Quality Control
Microsoft Word - BZ440V Assignment 8 ASSIGNMENT 08 BZ440 Quality Control Directions: Answer in complete sentences Be sure to use correct English, spelling and grammar. Sources must be cited in APA format. Your response should be four (4) double-spaced pages. Margins 1” all sides Headings Bold Type Style and Size Times New Roman 12-point Software MS Word Today’s business arenas are extremely competitive nationally and internationally. Please thoughtfully consider the following statements and provide answers supported from information in your textbook and include at least two outside sources with appropriate citations. 1. Support a leadership impact ideology for TQ implementation by synthesizing the processes necessary to provide an organization with a TQ paradigm. 2. Assess three (3) characteristics that contribute to a TQ organization by prioritizing the steps necessary to achieve a competitive advantage in the marketplace using TQ methodology. 3. Evaluate how you, as a manger, would integrate three (3) specific leadership initiative tools to provide your company with a TQ organization. Chapter 18.pdf Chapter Eighteen Optimizing and Controlling Processes Through Statistical Process Control Learning Objectives AFTER COMPLETING THIS CHAPTER, YOU SHOULD BE ABLE TO: Define the concept of statistical process control (SPC). Explain the rationale for SPC. List the steps for developing a control chart. Describe management’s role in SPC. Explain the rationale for using the quality tools before employing a control chart. Summarize the authority operators who use SPC have over processes. List the three broad phases of the process for implementing/deploying SPC. List the factors that are the worst inhibitors of SPC. The origin of what is now called statistical process control (SPC) dates back to 1931 and Dr. Walter Shewhart’s book The Economic Control of Quality of Manufactured Product. Shewhart, a Bell Laboratories statistician, was the first to recognize that industrial processes themselves could yield data, which, through the use of statistical methods, could signal that the process was in control or was being affected by special causes (causes beyond the natural, predictable variation). The control charts used today are based on Shewhart’s work. These control charts are the very heart of SPC. What may not be as obvious is that Shewhart’s work became the catalyst for the quality revolution in Japan1 and the entire movement now called total quality. We tend to look at SPC as one piece of the whole total quality picture, and it is, but it is also the genesis of total quality. Since the first edition of this book was written in 1993, two very significant things have occurred in the SPC field. First, many organizations have adopted SPC as a preferred way of controlling manufacturing processes. Much of this has come about as a result of the quality quest by first-tier companies, making it necessary to require that their second-tier suppliers practice SPC. We have seen this ripple down to at least the fourth tier. Nowhere is this more evident than in the auto industry. But even beyond the mandate by corporate customers, more and more small companies are using SPC as part of their quality and competitiveness initiatives. The second big change we have seen is that SPC users have backed away from the shotgun approach, where every process, no matter how trivial or foolproof, had to have SPC charts. Several years ago, we visited a North American semiconductor plant and were overwhelmed by the sheer numbers of SPC charts. Everywhere you looked you saw control charts. The plant proudly admitted to having over 900 processes under control charts. When we visited the same plant a couple of years later, the picture was very different. You could still find control charts, but only where they offered real benefit. The company had discovered that about 800 of its original charts had not been worthwhile. Control charts were being used with those processes that needed them, and no more. It is evident that this is the current thinking in industry. Don’t waste time, energy, and money with more control charts than you need. In those process applications where you do need them, the control chart is invaluable. For all the rest, it is just window dressing. The important thing is to know the difference. Statistical Process Control Defined Although SPC is normally thought of in industrial applications, it can be applied to virtually any process. Everything done in the workplace is a process. All processes are affected by multiple factors. For example, in the workplace a process can be affected by the environment and the machines employed, the materials used, the methods (work instructions) provided, the measurements taken, and the manpower (people) who operate the process—the Five M’s. If these are the only factors that can affect the process output, and if all of these are perfect—meaning the work environment facilitates quality work; there are no misadjustments in the machines; there are no flaws in the materials; and there are totally accurate and precisely followed work instructions, accurate and repeatable measurements, and people who work with extreme care, following the work instructions perfectly and concentrating fully on their work—and if all of these factors come into congruence, then the process will be in statistical control. This means that there are no special causes adversely affecting the process’s output. Special causes are (for the time being, anyway) eliminated. Does that mean that 100% of the output will be perfect? No, it does not. Natural variation is inherent in any process, and it will affect the output. Natural variation is expected to account for roughly 2,700 out-of-limits parts in every 1 million produced by a three-sigma process (±3σ variation), 63 out-of-limits parts in every 1 million produced by a four-sigma process, and so on. Natural variation, if all else remains stable, will account for two out-of-limits parts per billion produced by a true six-sigma process. SPC does not eliminate all variation in the processes, but it does something that is absolutely essential if the process is to be consistent and if the process is to be improved. SPC allows workers to separate the special causes of variation (e.g., environment and the Five M’s) from the natural variation found in all processes. After the special causes have been identified and eliminated, leaving only natural variation, the process is said to be in statistical control (or simply in control). When that state is achieved, the process is stable, and in a three-sigma process, 99.73% of the output can be counted on to be within the statistical control limits. More important, improvement can begin. From this, we can develop a definition of statistical process control: Statistical process control (SPC) is a statistical method of separating variation resulting from special causes from variation resulting from natural causes in order to eliminate the special causes and to establish and maintain consistency in the process, enabling process improvement. NOTE: As explained in Chapter 1, the six-sigma numbers given in this section differ from the Motorola Six Sigma numbers (2 parts per billion vs. 3.4 parts per million). Rationale for SPC The rationale for SPC is much the same as that for total quality. It should not be surprising that the parallel exists because it was Walter Shewhart’s work that inspired the Japanese to invite W. Edwards Deming to help them get started in their quality program in 1949 to 1950. SPC was the seed from which the Japanese grew total quality. The rationale for the Japanese to embrace SPC in 1950 was simple: a nation trying to recover from the loss of a costly war needed to export manufactured goods so that it could import food for its people. The Asian markets once enjoyed by Japan had also been rendered extinct by the war. The remaining markets, principally North America, were unreceptive to Japanese products because of poor quality. If the only viable markets rejected Japanese products on the basis of quality, then Japanese manufacturers had to do something about their quality problem. This is why Shewhart’s work interested them. This also is why they called on Deming, and later Joseph Juran, to help them. That the effort was successful is well documented and manifestly evident all over the world. Deming told the Japanese industrialists in 1950 that if they would follow his teaching, they could become active players in the world markets within five years. They actually made it in four years. The Western world may not be in the same crisis Japan experienced following World War II, but the imperative for SPC is no less crucial. When one thinks of quality products today, Japan still comes to mind first. Many of the finest consumer products in the world come from Japan. That includes everything from electronics and optical equipment to automobiles, although U.S., European, and Korean car manufacturers effectively eliminated the quality gap as of 2010.2 They have done this by adopting such total quality strategies as SPC. As we approached the twenty-first century, the Japanese were the quality leaders in every level of the automobile market. Cars made by Toyota, Nissan, Honda, and Mazda (including those produced in their North American factories) were of consistently excellent quality. But manufacturers outside of Japan also adopted SPC and other total quality strategies, and the outcome of the race for quality leadership can no longer be predicted for each new product year. For example, in J. D. Power and Associates 2018 Initial Quality Study, the 10 top-rated brands in its U.S. Nameplate Quality Study were Genesis, Kia, and Hyundai (Korean), Porsche (German), Ford, Chevrolet and Lincoln (American), Lexus (Japanese), Ram (American), and Nissan (Japanese). That is three from Korea, one from Germany, four from the United States, and two from Japan. Automakers know that consumers pay close attention to these and other quality ratings and that there is an impact, positive or negative, on sales. Thus, the rationale for automakers to embrace SPC has been not only to improve product quality