A precedence diagram is a building block for more advanced techniques in operations and project management. Precedence diagrams are used as inputs to PERT and Gantt charts, line balancing, and Critical Path Method (topics of future installments of “The Third Degree.”)
Many resources discuss precedence diagramming as a component of the techniques mentioned above. However, the fact that it can be used for each of these purposes, and others, warrants a separate treatment of the topic. Separate treatment is also intended to encourage reuse, increasing the value of each diagram created.
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.
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.
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.
FMEA – Vol. V: Alignment
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.
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.
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.
Safety First! Or is it?
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:
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.
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
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.
Troubleshooting a system can be guided by instructions created by its developer or someone with extensive experience operating and maintaining similar systems. Without a specific context, however, a troubleshooting process can be very difficult to describe. There is an enormous number of variables that could potentially warrant consideration. The type of system (mechanical, power transmission, fluid power, electrical, motion control, etc.), operating environment (indoor, outdoor, arid, tropical, arctic, etc.), and severity of duty are only the beginning.
The vast array of systems and situations that could be encountered requires that troubleshooting be learned as a generalized skill. What tool set could be more general, more universally applicable, than our senses of sight, hearing, smell, taste, touch, and the most powerful of all, common sense?
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