1. Transparency Masters to accompany Operations Management, 5E (Heizer & Render) 4-20 © 1998 by Prentice Hall, Inc. A Simon & Schuster Company Upper Saddle River, N.J. 07458 Quality Management l Modern quality management l Quality:  Planning  Control  Improvement

2. Transparency Masters to accompany Operations Management, 5E (Heizer & Render) 4S-4 © 1998 by Prentice Hall, Inc. A Simon & Schuster Company Upper Saddle River, N.J. 07458 n Measures performance of a process n Uses mathematics (i.e., statistics) n Involves collecting, organizing, & interpreting data n Objective: Regulate product quality n Used to – Control the process as products are produced – Inspect samples of finished products Statistical Quality Control (SPC)

3. Transparency Masters to accompany Operations Management, 5E (Heizer & Render) 4S-6 © 1998 by Prentice Hall, Inc. A Simon & Schuster Company Upper Saddle River, N.J. 07458 n Characteristics for which you focus on defects n Classify products as either ‘good’ or ‘bad’, or count # defects – e.g., radio works or not n Categorical or discrete random variables AttributesVariables Quality Characteristics  Characteristics that you measure, e.g., weight, length  May be in whole or in fractional numbers  Continuous random variables

4. Transparency Masters to accompany Operations Management, 5E (Heizer & Render) 4S-7 © 1998 by Prentice Hall, Inc. A Simon & Schuster Company Upper Saddle River, N.J. 07458 n Statistical technique used to ensure process is making product to standard n All process are subject to variability – Natural causes: Random variations – Assignable causes: Correctable problems n Machine wear, unskilled workers, poor material n Objective: Identify assignable causes n Uses process control charts Statistical Process Control (SPC)

5. Transparency Masters to accompany Operations Management, 5E (Heizer & Render) 4S-8 © 1998 by Prentice Hall, Inc. A Simon & Schuster Company Upper Saddle River, N.J. 07458 Process Control Charts

6. Control Charts R Chart Variables Charts Attributes Charts X Chart P C Continuous Numerical Data Categorical or Discrete Numerical Data Control Chart Types

7. Produce Good Provide Service Stop Process Yes No Assign. Causes? Take Sample Inspect Sample Find Out Why Create Control Chart Start Statistical Process Control Steps

8. Transparency Masters to accompany Operations Management, 5E (Heizer & Render) 4S-15 © 1998 by Prentice Hall, Inc. A Simon & Schuster Company Upper Saddle River, N.J. 07458 n Type of variables control chart – Interval or ratio scaled numerical data n Shows sample means over time n Monitors process average n Example: Weigh samples of coffee & compute means of samples; Plot  X Chart

9. Transparency Masters to accompany Operations Management, 5E (Heizer & Render) 4S-16 © 1998 by Prentice Hall, Inc. A Simon & Schuster Company Upper Saddle River, N.J. 07458 Sample Range at Time i # Samples Sample Mean at Time i From Table S3.1  X Chart Control Limits

10. Transparency Masters to accompany Operations Management, 5E (Heizer & Render) 4S-17 © 1998 by Prentice Hall, Inc. A Simon & Schuster Company Upper Saddle River, N.J. 07458 n Type of variables control chart – Interval or ratio scaled numerical data n Shows sample ranges over time – Difference between smallest & largest values in inspection sample n Monitors variability in process n Example: Weigh samples of coffee & compute ranges of samples; Plot R Chart

11. Transparency Masters to accompany Operations Management, 5E (Heizer & Render) 4S-18 © 1998 by Prentice Hall, Inc. A Simon & Schuster Company Upper Saddle River, N.J. 07458 Sample Range at Time i # Samples From Table S4.1 R Chart Control Limits

12. Transparency Masters to accompany Operations Management, 5E (Heizer & Render) 4S-19 © 1998 by Prentice Hall, Inc. A Simon & Schuster Company Upper Saddle River, N.J. 07458 n Type of attributes control chart – Nominally scaled categorical data n e.g., good-bad n Shows % of nonconforming items n Example: Count # defective chairs & divide by total chairs inspected; Plot – Chair is either defective or not defective p Chart

13. Transparency Masters to accompany Operations Management, 5E (Heizer & Render) 4S-20 © 1998 by Prentice Hall, Inc. A Simon & Schuster Company Upper Saddle River, N.J. 07458 # Defective Items in Sample i Size of sample i z = 2 for 95.5% limits; z = 3 for 99.7% limits p Chart Control Limits

14. Transparency Masters to accompany Operations Management, 5E (Heizer & Render) 4S-21 © 1998 by Prentice Hall, Inc. A Simon & Schuster Company Upper Saddle River, N.J. 07458 n Type of attributes control chart – Discrete quantitative data n Shows number of nonconformities (defects) in a unit – Unit may be chair, steel sheet, car etc. – Size of unit must be constant n Example: Count # defects (scratches, chips etc.) in each chair of a sample of 100 chairs; Plot c Chart

15. Transparency Masters to accompany Operations Management, 5E (Heizer & Render) 4S-22 © 1998 by Prentice Hall, Inc. A Simon & Schuster Company Upper Saddle River, N.J. 07458 # Defects in Unit i # Units Sampled Use 3 for 99.7% limits c Chart Control Limits