Introduction to Engineering Statistics and Lean Sigma
Statistical Quality Control and Design of Experiments and Systems
Allen, Theodore T.
2nd ed. 2010, XXIII, 572 p.
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Second edition of a successful book expands on two major topics of increasing relevance to organizations: lean production and design for six sigma
Enables readers to identify which six sigma methods are most suitable for particular scenarios
Lean production, has long been regarded as critical to business success in many industries. Over the last ten years, instruction in six sigma has been increasingly linked with learning about the elements of lean production. Introduction to Engineering Statistics and Lean Sigma builds on the success of its first edition (Introduction to Engineering Statistics and Six Sigma) to reflect the growing importance of the "lean sigma" hybrid.
As well as providing detailed definitions and case studies of all six sigma methods, Introduction to Engineering Statistics and Lean Sigma forms one of few sources on the relationship between operations research techniques and lean sigma. Readers will be given the information necessary to determine which sigma methods to apply in which situation, and to predict why and when a particular method may not be effective. Methods covered include:
• control charts and advanced control charts,
• failure mode and effects analysis,
• Taguchi methods,
• gauge R&R, and
• genetic algorithms.
The second edition also greatly expands the discussion of Design For Six Sigma (DFSS), which is critical for many organizations that seek to deliver desirable products that work first time. It incorporates recently emerging formulations of DFSS from industry leaders and offers more introductory material on the design of experiments, and on two level and full factorial experiments, to help improve student intuition-building and retention.
The emphasis on lean production, combined with recent methods relating to Design for Six Sigma (DFSS), makes Introduction to Engineering Statistics and Lean Sigma a practical, up-to-date resource for advanced students, educators, and practitioners.
Content Level »Graduate
Keywords »Measure - algorithms - genetic algorithms - linear optimization - operations research - optimization - organization - quality