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 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.

     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.

     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.

     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.

     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.

     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.

     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.

     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.

     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.

     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.

     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.

     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.

     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.

     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.

     Regardless of the decision-making model used, or how competent and conscientious a decision-maker is, making decisions involves risk.  Some risks are associated with the individual or group making the decision.  Others relate to the information used to make the decision.  Still others are related to the way that this information is employed in the decision-making process.
     Often, the realization of some risks increases the probability of realizing others; they are deeply intertwined.  Fortunately, awareness of these risks and their interplay is often sufficient to mitigate them.  To this end, several decision-making perils and predicaments are discussed below.

     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.

     There is a “universal sequence for quality improvement,” according to the illustrious Joseph M. Juran, that defines the actions to be taken by any team to effect change.  This includes teams pursuing error- and defect-reduction initiatives, variation reduction, or quality improvement by any other description.
            Two of the seven steps of the universal sequence are “journeys” that the team must take to complete its problem-solving mission.  The “diagnostic journey” and the “remedial journey” comprise the core of the problem-solving process and, thus, warrant particular attention.