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.
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:
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.
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.
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.
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.
The origin of the spaghetti diagram – when and where it was first used or who first recognized its resemblance to a plate of pasta – is not well known. What is clear is that this simple tool can be a very powerful representation of waste in various processes. An easily-understood visual presentation often provides the impetus needed for an organization to advance its improvement efforts.
While flow charts (see Vol. II) depict logical progressions through a process, spaghetti diagrams illustrate physical progressions. The movements tracked may be made by people, materials, paperwork, or other entities. As is the case with other maps, spaghetti diagrams can be created in very simple form, with information added as improvement efforts advance.
Since the dawn of the industrial age, manufacturers have sought ways to improve their operations. Over time, these attempts became more sophisticated, as techniques and models for the measurement of performance were developed.
Performance measurement for service industries is a much more recent development. Fortunately, much of the pioneering work in performance measurement undertaken in manufacturing industries is also applicable to service providers. However, some techniques require adaptation to the unique operating characteristics of service industries to provide the full benefit of the monitoring tools.
Overall Equipment Effectiveness (OEE) is a case in point. OEE could be used to track performance of equipment used to provide a service. It is much more informative of the core objectives of the operation, however, to use the analogous Overall Service Effectiveness (OSE). As the name implies, it provides a “big picture” view of the quality of service provided to customers.
Introduced nearly a century ago, flow charts are one of the most basic mapping tools available; they are also very useful. As such, they have become ubiquitous, though the name used may vary slightly – flow diagram, process map, etc. When packaged with a PFMEA and Control Plan, it is a Process Flow Diagram (PFD). Extensions of the original flow chart have also been developed, identified with new aliases for what is, at its core, a process flow chart.
The variations need not be a distraction; a basic flow chart can be very useful to your organization. Once a basic chart is available, it can be expanded or modified to suit your needs as you learn and gain experience. The following discussion demonstrates this progression.
“Beware the Metrics System – Part 1” presented potential advantages of implementing a metrics system, metric classifications, and warnings of potential pitfalls. This installment will provide examples from diverse industries and recommendations for development and management of metrics systems.
Every business uses metrics to assess various aspects of its performance. Some – usually the smallest and least diversified – may focus exclusively on the most basic financial measures. Others may be found at the opposite end of the spectrum, tracking a multitude of metrics across the entire organization – finance, operations, sales & marketing, human resources, research & development, and so on. The more extensively metricated organization is not necessarily more efficiently operated or more effectively managed, however. The administration of a metrics system incurs costs that must be balanced with its utility for it to be valuable to an organization.
An efficacious metrics system can greatly facilitate an organization’s management and improvement; a misguided one can be detrimental, in numerous ways, to individuals, teams, and the entire organization. The structure of a well-designed metrics system is influenced by the nature of the organization to be monitored – product vs. service, for-profit vs. nonprofit, public vs. private, large vs. small, start-up vs. mature, etc. Organizations often choose to present their metrics systems according to popular templates – Management by Objectives (MBO), Key Performance Indicators (KPI), Objectives and Key Results (OKR), or Balanced Scorecard – but may choose to create a unique system or a hybrid. No matter what form it takes, or what name it is given, the purpose of a metrics system remains constant: to monitor and control – that is, to manage – the organization’s performance according to criteria its leaders deem relevant.
Businesses that provide great customer service can be identified by observing the behavior of their customers. Do customers patronize a business by default, or do they explore all other options first? Do customers enthusiastically recommend a business to friends, family, and colleagues? Do customers react to performance claims made by the business with deep skepticism? If patrons have genuinely positive feelings about their interactions with a service provider, it is an indicator that the company provides great customer service.
The objective of great customer service is to produce loyal customers – those that return regularly, and bring others with them, without significant additional effort or expenditure. Customer acquisition costs can become burdensome if customer retention rates are low. Improving customer service quality is a cost-effective approach to increasing customer retention (i.e. loyalty), thereby reducing customer acquisition costs.
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