Industrial Engineering Statistics - Application of Statistics in Industrial Engineering Practice

Industrial Engineering Statistics - Application of Statistics in Industrial Engineering Practice

Industrial engineering is productivity improvement. Industrial engineering is cost reduction. Industrial engineering efficiency improvement.

Industrial engineering is improving the productivity of every resource used in production using engineering processes. It can also be said that is improving the productivity of every process or operation of the process.

What is the role of the statistics subject in industrial engineering?

Have industrial engineers spent time on this question? Or have they taken some methods or tools developed by statisticians and simply added to their toolkit to apply them as they have the potential increase the productivity of processes.

Statistical Process Control and Statistics Quality Control were developed by statisticians and inspection and testing department people. Industrial engineers promoted them as they increased productivity by reducing time spent by people on these activities. When time spent by people goes down, time spent by equipment and tools also go down. Hence many times productivity improvement of one resource can mean productivity improvement of other resources also.
Statistical Quality Control – Industrial Engineering

Sampling was used in industrial engineering in work sampling to reduce the effort involved in time study or production study.

Six sigma is an application of statistics that reduces defects and thus contributes to increase of productivity. Six sigma can also be used to find the highest speed at which a machine can be run to produce acceptable quality. Thus it can be directly employed in productivity improvement. Six sigma now part of tool kit of industrial engineers.

Six Sigma in Machining Processes - Six Sigma Simple Explanation

Engineering Statistics - Text Books

Introduction to Engineering Statistics and Lean Sigma: Statistical Quality Control and Design of Experiments and Systems

Theodore T. Allen
Springer Science & Business Media, Apr 23, 2010 - 600 pages
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.
https://books.google.co.in/books?id=ev54lAwS2KIC





Modern Engineering Statistics
Thomas P. Ryan
John Wiley & Sons, Jun 22, 2007 - 736 pages
An introductory perspective on statistical applications in the field of engineering
"Modern Engineering Statistics" presents state-of-the-art statistical methodology germane to engineering applications. With a nice blend of methodology and applications, this book provides and carefully explains the concepts necessary for students to fully grasp and appreciate contemporary statistical techniques in the context of engineering.

With almost thirty years of teaching experience, many of which were spent teaching engineering statistics courses, the author has successfully developed a book that displays modern statistical techniques and provides effective tools for student use. This book features:

Examples demonstrating the use of statistical thinking and methodology for practicing engineers

A large number of chapter exercises that provide the opportunity for readers to solve engineering-related problems, often using real data sets

Clear illustrations of the relationship between hypothesis tests and confidence intervals

Extensive use of Minitab and JMP to illustrate statistical analyses

The book is written in an engaging style that interconnects and builds on discussions, examples, and methods as readers progress from chapter to chapter. The assumptions on which the methodology is based are stated and tested in applications. Each chapter concludes with a summary highlighting the key points that are needed in order to advance in the text, as well as a list of references for further reading. Certain chapters that contain more than a few methods also provide end-of-chapter guidelines on the proper selection and use of those methods. Bridging the gap between statistics education and real-world applications, Modern Engineering Statistics is ideal for either a one- or two-semester course in engineering statistics.
https://books.google.co.in/books?id=aZn7XNphKcgC

2006
Springer Handbook of Engineering Statistics
Editors: Hoang Pham Prof.
ISBN: 978-1-85233-806-0 (Print) 978-1-84628-288-1 (Online)
http://link.springer.com/referencework/10.1007%2F978-1-84628-288-1

Engineering Statistics Journals

Technometrics
http://www.tandfonline.com/loi/utch20


Volume 1 No.1
http://www.tandfonline.com/toc/utch20/1/1
Condensed Calculations for Evolutionary Operation Programs
G. E. P. Box & J. S. Hunter
pages 77-95

Volume 2 No. 1
http://www.tandfonline.com/toc/utch20/2/1#.VYIybvmqqko
Statistical Estimation of the Gasoline Octane Number Requirement of New Model Automobiles

Claude S. Brinegar & Ronald R. Miller
pages 5-18


Updated on 21 September 2019, 17 June 2015