Prepared by Lloyd R. Jaisingh

1. A PowerPoint Presentation Package to Accompany Applied Statistics in Business & Economics, 4th edition David P. Doane and Lori E. Seward Prepared by Lloyd R. Jaisingh

2. Chapter Contents 17.1 Quality and Variation 17.2 Pioneers in Quality Management 17.3 Quality Improvement 17.4 Control Charts: Overview 17.5 Control Charts for a Mean 17.6 Control Charts for a Range 17.7 Other Control Charts 17.8 Patterns in Control Charts 17.9 Process Capability 17.10 Additional Quality Topics (Optional) Quality Management Chapter 17

3. Chapter Learning Objectives LO17-1: Define quality and explain how it may be measured. LO17-2: Distinguish between common cause variation and special cause variation. LO17-3: Name key individuals and their contributions to the quality movement. LO17-4: List common statistical tools used in quality improvement. LO17-5: List steps toward continuous quality improvement and variance reduction. LO17-6: Make and interpret common control charts (x-bar, R, and p). LO17-7:Recognize abnormal patterns in control charts and their potential causes. LO17-8: Assess the capability of a process. Quality Management Chapter 17

4. 17.1 Quality and Variation LO17-1 Chapter 17 • Quality may be measured as - a physical metric- an aesthetic attribute- a functional characteristic- a personal attribute- an efficiency attribute LO17-1:Define quality and explain how it may be measured. What is Quality? • Quality includes these attributes:- Conformance to specifications.- Performance in the intended use.- As near-zero defects as possible.- Reliability and durability.- Serviceability when needed.- Favorable customer perceptions.

5. 17.1 Quality and Variation LO17-2 Chapter 17 LO17-2: Distinguish between common cause variation and special cause variation. Common Cause versus Special Cause • Sources of variation in processes include • - human abilities • - training • - motivation • - technology • - materials • - management • - organization • Common cause variation(random “noise”) is - normal and expected and - present in any stable, in-control process. • Special cause variationis due to factors that are abnormal and require investigation. • Special cause variation must be eliminated in order for the process to be in control.

6. 17.2 Pioneers in Quality Management LO17-3 Chapter 17 LO17-3: Name key individuals and their contributions to the quality movement. Brief History of Quality Control

7. 17.2 Pioneers in Quality Management LO17-3 Chapter 17 W. Edward Deming

8. 17.2 Pioneers in Quality Management LO17-3 Chapter 17 W. Edward Deming’s 14 Points

9. 17.3 Quality Improvement LO17-4 Chapter 17 • Total quality managementor TQM requires that all business activities should be oriented toward - meeting and exceeding customer needs- empowering employees- eliminating waste or rework- ensuring the long-run viability of the enterprise through continuous quality improvement • TQM includes elements such as statistics, benchmarking, process redesign, team building, group communications, quality function deployment, and cross-functional management. LO17-4: List common statistical tools used in quality improvement. Business Quality Philosophies (Total Quality Management (TQM))

10. 17.3 Quality Improvement LO17-4 Chapter 17 Business Process Redesign (BPR) • Business process redesign or BPR seeks radical redesign of processes to achieve breakthrough improvement in performance measures. Statistical Quality Control (SQC) • Statistical quality control or SQC refers to a subset of quality improvement techniques that rely on statistics. Listed below are some:

11. 17.3 Quality Improvement LO17-5 Chapter 17 LO17-5: List steps toward continuous quality improvement and variance reduction. Continuous Quality Improvement (CQI) • Quality improvement begins with measurement of a variable or an attribute. • For a variable, quality improvement means reducing variation from the target specification. • For an attribute, quality improvement means decreasing the rate of nonconformance. • Goal is to use statistical methods to eliminate sources of special cause (nonrandom) variation.

12. 17.3 Quality Improvement LO17-5 Chapter 17 Continuous Quality Improvement (CQI) • Six Sigma steps to quality improvement – DMAIC (define, measure, analyze, improve, control):

13. 17.5 Control Charts for a Mean LO17-6 Chapter 17 • The process mean  is the centerline of the control chart. LO17-6: Make and interpret common control charts (x-bar, R, and p). Control Limits: Known  and  • The upper control limit (UCL) and lower control limit (LCL) are set at + 3 standard errors from the centerline. • The Empirical rule says that 99.73% of the sample means will fall within “3-sigma” limits.

14. 17.5 Control Charts for a Mean LO17-6 Chapter 17 Control Limits: Known  and  • Sample means will vary but should stay within the control limits and be symmetrically distributed on either side of the centerline. • If a sample mean falls outside of these limits, then we suspect that the sample may be from a different population from the one specified. Empirical Control Limits: Un-known  and  When  and  are unknown, we can estimate them with the sample mean and sample standard deviation. The control limits will then be:

15. 17.5 Control Charts for a Mean LO17-6 Chapter 17 Empirical Control Limits • If using R, where we estimate the standard deviation with the range for the sample subgroups, the formulas become We use this table to obtain d2.

16. 17.5 Control Charts for a Mean LO17-6 Chapter 17 Detecting Abnormal Patterns: Four Rules • Rule 1.Single point outside 3 sigma. • Rule 2.Two of three successive points outside 2 sigma on same side of centerline. • Rule 3. Four of five successive points outside 1 sigma on same side of centerline. • Rule 4.Nine successive points on same side of centerline.

17. 17.6 Control Charts for a Range LO17-6 Chapter 17 Control Limits for the Range • The x-bar chart of sample means only reveals information about the centrality of the process. • To determine if the process is in control, we need to also examine the variation around the mean – traditionally, the sample range – in the R chart. • The R chart has asymmetric control limits since the sample range is not a normally distributed statistic.

18. 17.7 Other Control Charts Chapter 17 • The p chart for attribute data plots the proportion of nonconforming items using the sample proportion p (the defect rate). Attribute Data: p Charts • The number of nonconforming items X in a sample of n items is a binomial random variable. • The population nonconformance rate  can be found by using one of the following: an assumed value of  (e.g., a target rate of nonconforming) - an empirical estimate of  based on a large number of trials - an estimate  from the samples being tested (if no other choice)

19. 17.7 Other Control Charts Chapter 17 Other Standard Control Charts • Other common types of control charts include: • - I charts (for individual numerical observations) • - MR charts (moving range for individual obs.) • - s charts (for standard deviations) • - c charts (for Poisson events) • - np charts (for binomial totals) • - zone charts (using 6 regions based on s)

20. 17.8 Patterns in Control Charts LO17-7 Chapter 17 LO17-7: Recognize abnormal patterns in control charts and their potential causes. Abnormal Patterns

21. 17.9 Process Capability LO17-8 Chapter 17 • Customer requirements must be translated into and upper specification limit (USL) and lower specification limit (LSL) of a quality metric. • These limits do not depend on the process. • Whether the process is capable of meeting these requirements depends on - the magnitude of the process variation () and- whether the process is correctly centered () LO17-8: Assess the capability of a process. Cp Index • Managers typically require Cp > 1.33 • Although easy to understand, Cp fails to show whether the process is well-centered. • Cpk remedies this weakness by considering the relationship between USL and LSL and the process centerline. Cpk Index

22. 17.10 Additional Quality Topics Chapter 17 • Acceptance Sampling • Supply-Chain Management • Quality and Design • Taguchi’s Robust Design • Six Sigma and Lean Six Sigma • ISO 9000 • Malcolm Baldrige Award