Handbook of multivariate process capability indices / Ashis Kumar Chakraborty, Moutushi Chatterjee.
By: Chakraborty, Ashis K [author.].
Contributor(s): Chatterjee, Moutushi [author.].
Material type: BookPublisher: Boca Raton : CRC Press, 2021Edition: First edition.Description: 1 online resource (xviii, 334 pages) : illustrations.Content type: text Media type: computer Carrier type: online resourceISBN: 9780429298349; 042929834X; 9781000222081; 100022208X; 9781000222036; 1000222039; 9781000222135; 1000222136.Subject(s): BUSINESS & ECONOMICS / Quality Control | MATHEMATICS / Probability & Statistics / General | TECHNOLOGY / Quality Control | Process control -- Statistical methods | Multivariate analysis -- Data processingDDC classification: 658.56201519535 Online resources: Taylor & Francis | OCLC metadata license agreement1 Introduction2 Some Useful Concepts of Univariate and Multivariate Statistics3 Univariate Process Capability Indices4 Bi-variate Process Capability Indices (BPCIs)5 Multivariate Process Capability Indices for Bilateral Specification Region Based on Principal Component Analysis (PCA)6 Ratio Based Multivariate Process Capability Indices for Symmetric Specification Region7 Multivariate Process Capability Indices for Asymetric Specification Region8 Multivariate Process Capability Indices for Unilateral Specification Region9 Multivariate Process Capability Indices Based on Proportion of Non-conformance10 Multivariate Process Capability Indices for Quality Characteristics Having Non-Normal Statistical Distributions11 Multivariate Process Capability Indices Based on Bayesian Approach12 Multivariate Process Capability Indices for Auto-correlated Data13 Multivariate Process Capability Vectors14 MPCIs Defined by Other Miscellaneous Approaches15 Applications of MPCIs
Providing a single-valued assessment of the performance of a process is often one of the greatest challenges for a quality professional. Process Capability Indices (PCIs) precisely do this job. For processes having a single measurable quality characteristic, there is an ample number of PCIs, deαned in literature. The situation worsens for multivariate processes, i.e., where there is more than one correlated quality characteristic. Since in most situations quality professionals face multiple quality characteristics to be controlled through a process, Multivariate Process Capability Indices (MPCIs) become the order of the day. However, there is no book which addresses and explains di?erent MPCIs and their properties. The literature of Multivariate Process Capability Indices (MPCIs) is not well organized, in the sense that a thorough and systematic discussion on the various MPCIs is hardly available in the literature. Handbook of Multivariate Process Capability Indices provides an extensive study of the MPCIs deαned for various types of speciαcation regions. This book is intended to help quality professionals to understand which MPCI should be used and in what situation. For researchers in this αeld, the book provides a thorough discussion about each of the MPCIs developed to date, along with their statistical and analytical properties. Also, real life examples are provided for almost all the MPCIs discussed in the book. This helps both the researchers and the quality professionals alike to have a better understanding of the MPCIs, which otherwise become di?cult to understand, since there is more than one quality characteristic to be controlled at a time. Features: A complete guide for quality professionals on the usage of di?erent MPCIs. A step by step discussion on multivariate process capability analysis, starting from a brief discussion on univariate indices. A single source for all kinds of MPCIs developed so far. Comprehensive analysis of the MPCIs, including analysis of real-life data. References provided at the end of each chapter encompass the entire literature available on the respective topic. Interpretation of the MPCIs and development of threshold values of many MPCIs are also included. This reference book is aimed at the post graduate students in Industrial Statistics. It will also serve researchers working in the αeld of Industrial Statistics, as well as practitioners requiring thorough guidance regarding selection of an appropriate MPCI suitable for the problem at hand.
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