Reliability Analysis with Minitab

Statistical Analysis for the Reliability Engineering Professional

Effectively conduct reliability analysis using the world’s leading statistical software. Reliability Analysis with Minitab® outlines statistical concepts and applications, explains the theory of probability, reliability analysis, and quality improvement, and provides step-by-step instruction on the use of Minitab. Minitab introduces reliability analysis tools that can be used to perform tasks that range from checking the distribution fit of lifetime data to estimating the warranty costs of a product.

Perform the Analyses Needed to Minimize Product Failures and Reduce Costs

Chock full of examples that include numerous case studies and over 200 screenshots, this book is a comprehensive guide to quality and reliability in the service and manufacturing industries. It illustrates the shapes of the most commonly used statistical distributions in reliability analysis, and in simple language demonstrates concepts that include parametric reliability analysis, nonparametric reliability analysis, warranty analysis, accelerated life testing, reliability test plans, and probit analysis.

Illustrating the application of Minitab for reliability analysis, this book explains how to:

• Perform reliability analysis of a product with right-censored and exact failure time data



• Complete reliability analysis of a product with arbitrarily censored failure time data



• Achieve nonparametric reliability analysis of a product



• Predict the amount of money that is needed to cover the warranty costs for products in a specific period of time in the future



• Analyze the results from accelerated life testing on two different products



• Determine the reliability test sample size when the test time and the number of failures are constrained



• Regulate the testing time when test sample size and the number of failures are constrained



• Compare the reliabilities of parts from different vendors



• Test whether the reliability of a product depends on certain factors



• Predict the stress level at which a product will fail after a certain test period

Kishore K. Pochampally is a professor at Southern New Hampshire University. He teaches lean six sigma, business analytics, project management, and operations management. He holds a Ph.D. in industrial engineering from Northeastern University. He is a Six Sigma Black Belt (ASQ), Project Management Professional (PMP®), and Certified Analytics Professional (CAP®).

Surendra M. Gupta is a professor of mechanical and industrial engineering and the director of the laboratory for responsible manufacturing at Northeastern University. He received his BE from Birla Institute of Technology and Science, MBA from Bryant University, and MSIE and Ph.D. from Purdue University. Dr. Gupta’s research interests span the areas of production/manufacturing systems and operations research. He has authored/coauthored well over 500 technical papers published in books, journals and international conference proceedings which have been cited worldwide by thousands of researchers. He is the recipient of outstanding research, industrial engineering professor, and doctoral dissertation advisor awards.