IMPLEMENTING SPC in .NET

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16-12 IMPLEMENTING SPC
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Focus on continuous improvement. Constantly try to improve the production and service system. Involve the workforce in these activities and make use of statistical methods, particularly the SPC problem-solving tools discussed in the previous section. Practice modern training methods and invest in training for all employees. Everyone should be trained in the technical aspects of their job, as well as in modern quality- and productivity-improvement methods. The training should encourage all employees to practice these methods every day. Practice modern supervision methods. Supervision should not consist merely of passive surveillance of workers, but should be focused on helping the employees improve the system in which they work. The rst goal of supervision should be to improve the work system and the product. Drive out fear. Many workers are afraid to ask questions, report problems, or point out conditions that are barriers to quality and effective production. In many organizations the economic loss associated with fear is large; only management can eliminate fear. Break down the barriers between functional areas of the business. Teamwork among different organizational units is essential for effective quality and productivity improvement to take place. Eliminate targets, slogans, and numerical goals for the workforce. A target such as zero defects is useless without a plan as to how to achieve this objective. In fact, these slogans and programs are usually counterproductive. Work to improve the system and provide information on that. Eliminate numerical quotas and work standards. These standards have historically been set without regard to quality. Work standards are often symptoms of management s inability to understand the work process and to provide an effective management system focused on improving this process. Remove the barriers that discourage employees from doing their jobs. Management must listen to employee suggestions, comments, and complaints. The person who is doing the job is the one who knows the most about it, and usually has valuable ideas about how to make the process work more effectively. The workforce is an important participant in the business, and not just an opponent in collective bargaining. Institute an ongoing program of training and education for all employees. Education in simple, powerful statistical techniques should be mandatory for all employees. Use of the basic SPC problem-solving tools, particularly the control chart, should become widespread in the business. As these charts become widespread, and as employees understand their uses, they will be more likely to look for the causes of poor quality and to identify process improvements. Education is a way of making everyone partners in the quality-improvement process. Create a structure in top management that will vigorously advocate the rst 13 points.
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As we read Deming s 14 points, we notice two things. First, there is a strong emphasis on change. Second, the role of management in guiding this change process is of dominating importance. But what should be changed, and how should this change process be started For example, if we want to improve the yield of a semiconductor manufacturing process, what should we do It is in this area that statistical methods most frequently come into play. To improve the semiconductor process, we must determine which controllable factors in the process
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CHAPTER 16 STATISTICAL QUALITY CONTROL
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in uence the number of defective units produced. To answer this question, we must collect data on the process and see how the system reacts to changes in the process variables. Statistical methods, including the SPC and experimental design techniques in this book, can contribute to this knowledge.
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SUPPLEMENTAL EXERCISES
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16-43. The diameter of fuse pins used in an aircraft engine application is an important quality characteristic. Twenty- ve samples of three pins each are shown as follows: (e) To make this process a six-sigma process, the variance 2 would have to be decreased such that PCRk 2.0. What should this new variance value be (f) Suppose the mean shifts to 64.01. What is the probability that this shift will be detected on the next sample What is the ARL after the shift 16-44. Rework Exercise 16-43 with X and S charts. 16-45. Plastic bottles for liquid laundry detergent are formed by blow molding. Twenty samples of n 100 bottles are inspected in time order of production, and the fraction defective in each sample is reported. The data are as follows: Sample 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Fraction Defective 0.12 0.15 0.18 0.10 0.12 0.11 0.05 0.09 0.13 0.13 0.10 0.07 0.12 0.08 0.09 0.15 0.10 0.06 0.12 0.13
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Sample Number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 64.030 63.995 63.988 64.002 63.992 64.009 63.995 63.985 64.008 63.998 63.994 64.004 63.983 64.006 64.012 64.000 63.994 64.006 63.984 64.000 63.988 64.004 64.010 64.015 63.982
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Diameter 64.002 63.992 64.024 63.996 64.007 63.994 64.006 64.003 63.995 74.000 63.998 64.000 64.002 63.967 64.014 63.984 64.012 64.010 64.002 64.010 64.001 63.999 63.989 64.008 63.984 64.019 64.001 64.021 63.993 64.015 63.997 63.994 63.993 64.009 63.990 63.994 64.007 63.998 63.994 63.998 64.005 63.986 64.018 64.003 64.013 64.009 63.990 63.990 63.993 63.995
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(a) Set up X and R charts for this process. If necessary, revise limits so that no observations are out-of-control. (b) Estimate the process mean and standard deviation. (c) Suppose the process speci cations are at 64 0.02. Calculate an estimate of PCR. Does the process meet a minimum capability level of PCR 1.33 (d) Calculate an estimate of PCRk. Use this ratio to draw conclusions about process capability.
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(a) Set up a P chart for this process. Is the process in statistical control (b) Suppose that instead of n 100, n 200. Use the data given to set up a P chart for this process. Revise the control limits if necessary. (c) Compare your control limits for the P charts in parts (a) and (b). Explain why they differ. Also, explain why your assessment about statistical control differs for the two sizes of n.
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