Statistical Methods For Spc And Tqm
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Statistical Methods for SPC and TQM
Statistical Methods for SPC and TQM sets out to fill the gap for those in statistical process control (SPC) and total quality management (TQM) who need a practical guide to the logical basis of data presentation, control charting, and capability indices. Statistical theory is introduced in a practical context, usually by way of numerical examples. Several methods familiar to statisticians have been simplified to make them more accessible. Suitable tabulations of these functions are included; in several cases, effective and simple approximations are offered. Contents Data Collection and Graphical Summaries Numerical Data Summaries-Location and Dispersion Probability and Distribution Sampling, Estimation, and Confidence Sample Tests of Hypothesis; "Significance Tests" Control Charts for Process Management and Improvement Control Charts for Average and Variation Control Charts for "Single-Valued" Observations Control Charts for Attributes and Events Control Charts: Problems and Special Cases Cusum Methods Process Capability-Attributes, Events, and Normally Distributed Data Capability; Non-Normal Distributions Evaluating the Precision of a Measurement System (Gauge Capability) Getting More from Control Chart Data SPC in "Non-Product" Applications Appendices
Six Sigma: SPC and TQM in Manufacturing and Services
’Six Sigma’ is a quality concept initially used by Motorola and then developed beyond manufacturing by GE Capital. It has underpinned that organisation's phenomenal success. In essence it is a way of scientifically measuring your current success (or failure) rate in relation to customer expectations (regardless of industry sector) and then ensuring that improvements are made. Six Sigma is the application of a new model of working, statistical measurement and process control to TQM. The ’normal’ position of businesses today is three sigma (or a defect rate of about 5-10%); six sigma - a defect rate of less than four in every million - is the ultimate goal. This book explains the concept of Six Sigma and its statistical basis; explains the benefits which can be achieved by using it in any company and then outlines how to implement such a programme.
Introduction to Statistical Process Control
A major tool for quality control and management, statistical process control (SPC) monitors sequential processes, such as production lines and Internet traffic, to ensure that they work stably and satisfactorily. Along with covering traditional methods, Introduction to Statistical Process Control describes many recent SPC methods that improve upon the more established techniques. The author—a leading researcher on SPC—shows how these methods can handle new applications. After exploring the role of SPC and other statistical methods in quality control and management, the book covers basic statistical concepts and methods useful in SPC. It then systematically describes traditional SPC charts, including the Shewhart, CUSUM, and EWMA charts, as well as recent control charts based on change-point detection and fundamental multivariate SPC charts under the normality assumption. The text also introduces novel univariate and multivariate control charts for cases when the normality assumption is invalid and discusses control charts for profile monitoring. All computations in the examples are solved using R, with R functions and datasets available for download on the author’s website. Offering a systematic description of both traditional and newer SPC methods, this book is ideal as a primary textbook for a one-semester course in disciplines concerned with process quality control, such as statistics, industrial and systems engineering, and management sciences. It can also be used as a supplemental textbook for courses on quality improvement and system management. In addition, the book provides researchers with many useful, recent research results on SPC and gives quality control practitioners helpful guidelines on implementing up-to-date SPC techniques.