivated, or otherwise biased or unethical managers can influence decisions, leading to a suboptimal (to be charitable) project portfolio, if they are left to operate without “guard rails” (see Making Decisions – Vol. VII:  Perils and Predicaments).
     Undue influence, bias, inexperience, and other decision-altering factors and the negative consequences they breed can be minimized by operating according to a portfolio management standard.  An organization can provide the guard rails needed by adding requirements that are specific to project selection to its decision-making standard.  An outline of this standard is provided in the section titled “How should a decision-making standard be structured?” in Making Decisions – Vol. IV.  Additional guidance on portfolio management can be obtained from the Project Management Institute (PMI).
 
     Competent project selection requires multiple points of view – short- and long-term, financial and nonfinancial.  An organization that constructs a portfolio in which each project augments and amplifies the benefits of previous projects can expect better performance than competitors that use a less analytical project selection process.
 
     For additional guidance or assistance with project selection or other Operations challenges, feel free to leave a comment, contact JayWink Solutions, or schedule an appointment.
 
References
[Link] “Project selection and termination--how executives get trapped.”  W.G. Meyer; Project Management Institute, 2012.
[Link] “Knowledge Contribution as a Factor in Project Selection.”  Shuang Geng, et al; Project Management Journal, February/March 2018.
[Link] “The ‘Everything is Important’ paradox:  9 practical methods for how to prioritize your work (and time).”  Jory MacKay; Rescue Time, May 5, 2020.
[Link] “Taguchi loss function.”  Six Sigma Ninja, November, 11, 2019.
[Link] “Everything You Need to Know about Project Selection.”  Kate Eby; Smartsheet, August 16, 2021.
[Link] “Use the Value Index to Prioritize Project Efforts.”  Carl Berardinelli; iSixSigma.
[Link] “Selecting the Best Business Process Improvement Efforts.”  J. DeLayne Stroud; iSixSigma.
[Link] “Use Point System for Better Six Sigma Project Selection.”  Drew Peregrim; iSixSigma.
[Link] “Project Selection: Don't Pan for Gold in Your Hot Tub!”  Gary A. Gack; iSixSigma.
[Link] “Black Belts Should Create Balanced Project Portfolios.”  William Rushing; iSixSigma.
[Link] “Select Projects Using Evaluation and Decision Tools.”  Rupesh Lochan; iSixSigma.
[Link] “PMP : Quantitative Approach to Selecting the Right Project.”  Abhishek Maurya; Whizlabs, March 20, 2017.
[Link] “A Guide to Project Prioritization and Selection.”  EcoSys Team; October 2, 2018.
[Link] “ISO 13053: Quantitative Methods in Process Improvement – Six Sigma – Part 1:  DMAIC Methodology.”  ISO, 2011.
[Link] Juran’s Quality Handbook.  Joseph M. Juran et al; McGraw-Hill.
[Link] “Project Prioritization Troubles? Brainstorm New Metrics.”  Barbara Carkenord; RMC Learning Solutions, September 8, 2021.
[Link] “Using Taguchi's Loss Function to Estimate Project Benefits.”  Michael Ohler; iSixSigma.
[Link] “The Right Decision.”  Douglas P. Mader; Quality Progress, November 2009.
[Link] “Trade-off Analysis in Decision Making.”  APQC, 2009.
[Link] “Building Leadership Capital – Action Learning Project Workbook.”  Deakin University, 2013.

 
Jody W. Phelps, MSc, PMP®, MBA
Principal Consultant
JayWink Solutions, LLC
jody@jaywink.com
]]>
<![CDATA[Commercial Cartography – Vol. V:  Hazard Mapping]]>Wed, 12 Jan 2022 16:00:00 GMThttp://jaywinksolutions.com/thethirddegree/commercial-cartography-vol-v-hazard-mapping     An effective safety program requires identification and communication of hazards that exist in a workplace or customer-accessible area of a business and the countermeasures in place to reduce the risk of an incident.  The terms hazard, risk, incident, and others are used here as defined in “Safety First!  Or is It?
     A hazard map is a highly-efficient instrument for conveying critical information regarding Safety, Health, and Environmental (SHE) hazards due to its visual nature and standardization.  While some countermeasure information can be presented on a Hazard Map, it is often more salient when presented on a corollary Body Map.  Use of a body map is often a prudent choice; typically, the countermeasure information most relevant to many individuals pertains to the use of personal protective equipment (PPE).  The process used to develop a Hazard Map and its corollary Body Map will be presented.
     Hazard mapping originated with Italian auto workers’ attempts to raise awareness of workplace hazards in the 1960s.  A factory blueprint was adorned with circles of varying size and color, each representing a particular hazard and the risk associated with it.
     When this map was presented to management by the workers’ union, it was rejected.  The company claimed that it was unscientific and, therefore, unreliable.  Subsequent research by scientists, however, substantiated the workers’ empirical findings.
     To impart the greatest value, hazard mapping efforts, and the entirety of the safety program, should employ both scientific (data collection and analysis) and empirical (observations and perceptions) methods.  When findings from both methods correlate, management support for investment in safety improvements should be forthcoming.  When they do not, further investigation may be necessary to ensure that all hazards are receiving the attention they warrant, be it from management or from imperiled individuals.
     Hazard maps come in many forms, as there is no single, widely-accepted standard.  Various organizations have established hazard classification schemes, risk rating scales, color codes, and symbology to be used in the creation of their hazard maps.  Standardization within an organization is critical to effective communication to employees, contractors, and others throughout its facilities.  Standardization across organizations is highly desirable, but has not yet come to fruition.
     The next section proposes a hazard classification and risk rating scheme targeting four key characteristics of an effective communication tool.  Such a tool should be:
  • consistent.  Providing a single communication standard ensures that all affected individuals can quickly comprehend the information presented, no matter where they venture within an organization.
  • visual.  Use of colors and symbols facilitates rapid assimilation of critical information, while greater detail is provided in accompanying text.
  • universal.  To the extent possible, information is clearly conveyed, irrespective of the native language or expertise of the reader.
  • compatible.  A communication standard that parallels those used for other purposes within the organization can be rapidly adopted without inducing misinterpretation.
 
The JayWink Standard
     The standard proposed here exhibits the four key characteristics described above.  Consistency is achieved by describing each hazard category and risk severity category such that practitioners can easily differentiate between them.
     There are four risk severity categories that encompass a 1 – 10 risk rating scale, as follows:
  • Low (1 – 2)
  • Moderate (3 – 5)
  • High (6 – 8)
  • Extreme (9 – 10)
There are five hazard classifications, defined as follows:
  • Physical Safety – acute injuries caused by accidents.  Many are visible, such as lacerations; others may be less obvious, such as contusions or electrical shock.
  • Chemical/Radiation Exposure – exposure to dangerous chemicals or radioactive substances caused by a spill or other release.  Burns, respiratory distress, or other acute injury may result.  Chronic health issues may also result from exposure, persisting long after the obvious injuries have healed.
Physical Safety and Chemical/Radiation Exposure comprise the supercategory of Safety Hazards.
  • Ergonomic Factors – chronic health issues that result from repetitive tasks or conditions that can cause injury, though not generally considered accidents.  Lack of sufficient access space, lifting aids, or lighting, and excessive noise are common examples.
  • Stressors – psychological factors that influence an individual’s well-being.  Examples include unreasonable or disrespectful supervisors, uncooperative coworkers, time pressure, and lack of security.  These factors can effect performance so severely that other risks are amplified.  For example, an individual’s stress-related loss of focus could lead to a mistake that results in a chemical spill or physical injury.
Ergonomic Factors and Stressors comprise the supercategory of Health Hazards.
  • Environmental Hazards – long-term impacts on natural systems; societal cost.  Any potential contamination of air, water, or soil and consumption of natural resources are included in this category.
     A description of each risk severity category, corresponding to each hazard classification, is provided in the summary table in Exhibit 1.  Examples of hazards in each classification, or category, are provided in Exhibit 2.
     The visual nature of the standard is partially revealed in Exhibit 1; the color codes for hazard classifications and risk severity categories are shown.  Risk severity categories are presented as follows:
  • Low:  Green
  • Moderate:  Yellow
  • High:  Orange
  • Extreme:  Red.
The hazard classification color code is as follows:
  • Physical Safety:  Red
  • Chemical/Radiation Exposure:  Yellow
  • Ergonomic Factors:  Blue
  • Stressors:  Black
  • Environmental Risks:  Green.
The color codes are used in conjunction with standard symbols that represent the hazard classification supercategories, as shown in Exhibit 3.  Use of the composite symbols to construct a Hazard Map will be demonstrated in the next section.
     The standard maintains universality, in part, by incorporating standard, recognizable symbols defined in ISO 7010 Graphical symbols – Safety colours and safety signs – Registered safety signs; the global standard provides international users a common resource.  Limiting the number of hazard classifications and symbols used facilitates broad application in a wide variety of environments.  This standard is equally applicable to manufacturing operations and service industries of all types, in both front-office and back-office settings.  It can also be applied to government buildings, community centers, parks, or other publicly-accessible areas to facilitate protection of patrons.
     The hazard mapping standard is highly compatible with other communication tools in common use.  The 1 – 10 scale is similar to the severity, occurrence, and detection scales used in FMEA.  The four color-coded risk severity categories are analogous to the Homeland Security Advisory System (replaced in 2011) that warned the USA of terrorist threats.  The hazard classification color code also incorporates broadly-recognized implications – red and yellow are commonly associated with safety concerns, while green is “the” color representing matters of concern for the environment.
 
Creating a Hazard Map
     To create a Hazard Map, begin with a visual representation of the area under scrutiny, such as a layout, as discussed in Commercial Cartography – Vol. III.  If a layout has not been created, a sketch, photo, or other image may suffice, provided sufficient detail can be shown and hazard locations are accurate enough to effectively manage them.  Larger facilities may require several Hazard Maps to present all required information clearly.  For example, each department of a manufacturing facility may have its own map.  A small retail outlet, on the other hand, may be sufficiently documented on a single map.
     As is the case with many Operations documents, Hazard Maps often proceed through several draft stages before they are published.  A moderately complex map may be developed, for example, in the following four stages.
     Creation of a first draft begins by adding hand-written notes to a layout drawing.  A small group of knowledgeable individuals can brainstorm the area’s hazards, completing this step quickly.  The next step is to collect information for the map’s legend.  Hand-written notes are also recommended here to maintain the flow of information in the group.
     Convert the hand-written notes into hazard descriptions and classifications; evaluate each and assign a risk rating number.  Create a composite symbol representing each hazard identified and add it to the layout as shown in Exhibit 4.  A hazard symbol can be placed on a map to identify the location of a hazard as an entire area or a specific machine, work cell, process line, etc.  In the Simple Hazard Map Example, the first two hazards use leaders to identify the location as the “Chemical Store,” while the other locations are more general.  The less-specific locations can be interpreted as “approximate,” “the area surrounding the symbol,” or other similar statement.  If greater precision is required than can be made obvious on the layout, greater detail should be included in the hazard description on the map legend, such as a machine ID.
     Complete the Hazard Map Legend for the hazards identified, adding any missing information on countermeasures and reaction plans and color-coding the hazard and risk information columns, as shown in Exhibit 5.
     A second draft is created by conducting an onsite review to verify information included in the first draft and identify new hazards to be added.  The first two drafts are created, primarily, by scientific methods; thus, any SHE data available should be incorporated.
     The third draft is created by incorporating the empirical data collected from “front-line” personnel.  Observations and perceptions that have not previously been recorded, in SHE data or otherwise, must be evaluated and properly addressed.  Legitimate hazards are added to the map and put “on the radar” for development of additional protections or elimination strategies.  Unfounded concerns should be alleviated through education, but should never be ignored.
     A final review by all concerned parties should result in a Hazard Map approved for publication.  To be complete, a Hazard Map must include three components:
(1) the layout with composite symbols identifying the hazard location and basic information (Exhibit 4);
(2) the legend table containing detailed hazard information (Exhibit 5); and
(3) an explanation of the composite symbols (Exhibit 3 and Exhibit 5).
 
     Hazard Maps are most valuable when they are collaboratively developed, prominently displayed for easy reference, and all personnel have a working understanding of the information presented on them.  Large facilities with multiple hazard maps may find it useful to compile data from all maps into a “Master Legend,” creating a single reference for hazards throughout the entire plant.  A Master Legend can be sorted in various ways to facilitate resource allocation decisions for SHE projects.  For example, grouping by hazard classification may reveal opportunities to improve several areas with a single project or provide background data for OSHA partner programs or ISO 14001 initiatives.  Also, sorting by risk rating can facilitate prioritization of projects among disparate areas of the facility.
     Hazard Maps – and the underlying data and perceptions – should be periodically reviewed and updated.  Process development, equipment upgrades, risk mitigation projects, organizational changes, and other factors could cause significant changes in the hazard and risk profiles of a facility.  Hazard Maps must reflect current conditions to be effective.
 
Creating a Body Map
     Human nature ensures that individuals will focus, predominantly, on their immediate well-being and somewhat less on their long-term health.  Environmental issues, while important, tend to enjoy less mindshare than more proximate causes of distress.  This tendency will direct attention toward the countermeasures recorded in Hazard Map Legends.  These are the mechanisms by which individuals protect themselves from hazards.  The most salient – and easiest to implement reliably – is often PPE; proper use of PPE is critical to a safe environment.  A simple and effective tool to support proper use is a PPE Body Map, or simply Body Map.
     Several options exist for presenting required PPE on a body map.  The example in Exhibit 6 uses a generic outline of a human form with standard symbols (ISO 7010) placed near the part of the body to be protected.
     Exhibit 7 provides a more relatable image of a person, but some ambiguity may remain (“Is that a fire suit?”).  The image in Exhibit 8 is more realistic, conveying more information visually than the previous example.  Text provides additional information to aid understanding of the types of hazards from which each protects the user.  The symbols, however, may not be universally understood, particularly by international associates.
     The most information may be conveyed with the least effort when standard symbols and detailed text accompany a photo of person wearing the PPE described.  This is the recommended format of Body Map; an example is shown in Exhibit 9.  Any text added to the body map should be succinct and critical to proper use of PPE.  Examples of appropriate text include specifications (e.g. ANSI Z87.1 for safety glasses, filtration level requirements for respirators) or additional requirements (e.g. cut-resistance rating for gloves).
     The Body Map can be further augmented, if desired, with direct references to the Hazard Map Legend.  For example, the composite symbol “flags” can be reproduced adjacent to the PPE identifiers.  Some examples of this practice are shown in Exhibit 9.  Doing so may reinforce users’ understanding of the Hazard Map and the importance of proper PPE.  In turn, this could facilitate training and reduce the effort required to monitor and enforce proper PPE use.
Body Map developers must weigh the benefits of additional information against the risk of information overload – sometimes, less is more.  Publish Body Maps that will provide the greatest benefit to individuals and the organization – ones that will be referenced regularly and understood thoroughly.
     As mentioned in the introduction, a Body Map is a corollary to – not a component of – a Hazard Map.  It is, however, a relatively simple step that adds value to the safety program by translating hazard information into an easily-understood format that helps individuals protect themselves.
     Like Hazard Maps and other documents, Body Maps should be subject to periodic review to verify that current information is provided and individuals are adequately protected.  Overlaying incident data on a PPE Body Map can be an effective aid to evaluating current protection measures.
 
     Hazard maps and body maps are valuable additions to an organization’s SHE toolbox.  The information assembled to create them supports improvement initiatives on several fronts; creativity and insight of practitioners may reveal additional opportunities to leverage these tools across their organizations.
 
     For additional guidance or assistance creating Hazard Maps for your organization’s facilities or other Operations challenges, feel free to leave a comment, contact JayWink Solutions, or schedule an appointment.
 
     For a directory of “Commercial Cartography” volumes on “The Third Degree,” see Vol. I:  An Introduction to Business Mapping.
 
References
[Link] “Hazard Mapping Reduces Injuries at GE.”  Communications Workers of America; December 2, 2009.
[Link] RiskMap.com
[Link] “ISO 7010:2019(en)  Graphical symbols — Safety colours and safety signs — Registered safety signs”
[Link] “Design and analysis of a virtual factory layout,”  M. Iqbal and M.S.J Hashmi.  Journal of Materials Processing Technology; December 2001.
[Link] “Citizen Guidance on the Homeland Security Advisory System.”  Ready.gov.
[Link] “Coloring the hazards: risk maps research and education to fight health hazards,” J. Mujica.  American Journal of Industrial Medicine; March 1992.
[Link] “Hazard Mapping.” NJ Work Environment Council.
[Link] “Using Workplace and Body mapping tools.”  Irish Congress of Trade Unions.
[Link] “Slips and trips mapping tool - An aid for safety representatives.”  Health and Safety Executive, UK.
[Link] “Using Hazard Maps to Identify and Eliminate Workplace Hazards: A Union-Led Health and Safety Training Program,” Joe Anderson, Michele Collins, John Devlin, and Paul Renner.  NEW SOLUTIONS - A Journal of Environmental and Occupational Health Policy; September 2012.
[Link] “Job Hazard Analysis.”  Occupational Safety and Health Administration, U.S. Department of Labor; 2002.
[Link] “Health and Safety in the Restaurant Industry.”  Interfaith Worker Justice; 2011.
[Link] “Mapping out work hazards.”  centrepages; 1997.
[Link] “Slips, trips and falls mapping.”  Health and Safety Authority, Ireland; 2014.
[Link] “Mapping.”  Tools for Barefoot Research, International Labour Organization; 2002.
[Link] “Risk-Mapping.”  DC 37 Safety & Health Factsheet, District Council 37, New York, New York.

 
Jody W. Phelps, MSc, PMP®, MBA
Principal Consultant
JayWink Solutions, LLC
jody@jaywink.com
]]>
<![CDATA[Learning Games]]>Wed, 30 Dec 2020 15:30:00 GMThttp://jaywinksolutions.com/thethirddegree/learning-games     Training the workforce is a critical responsibility of an organization’s management.  Constant effort is required to ensure that all members are operating according to the latest information and techniques.  Whether training is developed and delivered by internal resources or third-party trainers, more efficacious techniques are always sought.
     Learning games, as we know them, have existed for decades (perhaps even longer than we realize), but are gaining popularity in the 21st century.  Younger generations’ affinity for technology and games, including role-playing games, makes them particularly receptive to this type of training exercise.  Learning games need not be purely digital, however.  In fact, games that employ physical artifacts have significant advantages of their own.
     Several terms may be encountered when researching learning games.  Most include “game,” “simulation,” or a combination of the two.  Broadly-accepted definitions describe games as activities that include an element of competition, while simulations attempt to reproduce aspects of the real world.  For simplicity and brevity, this post will use learning games as an umbrella term covering all variants, including simulation games, serious games, serious simulation games, and so on.
     Learning games have been developed for a variety of topics, while others could benefit from creative developers’ attention.  This discussion focuses on typical Third Degree topics, such as engineering, operations and supply chain management, business strategy, and project management in post-secondary and professional education.
     Learning games can take many forms.  Simulations of critical processes are made as realistic as possible to convey the gravity of the real-world situation it represents and teach participants how to manage the risk involved in order to make prudent decisions.  Games can also teach certain concepts or proper judgment through fantastical presentations; this approach can be particularly useful when faced with a reluctant, unreceptive audience.
     Learning games can be digital, analog, or hybrid.  Computer games are popular with tech-savvy students that are accustomed to sophisticated programs and high-quality graphics.  Highly-sophisticated versions may even employ virtual reality or augmented reality.  Use of highly accurate digital twins can improve a game’s effectiveness.
     Tabletop games utilize physical space and objects in game play.  The layout of a game need not be limited to the size of a table; an entire room can be mapped as a gameboard, allowing people, as well as objects, ample space to move about.  The physical representation of the learning game can be used to impart lessons more tangibly than displays and numbers can achieve.  Concepts of inventory and transportation waste are examples of this type of application.  Hybrid games employ digital and physical elements, seeking the most effective combination to aid learning and retention.
     The level of interaction participants have with a learning game is another important characteristic to consider.  Observational games present participants with a situation and a fixed data set.  The data can be analyzed in various ways and other queries may be possible.  From these analyses, participants draw conclusions and make decisions that are then critiqued by facilitators.  Observational games can be thought of as enhanced case studies; they are somewhat more interactive, and may employ multimedia technology or other embellishments for a more appealing and engaging presentation.
     Experimental games, on the other hand, are highly interactive, allowing participants to modify elements of the game and directly assess the impacts of their decisions on system or process performance.  Multiple analyses can be performed in search of an optimal solution.  The ability to manipulate the system and receive feedback on the effects of each change often leads to deeper understanding of the systems and processes simulated.  The resulting competence improves safety and efficiency of the real-world counterparts when the students become managers.
     Learning games can also be categorized according to their level of complexity.  One such taxonomy [Wood, 2007] includes insight games, analysis games, and capstone games.  Wood’s taxonomy of games is summarized in Exhibit 1Insight games seek to develop understanding of basic concepts and context required for students to comprehend subsequent material and advanced concepts.
     Students develop required skills “through iterations of hypothesis, trial, and assessment” [Wood, 2007] in analysis games.  These games seek to bridge the gap between understanding a concept and performing a related task effectively.  Wood cites the example of riding a bicycle; a student may understand the task by reading its written description, but this does not ensure a safe ride upon first attempt.  Practice is needed to match ability with understanding.
     Capstone games, as you may have guessed, require participants to consider multiple objectives or perspectives.  These games incorporate multiple disciplines in a single game to simulate the complexity of decisions that real-world managers must make.  When conducted as a team exercise, with members of varying background and experience, capstone games can provide a very realistic approximation of situations faced by managers on a regular basis.
Stages of Game Play
     Learning games typically proceed in three stages:  preparation, play, and debriefing.  Players and facilitators bear responsibility for a successful game in each stage.  As is the case with any type of training, engagement of participants is critical to success.
     In the preparation stage, facilitators are responsible for “setting the stage” for game play.  This may include preparing presentations to explain the rules of the game, assigning students to teams within the group, or stocking the physical space with required materials or accommodations.  Players are required to review any information provided in advance and procure any material they are expected to provide.
     During game play, players are expected to remain engaged, maximizing the learning benefit for themselves and others through active participation and knowledge-sharing.  Facilitators enforce rules, such as time limits, and may have to “keep score” as the game progresses.  Facilitators also answer questions, provide guidance to ensure a successful game, and monitor the proceedings for improvement ideas.
     An effective debriefing is essential to a successful learning game.  It is in this stage that participants’ performance is evaluated.  Critiques of decisions made during the game provide participants with valuable insights.  In many game configurations, this is where the greatest learning occurs; it provides an opportunity to learn from the experience of other teams, including situations that may not have occurred in one’s own game.  Facilitators are responsible for providing information participants may need in order to understand why certain decisions are better than others.  Players may assist facilitators in providing critiques and explanations, ensuring that all participants develop the understanding necessary to apply the new information to future real-world scenarios.
 
Benefits of Learning Games
     Learning games provide many benefits to players and the organizations that employ them.  Advantages include the number of people that can be effectively trained, the time and expense required for training, and the risk of poor performance.  The advantages of learning games relative to on-the-job-training is summarized in Exhibit 2, where the “real world” is compared to a learning game environment.
     Learning games may also offer additional benefits related to onboarding, team-building, or unique aspects of your organization.  Consider all possible benefits to be gained when evaluating learning games for your team.
 
Example Learning Games
     While some organizations may use proprietary learning games, many are widely distributed, often for free or at very low cost.  Several learning games are cited below, but only to serve as inspiration.  Assessment of each should be conducted in the context of the group to be trained.  Therefore, the time and space required to provide a thorough review of each would provide little value.  Also, doing so may encourage readers to limit their choices to those games mentioned here which is contrary to the objective of this post.

Physics/Engineering:  Whether your interest is in equations of motion or medieval warfare, the Virtual Trebuchet is for you.  Players define several attributes of the trebuchet, launch a projectile, and observe its trajectory.  Peak height, maximum distance, and other flight data are displayed for comparison of different configurations.  Visually simple, highly educational, and surprisingly fun, even the non-nerds among us can appreciate this one.
Project Management:  The Project Management Institute (PMI) Educational Foundation has developed the Tower Game for players ranging from elementary school students to practicing professionals.  Teams compete to build the “tallest” tower in a fixed time period with a standard set of materials.  “Height bonuses” are earned through resource efficiency.  The game can be customized according to the group’s experience level.
Operations Management:  Considering the complexity of operations management, it is no surprise that these games are among the most sophisticated.  OMG!, the Operations Management Game, is a tabletop game that maps a production process with tablemats for each step.  Each step is represented by a single player; the number of steps can be varied to accommodate groups of different size.  Physical artifacts represent work-in-process (WIP) and finished goods inventories, and dice are used to simulate demand and process variability.  Upgrades are available when sufficient profits have been retained to purchase them.  Many important aspects of operations management are included in this game; it could be a valuable learning tool.
            A camshaft manufacturing process is modelled in Watfactory, a game used to study techniques of variation reduction.  It includes a large number of variables – 60 variable inputs, 30 fixed inputs, and 3 process step outputs – and several investigation options that define data analyses to be performed.  This one is not for beginners or the faint of heart, but is a solid test of skills.
Supply Chain Management:  Revered as the granddaddy of all learning games, the Beer Game was first developed at MIT in the 1960s to explore the bullwhip effect in supply chains.  Since then, many alternate versions have been developed, including virtual ones.
Business StrategyBizMAP is a game that can be used to assess an individual’s aptitude for entrepreneurship or suitability for an executive leadership role.  If one trusts its predictive capability, it could be an extremely valuable aid in averting disasters.  Poor executive decision-making or an ill-advised decision to quit one’s day job can be avoided.
     Many other learning games are available with differing objectives, configurations, and complexity.  Other professional practices, including Managerial Accounting (!) and bridge design can be explored using learning games.  Explore and be amazed!
 
Serious Business
     In some circles, learning games have become serious business.  High-stakes decisions are often based on simulations.  Ever heard of War Games?  The accuracy and reliability of such simulations is a serious matter indeed.
     Less costly in terms of human life, but potentially catastrophic in financial terms, businesses may simulate the competitive landscape in which they operate.  If invalid assumptions are made, or the simulation otherwise misrepresents the competitive marketplace, decisions based on it could be financially ruinous.
     The development of the learning games industry reflects just how serious it has become.  Industry conferences, such as the Serious Games Summit and the Serious Play Conference, are held for serious gamers to share developments with one another.  A professional association – Association for Business Simulation and Experiential Learning (ABSEL) – has also been chartered to serve this growing community.
            In the early 2000s, MIT embarked on its Games to Teach Project, a collaboration aimed at developing games for science and engineering instruction.  Decades after launching the movement, MIT’s ongoing commitment to learning games is reflected in the Scheller Teacher Education Program, in which these tools play a prominent role.
 
     No matter your field of study or level of experience, chances are good that a learning game has been developed for your target demographic.  However, improvements can always be made.  If you are an aspiring programmer or game developer, a learning game is an excellent vehicle for demonstrating your skills while providing value to users.  It will look great on your resume!
 
     If you have a favorite learning game, or an idea for a new one, please tell us about it in the comments section.  If you would like to introduce learning games to your organization, contact JayWink Solutions for additional guidance.
 
References
[Link] “The Role of Computer Games in the Future of Manufacturing Education and Training.”  Sudhanshu Nahata; Manufacturing Engineering, November 2020.
[Link] “Online Games to Teach Operations.”  Samuel C. Wood; INFORMS Transactions on Education, 2007.
[Link] “Game playing and operations management education.”  Michael A. Lewis and Harvey R. Maylor; International Journal of Production Economics, January, 2007.
[Link] “A game for the education and training of production/ operations management.”  Hongyi Sun; Education + Training, December 1998.

 
Jody W. Phelps, MSc, PMP®, MBA
Principal Consultant
JayWink Solutions, LLC
jody@jaywink.com
]]>