Six Sigma Case Studies with Minitab® by Kishore K. Pochampally

By Kishore K. Pochampally

Six Sigma is among the most generally used caliber development methodologies this present day, either in production and repair industries. Minitab® is a statistical software program package deal that's usually utilized in conjunction with Six Sigma tasks. This booklet illustrates the applying of Minitab® for 6 Sigma initiatives, utilizing case stories in various sectors, reminiscent of healthcare, production, airline, and speedy meals. Detailed Read more...

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Somerset, NJ: John Wiley & Sons. 5th ed. Perry, Randy C. and Bacon, David W. 2006. Commercializing Great Products with Design for Six Sigma. Upper Saddle River, NJ: Prentice Hall. Ryan, Thomas P. 2011. Statistical Methods for Quality Improvement. Somerset, NJ: John Wiley & Sons. 3rd ed. Wedgwood, I. 2006. Lean Sigma: A Practitioner’s Guide. Upper Saddle River, NJ: Prentice Hall. Section II Six Sigma Case Studies 3 Confidence Intervals to Assess Variation in Fat Content at a Fast-Food Restaurant Hamburrgerr, Inc.

16 Graphical summary after improvement. 273 Confidence Intervals to Assess Variation in Fat Content 33 Test and CI for One Variance: Fat Method The chi-square method is only for the normal distribution. The Bonett method is for any continuous distribution. 17 One-sided 95% confidence interval for standard deviation after improvement. 492 gram for the standard deviation (given by the chi-square method). 492 gram. 492 gram is less than the assumption of 1 gram, it is evident that the process has improved with respect to the standard deviation as well.

3 Chi-Square Analysis Chi-square analysis is a type of hypothesis testing where a sample statistic (called chi-square value) used in the test is assumed to follow a chi-square distribution. This technique is explained in detail in Black (2011). Applications of this technique to Six Sigma are illustrated using Minitab® in Chapters 4 and 15. 4 Process Capability Analysis If USL is the upper specification limit for a process, LSL is the lower specification limit for a process, µ is the process mean, and σ is the process standard deviation, the following process capability ratios can measure process performance: First-generation process capability ratio, Cp = USL − LSL 6σ Second-generation process capability ratio with respect to LSL, Cpl = µ − LSL 3σ Second-generation process capability ratio with respect to USL, Cpu = USL − µ 3σ Second-generation process capability ratio, Cpk = MINIMUM of {Cpl , Cpu } The higher the Cp and Cpk values are, the better the process is.

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