From William Sealy Gosset's late 19th Century work at the Guinness Brewery to The Western Electric SQC Handbook that became the AT&T SQC Handbook in the late 1950s, there is a long tradition of large-scale manufacturers being fertile ground producing comprehensive documentation of basic, applied statistics for largely quality applications in business and industry. This book, by two GE Research and Development employees, follows in that tradition.
Decades of work in reliability and quality control areas during the peak years of the six sigma movement applied to real world problems at GE positioned the authors to put together this very practical text on the role of statistics in business and industry. The book describes many of the key problems to which they were exposed during their careers. They start by describing specific problems that are common in manufacturing and test design. They determine the key questions to address and then describe the statistical methods that are employed to solve these problems. This book helps formulate and describe applying statistical thinking to practical business problems.
This book's chapter layout makes the text work as both a reference guide and a textbook for either classroom or self-guided study. A well-thought progression of sections in each chapter concludes with summarizing takeaway bullet points with general and technical questions to motivate discussion and learning. An extensive FTP site provides additional material, including solutions to some of the applications.
Always taking a high-level view that admits readers of the most basic mathematical literacy, this is an introduction to statistics and its applications in business and industry. The explanations of how statistics helps one design, build, ensure reliability and improve products from aircraft engines to washing machines, and discussion of the use of statistics in specialized areas, such as the food and beverage, semiconductor and communications industries, can be grasped readily by the non-mathematician. Undergraduate students will find valuable connection points with the basic theory they are learning while manufacturing managers overseeing projects relying on statistics will find valuable enlightenment here. The core of the text is chapters rich in real-world case studies, if they are only given a brief overview, on such areas as product reliability, product field support, pharmaceuticals, financial services, and more.
Tom Schulte is a mathematics graduate student at Oakland University and by day leads a group of software developers at Plex Systems providing manufacturers with statistics-based solutions for SPC, GRR, and other quality applications.
Chapter 1. Statistics: An Overview.
1.1 About this Chapter.
1.2 What is Statistics?.
1.3 Areas of Application.
1.4 Statistical Literacy.
1.5 Better Decisions Require Better Data.
1.6 Statistical Thinking .
1.7 Related Disciplines .
1.8 Major Take-Aways .
Chapter 2. Statistics in Business and Industry.
2.1 About this Chapter.
2.2 Evolution of Statistics for Manufactured Product Applications.
2.3 Applications Beyond Manufactured Products.
2.4 Forces Impacting the Use of Statistics Today.
2.5 Major Take-Aways.
PART II. MANUFACTURED PRODUCT APPLICATIONS.
Chapter 3. Product Design: Concepts.
3.1 About this Chapter.
3.2 The Product Design Process.
3.4 Ensuring Measurement Capability.
3.5 Design and Development.
3.6 Design Validation.
3.7 Transition to Manufacturing.
3.8 Major Take-Aways.
Chapter 4. Product Design: Example.
4.1 About this Chapter.
4.2 Thermoplastic Resin for Child Car Seat: Introduction.
4.3 Some Basic Elements of Plastic Part Design and Manufacture.
4.4 Setting the Design Goals.
4.5 Ensuring Measurement Capability.
4.6 Design and Development.
4.7 Design Validation.
4.8 Transition to Manufacturing.
4.9 Major Take-Aways.
Chapter 5. Product Reliability Assurance.
5.1 About this Chapter .
5.2 The Key Role of Reliability.
5.3 Design for Reliability: The Need.
5.4 The Process and the Participants.
5.5 Setting Reliability Goals.
5.6 Reliability Evaluation of a Conceptual Design.
5.7 Product Reliability Development.
5.8 Reliability Validation.
5.9 Product Safety.
5.10 Reliability Growth Analysis.
5.11 Product Burn-In.
5.12 Software Reliability.
5.13 Further Reading.
5.14 Major Take-Aways.
Appendix: Product Life Distributions and Concepts.
Chapter 6. Manufacturing Quality Improvement: Concepts.
6.1 About this Chapter .
6.2 The Need for Manufacturing Quality Improvement.
6.3 Different Industries.
6.4 Considerations in Manufacturing Quality Improvement.
6.5 Disciplined Approaches to Manufacturing Quality Improvement.
6.6 Quality Assessment and Improvement.
6.7 Statistical Monitoring.
6.8 Major Takeaways.
Chapter 7. Manufacturing Quality Improvement: Example.
7.1 About this Chapter.
7.2 Generator Stator Bar Example.
7.3 Manufacturing Capability and Stability Assessment.
7.4 Addressing Measurement Error.
7.5 Reducing Deviations from Target and Variability.
7.6 Statistical Monitoring.
Chapter 8. Product Manufacturing: Further Applications.
8.1 About this Chapter.
8.2 Integrating Engineering Process Control and Statistical Process Control.
8.3 Polymer Resin Example.
8.4 Materials and Parts Quality Assurance.
8.5 Final Product Assessment.
8.6 Productivity Improvement.
8.7 Major Takeaways.
Chapter 9. Product Field Support.
9.1 About this Chapter.
9.2 Reliability Data Tracking.
9.3 Reliability Data Tracking for Non-Repairable Products .
9.4 Reliability Data Tracking for Repairable Products.
9.5 Planning the Failure Reporting System.
9.6 A Note on Product Safety .
9.7 Tracking Customer Satisfaction.
9.8 Competitive Evaluations and Advertising Claims.
9.9 Proactive Product Servicing .
9.10 Major Take-Aways.
PART III. OTHER APPLICATIONS.
Chapter 10. Pharmaceutical Products .
10.1 About this Chapter.
10.2 Drug Development: Process Overview.
10.3 The Role of a Pharmaceutical Statistician: An Overview.
10.4 Pre-Clinical Studies.
10.5 Clinical Studies.
10.6 Case Study: Phase 2 Clinical Trial.
10.7 Regulatory Review.
10.8 Some Other Applications.
10.9 Some Statistical Tools Used in Pharmaceutical Applications.
10.10 Sources of Further Information.
10.11 Major Take-Aways.
Chapter 11. Financial Services .
11.1 About this Chapter .
11.2 Financial Services and Its Uncertainties: .
11.3 Credit Scoring for Consumer Loan Approval .
11.4 Credit Scoring Example .
11.5 Model Development and Implementation: Some Important Nitty-Gritties.
11.6 Modeling in Credit Management: A Broader Look.
11.7 Specific Application Areas.
11.8 Statisticians in Financial Services.
11.9 Further Reading.
11.10 Major Take-Aways.
Chapter 12. Business Processes.
12.1 About this Chapter.
12.2 Business Processes.
12.3 Example: Improving the Loan Decision Process for a Business Equipment Financing Company.
12.4 Steps for Process Improvement.
12.5 Another Example: Improving the Skip Tracing Process.
12.6 Some Further Concepts and Methods.
12.7 Operations Research and Management Science .
12.8 Major Take-Aways.
Chapter 13. Further Applications.
13.1 About this Chapter.
13.2 Food, Beverage and Related Industries .
13.3 Semiconductor Industry .
13.4 Communications Industry .
13.5 Statistical Image Analysis for Medical, Security and Other Applications .
13.6 Other Application Areas.
13.7 Emerging Areas: A Glance Into the Future.
13.8 Major Take-Away.