"It is a book for manufacturing companies that are fighting desperately for survival and that will go to any length to improve their factories and overcome the obstacles to success. One could even call this book a ?bible? for corporate survival."

?Hiroyuki Hirano

Known as the JIT bible in Japan, JIT Implementation Manual ? The Complete Guide to Just-in-Time Manufacturing presents the genius of Hiroyuki Hirano, a top international consultant with vast experience throughout Asia and the West. Encyclopedic in scope, this six-volume practical reference provides unparalleled information on every aspect of JIT? the waste-eliminating, market-oriented production system. This historic, yet timeless classic is just as crucial in today's fast-changing global marketplace as when it was first published in Japan 20 years ago.

Volume 4: Leveling ? Changeover and Quality Assurance provides essential background on the core concept of level production in a JIT, or lean, manufacturing system and the implementation techniques. It also discusses changeover and the rules and procedures for changeover improvement and covers quality assurance in the context of level production, including how to recognize structures that create defects, plan for achieving zero defects, and make use of mistake-proofing devices.

Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors all leaders in the statistics community introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice.

New to the Third Edition

• Four new chapters on nonparametric modeling
• Coverage of weakly informative priors and boundary-avoiding priors
• Updated discussion of cross-validation and predictive information criteria
• Improved convergence monitoring and effective sample size calculations for iterative simulation
• Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation
• New and revised software code

The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book s web page."