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.”

     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.

     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.

     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.

     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.

     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.

     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.

     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.

     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.

     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.

     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.

     Of the “eight wastes of lean,” the impacts of defects may be the easiest to understand.  Most find the need to rework or replace a defective part or repeat a faulty service, and the subsequent costs, to be intuitive.  The consequences of excess inventory, motion, or transportation, however, may require a deeper understanding of operations management to fully appreciate.
     Conceptually, poka yoke (poh-kah yoh-keh) is one of the simplest lean tools; at least it was at its inception.  Over time, use of the term has morphed and expanded, increasing misuse and confusion.  The desire to appear enlightened and lean has led many to misappropriate the term, applying it to any mechanism used, or attempt made, to reduce defects.  Poka yoke is often conflated with other process control mechanisms, including engineering controls and management controls.
     To effectively reduce the occurrence of errors and resultant defects, it is imperative that process managers differentiate between poka yoke devices, engineering controls, and management controls.  Understanding the capabilities and limitations of each allows appropriate actions to be taken to optimize the performance of any process.

     Every organization wants error to be kept at a minimum.  The dedication to fulfilling this desire, however, often varies according to the severity of consequences that are likely to result.  Manufacturers miss delivery dates or ship faulty product; service providers fail to satisfy customers or damage their property; militaries lose battles or cause civilian casualties; all increase the cost of operations.
     You probably have some sensitivity to the effects errors have on your organization and its partners.  This series explores strategies, tools, and related concepts to help you effectively combat error and its effects.  This is your induction; welcome to The War on Error.

     While the Rational Model provides a straightforward decision-making aid that is easy to understand and implement, it is not well-suited, on its own, to highly complex decisions.  A large number of decision criteria may create numerous tradeoff opportunities that are not easily comparable.  Likewise, disparate performance expectations of alternatives may make the “best” choice elusive.  In these situations, an additional evaluation tool is needed to ensure a rational decision.
     The scenario described above requires Multi-criteria Analysis (MCA).  One form of MCA is Analytic Hierarchy Process (AHP).  In this installment of “Making Decisions,” application of AHP is explained and demonstrated via a common example – a purchasing decision to source a new production machine.