The Preference Ranking Organization METHods for Enrichment Evaluations (PROMETHEE) resemble previously-presented decision-making tools in some fundamental ways.  For example, it is a set of compensatory assessments employing pairwise comparisons.  However, substantial differences in PROMETHEE provide advantages not offered by other methods and, thus, warrant discussion.
            The use of various preference functions in PROMETHEE enable evaluations of criteria that more-accurately reflect the ways decision-makers think about their choices.  The determination of the “net desirability” of each alternative also differs from other compensatory methods discussed.  PROMETHEE methods are presented in this installment of the “Making Decisions” series, highlighting these key differences.

            ORESTE is a prioritization and decision-making tool that evaluates alternatives according to their deviations from a hypothetical ideal solution.  The acronym, translated from French, stands for “organization, storage, and synthesis of relational data.”  The method establishes a hierarchy of criteria that is then used to determine a hierarchy of alternatives.
            In this installment, a relatively-simple analysis is used to demonstrate the ORESTE method.  The hypothetical machine purchase of previous installments is revisited, maintaining continuity that facilitates comparison of methods.  A potential pitfall of the method and recovery options are also discussed.

            Decision-making tools vary in sophistication and in their applicability to some types of decisions.  The Fuller Triangle (FT) method is relatively simple; however, this comes with a tradeoff.  Similar to Analytic Hierarchy Process (AHP), presented in Vol. III (6May 2020) of this series, but less rigorous, FT may be less suitable to highly consequential or complex decisions.
            Multicriteria analysis (MCA) tools facilitate organization of decision information for several alternatives and attributes.  When the rigor of a complex method is not required and a decision is needed quickly, FT may be an excellent compromise.  In this installment of Making Decisions, the Fuller Triangle is presented, including some comparisons to AHP to facilitate selection of a decision-making aid.

     The work balance chart is a critical component of a line balancing effort.  It is both the graphical representation of the allocation of task time among operators, equipment, and transfers in a manufacturing or service process and a tool used to achieve an equal distribution.
     Like other tools discussed in “The Third Degree,” a work balance chart may be referenced by other names in the myriad resources available.  It is often called an operator balance chart, a valid moniker if only manual tasks are considered.  It is also known as a Yamazumi Board.  “Yamazumi” is Japanese for “stack up;” this term immediately makes sense when an example chart is seen, but requires an explanation to every non-Japanese speaker one encounters.  Throughout the following presentation, “work balance chart,” or “WBC,” is used to refer to this tool and visual aid.  This term is the most intuitive and characterizes the tool’s versatility in analyzing various forms of “work.”

     A cause & effect diagram is best conceptualized as a specialized application and extension of an affinity diagram.  Both types of diagram can be used for proactive (e.g. development planning) or reactive (e.g. problem-solving) purposes.  Both use brainstorming techniques to collect information that is sorted into related groups.  Where the two diverge is in the nature of relationships represented.
     An affinity diagram may present several types of relationships among pieces of information collected.  A cause & effect diagram, in contrast, is dedicated to a single relationship and its “direction,” namely, what is cause and what is effect.

     An affinity diagram may stretch the definition of “map,” but perhaps not as much as it first appears.  Affinity diagrams map regions of thought, or attention, within a world of unorganized data and information.
     Mapping regions of thought in an affinity diagram can aid various types of projects, including product or service development, process development or troubleshooting, logistics, marketing, and safety, health, and environmental sustainability initiatives.  In short, nearly any problem or opportunity an organization faces can benefit from the use of this simple tool.
     Numerous resources providing descriptions of affinity diagrams are available.  It is the aim of “The Third Degree” to provide a more helpful resource than these often bland or confusing articles by adding nuance, insight, and tips for effective use of the tools discussed in these pages.  In this tradition, the discussion of affinity diagrams that follows presents alternative approaches with the aim of maximizing the tool’s utility to individuals and organizations.

     Most manufactured goods are produced and distributed to the marketplace where consumers are then sought.  Services, in contrast, are not “produced” until there is a “consumer.”  Simultaneous production and consumption is a hallmark of service; no inventory can be accumulated to compensate for fluctuating demand.
     Instead, demand must be managed via predictable performance and efficiency.  A service blueprint documents how a service is delivered, delineating customer actions and corresponding provider activity.  Its pictorial format facilitates searches for improvements in current service delivery and identification of potential complementary offerings.  A service blueprint can also be created proactively to optimize a delivery system before a service is made available to customers.

     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.

     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.”

     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.

     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.

     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.

     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.

     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.

     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.

     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.

     Many organizations adopt the “Safety First!” mantra, but what does it mean?  The answer, of course, differs from one organization, person, or situation to another.  If an organization’s leaders truly live the mantra, its meaning will be consistent across time, situations, and parties involved.  It will also be well-documented, widely and regularly communicated, and supported by action.
     In short, the “Safety First!” mantra implies that an organization has developed a safety culture.  However, many fall far short of this ideal; often it is because leaders believe that adopting the mantra will spur the development of safety culture.  In fact, the reverse is required; only in a culture of safety can the “Safety First!” mantra convey a coherent message or be meaningful to members of the organization.

     Choosing effective strategies for waging war against error in manufacturing and service operations requires an understanding of “the enemy.”  The types of error to be combatted, the sources of these errors, and the amount of error that will be tolerated are important components of a functional definition (see Vol. I for an introduction).
     The traditional view is that the amount of error to be accepted is defined by the specification limits of each characteristic of interest.  Exceeding the specified tolerance of any characteristic immediately transforms the process output from “good” to “bad.”  This is a very restrictive and misleading point of view.  Much greater insight is provided regarding product performance and customer satisfaction by loss functions.

     Myriad tools have been developed to aid collaboration of team members that are geographically separated.  Temporally separated teams receive much less attention, despite this type of collaboration being paramount for success in many operations.
     To achieve performance continuity in multi-shift operations, an effective pass-down process is required.  Software is available to facilitate pass-down, but is not required for an effective process.  The lowest-tech tools are often the best choices.  A structured approach is the key to success – one that encourages participation, organization, and consistent execution.

     A person’s first interaction with a business is often his/her experience in its parking lot.  Unless an imposing edifice dominates the landscape, to be seen from afar, a person’s first impression of what it will be like to interface with a business is likely formed upon entering the parking lot.  It is during this introduction to the facility and company that many expectations are formed. “It” starts in the parking lot.  “It” is customer satisfaction.

     There is some disagreement among quality professionals whether or not precontrol is a form of statistical process control (SPC).  Like many tools prescribed by the Shainin System, precontrol’s statistical sophistication is disguised by its simplicity.  The attitude of many seems to be that if it isn’t difficult or complex, it must not be rigorous.
     Despite its simplicity, precontrol provides an effective means of process monitoring with several advantages (compared to control charting), including:

• It is intended for use on the shop floor for rapid feedback and correction.
• Process performance can be evaluated with far fewer production units.
• No calculations are required to perform acceptance evaluations.
• No charts are required.
• It is not based on an assumption that data is normally distributed.
• It is straightforward, based on specification limits.
• It uses a simple, compact set of decision rules.

     This installment of “The War on Error” explains the use of precontrol – how process monitoring zones are established, the decision rules that guide responses to sample measurements, and the fundamental requirements of implementation.  Some potential modifications will also be introduced.

     Lesser known than Six Sigma, but no less valuable, the Shainin System is a structured program for problem solving, variation reduction, and quality improvement.  While there are similarities between these two systems, some key characteristics lie in stark contrast.
     This installment of “The War on Error” introduces the Shainin System, providing background information and a description of its structure.  Some common problem-solving tools will also be described.  Finally, a discussion of the relationship between the Shainin System and Six Sigma will be presented, allowing readers to evaluate the potential for implementation of each in their organizations.

     Despite the ubiquity of corporate Six Sigma programs and the intensity of their promotion, it is not uncommon for graduates to enter industry with little exposure and less understanding of their administration or purpose.  Universities that offer Six Sigma instruction often do so as a separate certificate, unintegrated with any degree program.  Students are often unaware of the availability or the value of such a certificate.
     Upon entering industry, the tutelage of an invested and effective mentor is far from guaranteed.  This can curtail entry-level employees’ ability to contribute to company objectives, or even to understand the conversations taking place around them.  Without a structured introduction, these employees may struggle to succeed in their new workplace, while responsibility for failure is misplaced.
     This installment of “The War on Error” aims to provide an introduction sufficient to facilitate entry into a Six Sigma environment.  May it also serve as a refresher for those seeking reentry after a career change or hiatus.

     While Vol. IV focused on variable gauge performance, this installment of “The War on Error” presents the study of attribute gauges.  Requiring the judgment of human appraisers adds a layer of nuance to attribute assessment.  Although we refer to attribute gauges, assessment may be made exclusively by the human senses.  Thus, analysis of attribute gauges may be less intuitive or straightforward than that of their variable counterparts.
     Conducting attribute gauge studies is similar to variable gauge R&R studies.  The key difference is in data collection – rather than a continuum of numeric values, attributes are evaluated with respect to a small number of discrete categories.  Categorization can be as simple as pass/fail; it may also involve grading a feature relative to a “stepped” scale.  The scale could contain several gradations of color, transparency, or other visual characteristic.  It could also be graded according to subjective assessments of fit or other performance characteristic.

     While you may have been hoping for rest and relaxation, the title actually refers to Gauge R&R – repeatability and reproducibility.  Gauge R&R, or GRR, comprises a substantial share of the effort required by measurement system analysis.  Preparation and execution of a GRR study can be resource-intensive; taking shortcuts, however, is ill-advised.  The costs of accepting an unreliable measurement system are long-term and far in excess of the short-term inconvenience caused by a properly-conducted analysis.
     The focus here is the evaluation of variable gauges.  Prerequisites of a successful GRR study will be described and methodological alternatives will be defined.  Finally, interpretation of results and acceptance criteria will be discussed.