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.‎