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.
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.
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.
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.
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:
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.
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.
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