1.Review the section \"Statistical Process Control Defined.\" Explain how environment and the Five M's can affectprocesses used in the following: Only pick one of the following a. A hardware store b. A hospital c. An accounting firm d. A newspaper e. A factory f. A new-car dealership 2.Comment on the significance of this statement: \"Control chart parameters must be statistically derived and cannot simply be specifications or some arbitrary values that are based on production expectations.\" 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 processthe Five M's. If these are the only factors that can affect the process output, and if all of these are perfectmeaning 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 workand 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 outoflimits parts in every 1 million produced by a 3sigma process (3variation), 63 outoflimits parts in every 1 million produced by a 4sigma process, and so on. Natural variation, if all else remains stable, will account for 2 outoflimits parts per billion produced by a true 6sigma 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 3sigma 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 fromnatural 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 6-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 have 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 twentyfirst 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 2010 Initial Quality Study, the toprated cars in its ten cartype segments were four from Japan, three from the United States, two from Europe, and one from Korea. 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 not only been to improve product quality and simultaneously reduce costs, but also to improve product image in order to compete successfully in the world's markets. The same is true for virtually all industries. To comprehend how SPC can help accomplish this, it is necessary to examine five key points and understand how SPC comes into play in each one: control of variation, continual improvement,predictability of processes, elimination of waste, and product inspection. These points are discussed next. Rationale: Control of Variation The output of a process that is operating properly can be graphed as a bellshaped curve, as in Figure 18.1. The horizontal x-axis represents some measurement, such as weight or dimension, and the vertical y-axis represents the frequency count of the measurements, that is, the number of times that particular measurement value is repeated. The desired measurement value is at the center of the curve, and any variation from the desired value results in displacement to the left or right of the center of the bell. With no special causes acting on the process, 99.73% of the process output will be between the 3 limits. (This is not a specification limit, which may be tighter or looser.) This degree of variation about the center is the result of natural causes. The process will be consistent at this performance level as long as it is free of special causes of variation. When a special cause is introduced, the curve will take a new shape, and variation can be expected to increase, lowering output quality. Figure 18.2 shows the result of a machine no longer capable of holding the required tolerance, or an improper work instruction. The bell is flatter, meaning that fewer parts produced by the process are at, or close to, the target, and more fall outside the original 3s limits. The result is more scrap, higher cost, and inconsistent product quality. Figure 18.1 Frequency Distribution Curve: Normal Curve. Figure 18.2 Frequency Distribution Curve: Process Not as Precise as Figure 18.1. The curve of Figure 18.3 could be the result of input material from different vendors (or different batches) that is not at optimal specification. Again, a greater percentage of the process output will be displaced from the ideal, and more will be outside the original 3slimits. The goal should be to eliminate the special causes so the process operates in accordance with the curve shown in Figure 18.1 and then to improve the process, thereby narrowing the curve (see Figure 18.4). Figure 18.3 Bimodal Frequency Distribution Curve. When the curve is narrowed, more of the process output is in the ideal range, and less falls outside the original 3s limits. Actually, each new curve will have its own 3s limits. In the case of Figure 18.1, they will be much narrower than the original ones. If the original limits resulted in 2,700 pieces out of 1 million being scrapped, the improved process illustrated by Figure 18.4 might reduce that to 270 pieces, or even less, scrapped. Viewed from another perspective, the final product will be more consistently of high quality, and the chance of a defective product going to a customer is reduced by an order of magnitude. Figure 18.4 Frequency Distribution Curve: Narrowed (Less Variation) Relative to Figure 18.1. Variation in any process is the enemy of quality. As we have already discovered, variation results from two kinds of causes: special causes and natural causes. Both kinds can be treated, but they must be separated so that the special causesthose associated with environment and the Five M's can be identified and eliminated. After that is done, the processes can be improved, never eliminating the natural variation but continually narrowing its range and approaching perfection. It is important to understand why the special causes must first be eliminated. Until that happens, the process will not be stable, and the output will include too much product that is unuseable, therefore wasted. The process will not be dependable in terms of quantity or quality. In addition, it will be pointless to attempt improvement of the process because one can never tell whether the improvement is successfulthe results will be masked by the effect of any special causes that remain. In this context, elimination of special causes is not considered to be process improvement, a point frequently lost on enthusiastic improvement teams. Elimination of special causes simply lets the process be whatever it will be in keeping with its natural variation. It may be good or bad or anything in between. When thinking of SPC, most people think of control charts. If we wish to include the elimination of special causes as a part of SPC, as we should, then it is necessary to include more than the control chart in our set of SPC tools because the control chart has limited value until the process is purged of special causes. If one takes a broad view of SPC, all of the statistical tools discussed in Chapter 15 should be included. Pareto charts, cause-and-effect diagrams, stratification, check sheets, histograms, scatter diagrams, and run charts are all SPC tools. AlthoughIt is also important to note that although the flowchart is not a statistical device, it is useful in SPC. The SPC uses of these techniques are highlighted later in this chapter. Suffice it to say that the flowchart is used to understand the process better, the cause-and-effect diagram is used to examine special causes and how they impact the process, and the others are used to determine what special causes are at play and how important they are. The use of such tools and techniques makes possible the control of variation in any process to a degree unheard of before the introduction of SPC. Rationale: Continual Improvement Continual improvement is a key element of total quality. One talks about improvement of products, whatever they may be. In most cases, it would be more accurate to talk about continual improvement in terms of processes than in terms of products and services. It is usually the improvement of processes that yields improved products and services. Those processes can reside in the engineering department, where the design process may be improved by adding concurrent engineering and design-for-manufacture techniques, or in the public sector, where customer satisfaction becomes a primary consideration. All people use processes, and all people are customers of processes. A process that cannot be improved is rare. We have not paid sufficient attention to our processes. Most people have only a general idea of what processes are, how they work, what external forces affect them, and how capable they are of doing what is expected of them. Indeed, outside the manufacturing industry, many people don't realize that their work is made up of processes. Before a process can be improved, it is necessary to understand it, identify the external factors that may generate special causes of variation, and eliminate any special causes that are in play. Then, and only then, can we observe the process in operation and determine its natural variation. Once a process is in this state of statistical control, it can be tracked, using control charts, for any trends or newly introduced special causes. Process improvements can be implemented and monitored. Without SPC, process improvement takes on a hit-or-miss methodology, the results of which are often obscured by variation stemming from undetected factors