MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing 1 Chapter 11 Statistical Thinking and Applications

MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing 2 Key Idea Raw data collected from the field do not provide the information necessary for quality control or improvement. Data must be organized, analyzed, and interpreted. Statistics provide an efficient and effective way of obtaining meaningful information from data, allowing managers and workers to control and improve processes.

MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing 3 Statistical Thinking  All work occurs in a system of interconnected processes  Variation exists in all processes  Understanding and reducing variation are the keys to success

MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing 4 Sources of Variation in Production Processes Materials Tools OperatorsMethods Measurement Instruments Human Inspection Performance EnvironmentMachines INPUTSPROCESSOUTPUTS

MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing 5 Variation  Many sources of uncontrollable variation exist (common causes)  Special (assignable) causes of variation can be recognized and controlled  Failure to understand these differences can increase variation in a system

MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing 6 Key Idea A system governed only by common causes is called a stable system. Understanding a stable system and the differences between special and common causes of variation is essential for managing any system.

MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing 7 Problems Created by Variation  Variation increases unpredictability.  Variation reduces capacity utilization.  Variation contributes to a “bullwhip” effect.  Variation makes it difficult to find root causes.  Variation makes it difficult to detect potential problems early.

MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing 8 Importance of Understanding Variation time PREDICTABLE ? UNPREDECTIBLE

MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing 9 Two Fundamental Management Mistakes 1. Treating as a special cause any fault, complaint, mistake, breakdown, accident or shortage when it actually is due to common causes 2. Attributing to common causes any fault, complaint, mistake, breakdown, accident or shortage when it actually is due to a special cause

MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing 10 Deming’s Red Bead Experiment Dr. Deming used his “red bead” experiment to demonstrate that the success or failure of workers is primarily a function of the system they work in, and not their loyalty and effort. Given a container of 20 red balls and 80 white balls, the worker is asked to select only white balls. When a red ball is selected, the worker is criticized. When a white ball is selected, the worker is praised. Goal setting is then attempted to motivate the workers. Inspectors are employed to audit the process. Questions: How does this process affect worker morale, productivity and quality? What should be done to improve the outcome? How would goal setting improve this process? How do external inspectors help?

MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing 11 Lessons Learned  Quality is made at the top.  Rigid procedures are not enough.  People are not always the main source of variability.  Numerical goals are often meaningless.  Inspection is expensive and does not improve quality.

MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing 12 Statistical Foundations  Random variables  Probability distributions  Populations and samples  Point estimates  Sampling distributions  Standard error of the mean

MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing 13 Important Probability Distributions  Discrete  Binomial  Poisson  Continuous  Normal  Exponential

MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing 14 Central Limit Theorem  If simple random samples of size n are taken from any population, the probability distribution of sample means will be approximately normal as n becomes large.

MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing 15 Key Idea A good sampling plan should select a sample at the lowest cost that will provide the best possible representation of the population, consistent with the objectives of precision and reliability that have been determined for the study.

MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing 16 Sampling Error  Sampling error (statistical error)  Nonsampling error (systematic error)  Factors to consider:  Sample size  Appropriate sample design

MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing 17 Statistical Methods  Descriptive statistics  Statistical inference  Predictive statistics

MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing 18 Key Idea One of the biggest mistakes that people make in using statistical methods is confusing data that are sampled from a static population (cross-sectional data) with data sampled from a dynamic process (time series data).

MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing 19 Enumerative and Analytic Studies  Enumerative study – analysis of a static population  Analytic study – analysis of a dynamic time series

MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing 20 Design of Experiments  A designed experiment is a test or series of tests that enables the experimenter to compare two or more methods to determine which is better, or determine levels of controllable factors to optimize the yield of a process or minimize the variability of a response variable.  DOE is an increasingly important tool for Six Sigma.