Applied Data Analysis For Process Improvement

At last, a book that offers the reader a practical approach to process improvement using examples of common problems faced by data analysts! Author James L. Lamprecht, an experienced, widely published statistician, Master Black Belt, teacher, and consultant, has succeeded in combining examples that guide the reader through data analysis, Six Sigma project definition, conducting experiments, graphical analysis, and errors to avoid, all in one concise text. Unlike other books on data analysis, Lamprecht steers clear of classic, or “perfect” examples, preferring instead to address the everyday issues that data analysts confront, and explain the value certain data does and does not offer. The book includes numerous graphs that illustrate ways to intuitively analyze data. Data analysis techniques are presented first, then the author introduces Six Sigma concepts and integrates the two disciplines in a concluding chapter.!--nl--This book is ideal for Certified Six Sigma Black Belts as well as those who are uncertified, but would like to understand how data can be analyzed. Even those who rely on sophisticated statistical software to conduct their Six Sigma analysis will benefit from this insightful yet easy-to-use book by developing a true understanding of statistics and a better understanding of the results they are receiving. Numerous examples illustrate how various techniques are applied. Each example is reviewed from the perspective of what was not said in the example; in other words, the very information you will be faced with when you conduct your own analysis. Titles of some sections in the book include the words "optional" or "advanced." These sections cover more advanced but nonetheless useful topics, but skipping these sections will not affect the overall flow of the various subjects presented.

With the rise of Six Sigma, the use of statistics to analyze and improve processes has once again regained a prominent place in businesses around the world. an increasing number of employees and managers, bestowed with the titles Green Belt, Black Belt, or even Master Black Belts, are asked to apply statistical techniques to analyze and resolve industrial and non-industrial (also known as transactional) problems. These individuals are continuously faced with the daunting task of sorting out the vast array of sophisticated techniques placed at their disposal by an equally impressive array of statistical computer software packages. This book is intended for the ever-growing number of certified Black Belts as well as uncertified others that would like to understand how data can be analyzed. Many courses, including Six Sigma Black Belt courses, do a good job introducing participants to a vast array of sophisticated statistical techniques in as little as ten days, leaning heavily on statistical software packages. Although it is true that one can simplify statistical principles, learning how to interpret results produced by any statistical software requires the understanding of statistics that this book concisely provides.

Primarily intended for the undergraduate students of industrial, production, mechanical and manufacturing engineering, and postgraduate students of industrial, quality engineering and management and industrial engineering and management, this book fills the gap between theory and practice of tools and techniques of quality control and quality improvement. In this book, the principles and concepts are presented clearly and logically with necessary numerical illustrations to reinforce the understanding of the subject matter. The book is organized in two parts. Part I deals with statistical quality control. It starts with the fundamentals of statistics and quality followed by elaborate discussion on statistical process control, process and gauge capability studies with emphasis on their practical application. It also covers detailed discussion on the various types of control charts used to monitor and control quality of processes and products. It includes acceptance sampling inspection procedures and standard sampling systems. Part II deals with quality improvement techniques/methods. It is a data driven approach that discusses the application of Design of Experiments and Taguchi Methods for improving quality of processes and products. A comprehensive discussion on total quality management is also presented. KEY FEATURES • Provides a well structured procedure for the application of all the tools and techniques. • Includes Shainin DOE tools widely used in Six sigma projects. • Demonstrates the application of quality improvement techniques through real life case studies.

Ellis Ott taught generations of quality practitioners to be explorers of the truth through the collection and graphical portrayal of data. From a simple plea to "plot the data" to devising a graphical analytical tool called the analysis of means (ANOM), Ott demonstrated that process knowledge is to be gained by seeking the information contained within the data.In this newest version of Ott's classic text, the authors have strived to continue down the path that he created for others to follow. Additions to this revised edition include: the use of dot plots as an alternative to histograms; digidot plots; adding events to charts; emphasis on the role that acceptance control charts play in controlling risks and the computation of average run length (ARL); a new chapter devoted to process capability, process performance, and process improvement, including the use of confidence intervals for process capability metrics; narrow-limit gauging as another means of assessing the capability of a process; Six Sigma methodology; design resolution; scatter plot matrices as applied to datasets of higher dimensions; and a new chapter on measurement studies.

Organizations are continuously trying to improve by reducing cost, increasing customer satisfaction, and creating an environment of empowered employees who continuously strive for excellence in each process and product. In much the same way, governments are continuously required to do “more with less,” enhance budget and organizational performance, and identify innovative ways to increase their impact. There are challenges to applying the Lean-Six Sigma (LSS) tools in the public sector. Examples of these challenges include hierarchical environments, a lack of common goals, and the complexity of working in the public sector. The information included as part of this book provides over 30 spotlights highlighting project examples, lessons learned, and tips and tricks for using LSS in the public sector. These spotlights are based on interviews facilitated with a robust sampling of senior operations strategy practitioners. The LSS methodology focuses on eliminating waste (lean) and then reducing variation (Six Sigma) in a process or product that contains no waste. The information covered in this book will allow someone to have an immediate impact in any public sector organization. It describes some of the most powerful continuous process improvement tools that can be used, with limited training required. This is further enhanced by showing direct correlations to the LSS tools and the challenges that will be faced. Because the public sector spans such a diverse range of organizational charters (such as transportation, education, and defense), this book does not focus solely on either manufacturing or services. Rather, it provides a balanced approach to utilizing LSS in all environments.

Fully updated to reflect the 2022 ASQ Certified Six Sigma Black Belt (CSSBB) Body of Knowledge (BoK), The ASQ Certified Six Sigma Black Belt Handbook, Fourth Edition is ideal for candidates studying for the CSSBB examination. This comprehensive reference focuses on the core areas of organization-wide planning and deployment, team management, and each of the DMAIC project phases. The fourth edition of this handbook offers thorough explanations of statistical concepts in a straightforward way. It also reflects the latest technology and applications of Six Sigma and lean tools. Updates you will find in the fourth edition include: • New topics and tools, such as return on investment calculations, the roles of coaching and finance in projects, process-decision program charts, interrelationship digraphs, A3 analysis, maturity models, key behavior indicators, and audit MSA • A new chapter on risk analysis and management • Revamped statistics sections • New tables, figures, and examples to help illustrate key points The ASQ Certified Six Sigma Black Belt Handbook, Fourth Edition is also a valuable addition to any quality practitioner’s library.

Arranged in alphabetical order for quick reference, this book provides the quality practitioner with a single resource that illustrates, in a practical manner, how to execute specific statistical methods frequently used in the quality sciences. Each method is presented in a stand-alone fashion and includes computational steps, application comments, and a fully illustrated brief presentation on how to use the tool or technique. A plethora of topics have been arranged in alphabetical order, ranging from acceptance sampling control charts to zone format control charts. This reference is accessible for the average quality practitioner who will need a minimal prior understanding of the techniques discussed to benefit from them. Each topic is presented in a standalone fashion with, in most cases, several examples detailing computational steps and application comments. This second edition includes new sections on advanced SPC applications, reliability applications, and Simplex Optimization. There are expansions in the sections on process capability analysis, hypothesis testing, and design of experiments.

Reducing the variation in process outputs is a key part of process improvement. For mass produced components and assemblies, reducing variation can simultaneously reduce overall cost, improve function and increase customer satisfaction with the product. The authors have structured this book around an algorithm for reducing process variation that they call "Statistical Engineering." The algorithm is designed to solve chronic problems on existing high to medium volume manufacturing and assembly processes. The fundamental basis for the algorithm is the belief that we will discover cost effective changes to the process that will reduce variation if we increase our knowledge of how and why a process behaves as it does. A key way to increase process knowledge is to learn empirically, that is, to learn by observation and experimentation. The authors discuss in detail a framework for planning and analyzing empirical investigations, known by its acronym QPDAC (Question, Plan, Data, Analysis, Conclusion). They classify all effective ways to reduce variation into seven approaches. A unique aspect of the algorithm forces early consideration of the feasibility of each of the approaches. Also includes case studies, chapter exercises, chapter supplements, and six appendices. PRAISE FOR Statistical Engineering "I found this book uniquely refreshing. Don't let the title fool you. The methods described in this book are statistically sound but require very little statistics. If you have ever wanted to solve a problem with statistical certainty (without being a statistician) then this book is for you. - A reader in Dayton, OH "This is the most comprehensive treatment of variation reduction methods and insights I’ve ever seen."- Gary M. Hazard Tellabs "Throughout the text emphasis has been placed on teamwork, fixing the obvious before jumping to advanced studies, and cost of implementation. All this makes the manuscript !attractive for real-life application of complex techniques." - Guru Chadhabr Comcast IP Services COMMENTS FROM OTHER CUSTOMERS Average Customer Rating (5 of 5 based on 1 review) "This is NOT a typical book on statistical tools. It is a strategy book on how to search for cost-effective changes to reduce variation using empirical means (i.e. observation and experiment). The uniqueness of this book: Summarizes the seven ways to reduce variation so we know the goal of the data gathering and analysis, present analysis results using graphs instead of P-value, and integrates Taguchi, Shainin methods, and classical statistical approach. It is a must read for those who are in the business of reducing variation using data, in particular for the Six Sigma Black Belts and Master Black Belts. Don't forget to read the solutions to exercises and supplementary materials to each chapter on the enclosed CD-ROM." - A. Wong, Canada