Download presentation

Presentation is loading. Please wait.

Published byJohana Sember Modified over 2 years ago

1
© 1997 Prentice-Hall, Inc. S3 - 1 Principles of Operations Management Quality Via Statistical Process Control Chapter S3

2
© 1997 Prentice-Hall, Inc. S3 - 2 Learning Objectives n Explain statistical process control n Develop control charts for variables R chart, X chart R chart, X chart n Develop control charts for attributes l P chart, c chart

3
© 1997 Prentice-Hall, Inc. S3 - 3 Thinking Challenge In the mid-1980’s, most firms adjusted the process if output varied by ± 3 from average (2,700 defects per million products). In trouble, Motorola decided to use ± 6 . This meant no more than 2 defects per billion products. Should Motorola have followed industry practice, used 6 , or some other standard? © 1995 Corel Corp. AloneGroupClass

4
© 1997 Prentice-Hall, Inc. S3 - 4 Statistical Quality Control (SQC) n Uses mathematics (i.e., statistics) n Involves collecting, organizing, & interpreting data n Objective: Regulate product quality n Used to l Control the process as products are produced l Inspect samples of finished products

5
© 1997 Prentice-Hall, Inc. S3 - 5 Types of Statistical Quality Control

6
© 1997 Prentice-Hall, Inc. S3 - 6 n Characteristics for which you focus on defects n Classify products as either ‘good’ or ‘bad’, or count # defects l e.g., radio works or not n Categorical or discrete random variables Attributes Quality Characteristics n Characteristics that you measure l e.g., weight, length n May be whole number or fractional n Continuous random variables Variables

7
© 1997 Prentice-Hall, Inc. S3 - 7 Statistical Process Control (SPC) n Statistical technique used to ensure process is making product to standard n All process are subject to variability l Natural causes: Random variations l Assignable causes: Correctable problems s Machine wear, unskilled workers, poor mat’l n Objective: Identify assignable causes n Uses process control charts

8
© 1997 Prentice-Hall, Inc. S3 - 8 Process Control Charts n Graph of sample data plotted over time UCL LCL Assignable Cause Variation Process Average ± 3 Natural Variation

9
© 1997 Prentice-Hall, Inc. S3 - 9 Control Chart Purposes n Show changes in data pattern l e.g., trends s Make corrections before process is out of control n Show causes of changes in data l Assignable causes s Data outside control limits or trend in data l Natural causes s Random variations around average

10
© 1997 Prentice-Hall, Inc. S3 - 10 Theoretical Basis of Control Charts As sample size gets large enough ( 30)... sampling distribution becomes almost normal regardless of population distribution. Central Limit Theorem

11
© 1997 Prentice-Hall, Inc. S3 - 11 Theoretical Basis of Control Charts Properties of normal distribution 99.7% of all X fall within ± 3 X

12
© 1997 Prentice-Hall, Inc. S3 - 12 Theoretical Basis of Control Charts 95.5% of all X fall within ± 2 X Properties of normal distribution 99.7% of all X fall within ± 3 X

13
© 1997 Prentice-Hall, Inc. S3 - 13 Statistical Process Control Steps

14
© 1997 Prentice-Hall, Inc. S3 - 14 Control Chart Types Continuous Numerical Data Categorical or Discrete Numerical Data

15
© 1997 Prentice-Hall, Inc. S3 - 15 R Chart n Type of variables control chart l Interval or ratio scaled numerical data n Shows sample ranges over time l Difference between smallest & largest values in inspection sample n Monitors variability in process n Example: Weigh samples of coffee & compute ranges of samples; Plot

16
© 1997 Prentice-Hall, Inc. S3 - 16 R Chart Control Limits Sample Range at Time i # Samples From Table S3.1

17
© 1997 Prentice-Hall, Inc. S3 - 17 R Chart Example You’re manager of a 500-room hotel. You want to analyze the time it takes to deliver luggage to the room. For 7 days, you collect data on 5 deliveries per day. Is the process in control?

18
© 1997 Prentice-Hall, Inc. S3 - 18 R & X Chart Hotel Data Sample Sample DayDelivery TimeMeanRange 17.304.206.103.455.555.32 7.30 + 4.20 + 6.10 + 3.45 + 5.55 5 Sample Mean =

19
© 1997 Prentice-Hall, Inc. S3 - 19 R & X Chart Hotel Data Sample Sample DayDelivery TimeMeanRange 17.304.206.103.455.555.323.85 7.30 - 3.45 Sample Range = LargestSmallest

20
© 1997 Prentice-Hall, Inc. S3 - 20 R & X Chart Hotel Data Sample Sample DayDelivery TimeMeanRange 17.304.206.103.455.555.323.85 24.608.707.604.437.626.594.27 35.982.926.204.205.104.883.28 47.205.105.196.804.215.702.99 54.004.505.501.894.464.073.61 610.108.106.505.066.947.345.04 76.775.085.906.909.306.794.22

21
© 1997 Prentice-Hall, Inc. S3 - 21 R R R Chart Control Limits Solution From Table S3.1 (n = 5) R k UCLD i i k R 1 4 385427422 7 3894 211438948232.......

22
© 1997 Prentice-Hall, Inc. S3 - 22 Partial Table for Control Chart Limits

23
© 1997 Prentice-Hall, Inc. S3 - 23 R Chart Control Limits Solution

24
© 1997 Prentice-Hall, Inc. S3 - 24 R Chart Control Chart Solution UCL

25
© 1997 Prentice-Hall, Inc. S3 - 25 X Chart n Type of variables control chart l 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

26
© 1997 Prentice-Hall, Inc. S3 - 26 X Chart Control Limits Sample Range at Time i # Samples Sample Mean at Time i From Table S3.1

27
© 1997 Prentice-Hall, Inc. S3 - 27 R & X Chart Hotel Data Sample Sample DayDelivery TimeMeanRange 17.304.206.103.455.555.323.85 24.608.707.604.437.626.594.27 35.982.926.204.205.104.883.28 47.205.105.196.804.215.702.99 54.004.505.501.894.464.073.61 610.108.106.505.066.947.345.04 76.775.085.906.909.306.794.22

28
© 1997 Prentice-Hall, Inc. S3 - 28 X Chart Control Limits Solution * From Table S3.1 (n = 5)

29
© 1997 Prentice-Hall, Inc. S3 - 29 X Chart Control Chart Solution* UCL LCL

30
© 1997 Prentice-Hall, Inc. S3 - 30 Thinking Challenge You’re manager of a 500-room hotel. The hotel owner tells you that it takes too long to deliver luggage to the room (even if the process may be in control). What do you do? © 1995 Corel Corp. AloneGroupClass

31
© 1997 Prentice-Hall, Inc. S3 - 31 p Chart n Type of attributes control chart l Nominally scaled categorical data s e.g., good-bad n Shows % of nonconforming items n Example: Count # defective chairs & divide by total chairs inspected; Plot l Chair is either defective or not defective

32
© 1997 Prentice-Hall, Inc. S3 - 32 c Chart n Type of attributes control chart l Discrete quantitative data n Shows number of nonconformities (defects) in a unit l Unit may be chair, steel sheet, car etc. l Size of unit must be constant n Example: Count # defects (scratches, chips etc.) in each chair of a sample of 100 chairs; Plot

33
© 1997 Prentice-Hall, Inc. S3 - 33 What Is Acceptance Sampling? n Form of quality testing used for incoming materials or finished goods l e.g., purchased material & components n Procedure l Take one or more samples at random from a lot (shipment) of items l Inspect each of the items in the sample l Decide whether to reject the whole lot based on the inspection results

34
© 1997 Prentice-Hall, Inc. S3 - 34 What Is an Acceptance Plan? n Set of procedures for inspecting incoming materials or finished goods n Identifies l Type of sample l Sample size (n) l Criteria (c) used to reject or accept a lot n Producer (supplier) & consumer (buyer) must negotiate

35
© 1997 Prentice-Hall, Inc. S3 - 35 Producer’s & Consumer’s Risk Producer's risk ( ) Producer's risk ( ) l Probability of rejecting a good lot l Probability of rejecting a lot when fraction defective is AQL n Consumer's risk (ß) l Probability of accepting a bad lot l Probability of accepting a lot when fraction defective is LTPD

36
© 1997 Prentice-Hall, Inc. S3 - 36 ConclusionConclusion n Explained statistical process control n Developed control charts for variables R chart, X chart R chart, X chart n Discussed control charts for attributes l P chart, c chart n Explained acceptance sampling l Producer’s & consumer’s risk

Similar presentations

OK

1 Slides used in class may be different from slides in student pack Technical Note 8 Process Capability and Statistical Quality Control Process Variation.

1 Slides used in class may be different from slides in student pack Technical Note 8 Process Capability and Statistical Quality Control Process Variation.

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on channels of distribution strategy Ppt on model view controller mvc Ppt on support vector machine Ppt on human chromosomes sex Ppt on review of literature outline Ppt on magic squares Ppt on self help groups in india Ppt on money and credit download Ppt on road accidents statistics Ppt on rainwater harvesting structures