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.

     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.

     Previous volumes of “Making Decisions” have alluded to voting processes, but were necessarily lacking in detail on this component of group decision-making.  This volume remedies that deficiency, discussing some common voting systems in use for group decision-making.  Some applications and issues that plague these systems are also considered.
     Although “voting” is more often associated with political elections than decision-making, the two are perfectly compatible.  An election, after all, is simply a group (constituency) voting to decide (elect) which alternative (candidate) to implement (inaugurate).  Many descriptions of voting systems are given in the context of political elections; substituting key words, as shown above, often provides sufficient understanding to employ them for organizational decision-making.

     “Fundamentals of Group Decision-Making” (Vol. IV) addressed structural attributes of decision-making groups.  In this volume, we discuss some ways a group’s activities can be conducted.  An organization may employ several different techniques, at different times, in order to optimize the decision-making process for a specific project or group.
     The following selection of techniques is not comprehensive; organizations may discover others that are useful.  Also, an organization may develop its own technique, often using a commonly-known technique as a foundation on which to create a unique process.  The choice or development of a decision-making process must consider the positive and negative impacts – potential or realized – on decision quality, efficiency, and organizational performance factors.

     In business contexts, many decisions are made by a group instead of an individual.  The same is true for other types of organization as well, such as nonprofits, educational institutions, and legislative bodies.  Group decision-making has its advantages and its disadvantages.  There are several other considerations also relevant to group decision-making, such as selecting members, defining decision rules, and choosing or developing a process to follow.
     Successful group decision-making relies on a disciplined approach that proactively addresses common pitfalls.  If an organization establishes a standard that defines how it will form groups and conduct its decision-making activities, it can reap the rewards of faster, higher-quality decisions, clearer expectations, less conflict, and greater cooperation.

     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.

     The rational model of decision-making feels familiar, intuitive, even obvious to most of us.  This is true despite the fact that few of us follow a well-defined process consistently.  Inconsistency in the process is reflected in poor decision quality, failure to achieve objectives, or undesired or unexpected outcomes.
     Versions of the rational model are available from various sources, though many do not identify the process by this name.  Ranging from four to eight steps, the description of each varying significantly, these sources offer a wide variety of perspectives on the classic sequential decision-making process.  Fundamentally, however, each is simply an interpretation of the rational model of decision-making.

     Given the importance of decision-making in our personal and professional lives, the topic receives shockingly little attention.  The potential consequences of low-quality decisions warrant extensive courses to build critical skills, yet few of us ever receive significant instruction in decision-making during formal education, as part of on-the-job training, or from mentors.  It is even under the radar of many conscientious autodidacts.  The “Making Decisions” series of “The Third Degree” aims to raise the profile of this critical skillset and provide sufficient information to improve readers’ decision-making prowess.
     It is helpful, when beginning to study a new topic, to familiarize oneself with some of the unique terminology that will be encountered.  This installment of “Making Decisions” will serve as a glossary for reference throughout the series.  It also provides a preview of the series content and a directory of published volumes.

     Uses of augmented reality (AR) in various industries has been described in previous installments of “Augmented Reality” (Part 1, Part 2).  In this installment, we will explore AR applications aimed at improving customer experiences in service operations.  Whether creating new service options or improving delivery of existing services, AR has the potential to transform our interactions with service providers.
     Front-office operations are mostly transparent due to customer participation.  Customer presence is a key characteristic that differentiates services from the production of goods.  Thus, technologies employed in service industries are often highly visible.  This can be a blessing or a curse.

     Some of the augmented reality (AR) applications most likely to attract popular attention were presented in “Part 1:  An Introduction to the Technology.”  When employed by manufacturing companies, AR is less likely to be experienced directly by the masses, but may have a greater impact on their lives.  There may be a shift, however, as AR applications pervade product development and end-user activities.
     In this installment, we look at AR applications in manufacturing industries that improve operations, including product development, quality control, and maintenance.  Some are involved directly in the transformation of materials to end products, while others fill supporting roles.  The potential impact on customer satisfaction that AR use provides will also be explored.

     When we see or hear a reference to advanced technologies, many of us think of modern machinery used to perform physical processes, often without human intervention.  CNC machining centers, robotic work cells, automated logistics systems, drones, and autonomous vehicles often eclipse other technologies in our visions.  Digital tools are often overlooked simply because many of us find it difficult to visualize their use in the physical environments we regularly inhabit.
     There is an entire class of digital tools that is rising in prominence, yet currently receives little attention in mainstream discourse:  augmented reality (AR).  There are valid applications of AR in varied industries.  Increased awareness and understanding of these applications and the potential they possess for improving safety, quality, and productivity will help organizations identify opportunities to take the next step in digital transformation, building on predecessor technologies such as digital twins and virtual reality.

     The use of digital technologies in commercial applications is continually expanding.  Improvements in virtual reality (VR) systems have increased the practical range of opportunities for their use across varied industries.
     As discussed with respect to other technologies experiencing accelerated development and expansion, several definitions of “virtual reality” may be encountered.  Researchers and practitioners may disagree on which applications qualify for use of the term.  For our purposes, we will use a simple description of virtual reality:
“Virtual reality” is an experience created, using a digital twin or other model, where

• the user has a first-person perspective (i.e. immersion),
  
• sensory input is limited to the “reality” created (i.e. sensory deprivation and replacement),
• physical phenomena are added to enhance realism (e.g. motion, acceleration, heat), and
• the user can interact with the simulated environment.

     Digital Twin technology existed long before this term came into common use.  Over time, existing technology has advanced, new applications and research initiatives have surfaced, and related technologies have been developed.  This lack of centralized “ownership” of the term or technology has led to the proliferation of differing definitions of “digital twin.”
     Some definitions focus on a specific application or technology – that developed by those offering the definition – presumably to coopt the term for their own purposes.  Arguably, the most useful definition, however, is the broadest – one that encompasses the range of relevant technologies and applications, capturing their corresponding value to the field.  To this end, I offer the following definition of digital twin:
     An electronic representation of a physical entity – product, machine, process, system, or facility – that aids understanding of the entity’s design, operation, capabilities, or condition.