1. Chapter 17 Process Improvement Using Control Charts Copyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved.McGraw-Hill/Irwin

2. Process Improvement Using Control Charts 17.1Quality: Meaning and Historical Perspective 17.2Statistical Process Control and Causes of Variation 17.3Sampling a Process, Rational Subgrouping and Control Charts 17.4  and R Charts 17-2

3. Process Improvement Using Control Charts Continued 17.5Comparison of a Process with Specifications: Capability Studies 17.6Charts for Fraction Nonconforming 17.7Cause and Effect, Defect Concentration Diagrams (Optional) 17-3

4. 17.1Quality: Meaning and Historical Perspective Quality ◦ Fitness for use ◦ Extent to which customer expectations are met Types of quality ◦ Quality of design ◦ Quality of conformance ◦ Quality of performance LO17-1: Discuss the principles and importance of quality improvement. 17-4

5. History of the Quality Movement 1924Statistical Quality Control/Control Charts, Shewart/Bell Telephone 1920’sStatistical Acceptance Sampling, Bell Telephone 1946American Society for Quality Control created 1950W. Edwards Deming introduces statistical quality control in Japan 1951Deming Prize established in Japan 1980’sTotal Quality Management (TQM) 1988Malcolm Baldrige National Quality Awards established 1990’sISO 9000, international quality standards adopted LO17-1 17-5

6. 17.2 Statistical Process Control and Causes of Process Variation Historical inspection approach ◦ Inspection of output ◦ Action on output  Scrap, rework, downgrade (expensive!) Statistical process control ◦ Monitor and study process variation ◦ Goal: Continuous process improvement ◦ Preventing by quality through process improvement LO17-2: Distinguish between common causes and assignable causes of process variation. 17-6

7. Causes of Process Variation Common causes ◦ Typical (random) variation inherent in process design ◦ Process in statistical control Assignable causes ◦ Unusual process variation ◦ Intermittent or permanent process changes ◦ Not common to all process observations ◦ Process not in statistical control LO17-2 17-7

8. 17.3 Sampling a Process and Rational Subgrouping and Control Charts Must decide which process variables to study ◦ Best to study a quantitative variable  This means we are employing measurement data We will take a series of samples over time ◦ Usually called subgroups ◦ Usually of size two to six ◦ Usually observed over a short period of time Want to observe often enough to detect important process changes LO17-3: Sample a process by using rational subgrouping. 17-8

9. Control Charts A control chart employs a center line, upper control limit and lower control limit The center line represents average performance The upper and lower control limits are established so that when in control almost all plot points will be between the limits LO17-3 17-9

10. 17.4  and R Charts  and R charts are the most commonly used control charts for measurement data ◦  chart plots subgroup means versus time ◦ R chart plots subgroup range versus time  chart monitors the process mean R chart monitors the amount of variability These two charts must be used together LO17-4: Use  and R charts to establish process control. 17-10

11. Pattern Analysis An observation beyond the control limits indicates the presence of an assignable cause Other types of patterns also indicate the presence of an assignable cause These patterns are more easily described in terms of control chart zones ◦ A, B, C LO17-5: Detect the presence of assignable causes through pattern analysis. 17-11

12. 17.5 Comparison of a Process with Specifications: Capability Studies Natural tolerance limits for a normally distributed process in statistical control will contain about 99.73 percent of the process observations and is given by If the natural tolerance limits are inside the process specification limits, we say that the process is capable of meeting specifications LO17-6: Decide whether a process is capable of meeting specifications. 17-12

13. 17.6 Charts for Fraction Nonconforming Sometimes we inspect items and simply decide if they conform to standards or not ◦ Nonconforming: does not meet standards  Defective ◦ Conforming: meets standards Use a p chart Observe subgroups of n units over time ◦ Determine the number nonconforming LO17-7: Use p charts to monitor process quality. 17-13

14. 17.7 Cause-and-Effect Concentration Diagrams (Optional) A cause-and-effect diagram for “why tables are not cleared quickly in a restaurant” Also known as Ishikawa diagrams or fishbone charts LO17-8: Use diagrams To discern the causes of quality problems (Optional). Figure 17.26 17-14