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Statistical Process Control Chapter 3 Russell and Taylor Operations and Supply Chain Management, 8th Edition.

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Presentation on theme: "Statistical Process Control Chapter 3 Russell and Taylor Operations and Supply Chain Management, 8th Edition."— Presentation transcript:

1 Statistical Process Control Chapter 3 Russell and Taylor Operations and Supply Chain Management, 8th Edition

2 Lecture Outline Basics of Statistical Process Control – Slide 4Basics of Statistical Process Control Control Charts – Slide 12Control Charts Control Charts for Attributes – Slide 16Control Charts for Attributes Control Charts for Variables – Slide 27Control Charts for Variables Control Chart Patterns – Slide 45Control Chart Patterns SPC with Excel and OM Tools – Slide 52SPC with Excel and OM Tools Process Capability – Slide 54Process Capability 3-2 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e

3 Learning Objectives Explain when and how to use statistical process control to ensure the quality of products and services Discuss the rationale and procedure for constructing attribute and variable control charts Utilize appropriate control charts to determine if a process is in-control Identify control chart patterns and describe appropriate data collection Assess the process capability of a process 3-3 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e

4 Statistical Process Control (SPC) Statistical Process Control monitoring production process to detect and prevent poor quality Sample subset of items produced to use for inspection Control Charts process is within statistical control limits 3-4 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e UCL LCL

5 Process Variability Random inherent in a process depends on equipment and machinery, engineering, operator, and system of measurement natural occurrences Non-Random special causes identifiable and correctable include equipment out of adjustment, defective materials, changes in parts or materials, broken machinery or equipment, operator fatigue or poor work methods, or errors due to lack of training © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e 3-5

6 SPC in Quality Management SPC uses Is the process in control? Identify problems in order to make improvements Contribute to the TQM goal of continuous improvement 3-6 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e

7 Quality Measures: Attributes and Variables Attribute A characteristic which is evaluated with a discrete response good/bad; yes/no; correct/incorrect Variable measure A characteristic that is continuous and can be measured Weight, length, voltage, volume 3-7 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e

8 SPC Applied to Services Nature of defects is different in services Service defect is a failure to meet customer requirements Monitor time and customer satisfaction 3-8 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e

9 SPC Applied to Services Hospitals timeliness & quickness of care, staff responses to requests, accuracy of lab tests, cleanliness, courtesy, accuracy of paperwork, speed of admittance & checkouts Grocery stores waiting time to check out, frequency of out-of-stock items, quality of food items, cleanliness, customer complaints, checkout register errors Airlines flight delays, lost luggage & luggage handling, waiting time at ticket counters & check-in, agent & flight attendant courtesy, accurate flight information, cabin cleanliness & maintenance 3-9 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e

10 SPC Applied to Services Fast-food restaurants waiting time for service, customer complaints, cleanliness, food quality, order accuracy, employee courtesy Catalogue-order companies order accuracy, operator knowledge & courtesy, packaging, delivery time, phone order waiting time Insurance companies billing accuracy, timeliness of claims processing, agent availability & response time 3-10 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e

11 Where to Use Control Charts Process Has a tendency to go out of control Is particularly harmful and costly if it goes out of control Examples At beginning of process because of waste to begin production process with bad supplies Before a costly or irreversible point, after which product is difficult to rework or correct Before and after assembly or painting operations that might cover defects Before the outgoing final product or service is delivered 3-11 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e

12 Control Charts A graph that monitors process quality Control limits upper and lower bands of a control chart Attributes chart p-chart c-chart Variables chart mean (x bar – chart) range (R-chart) 3-12 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e

13 Process Control Chart 3-13 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e Sample number Upper control limit Process average Lower control limit Out of control

14 Normal Distribution Probabilities for Z= 2.00 and Z = © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e  =011 22 33 -1  -2  -3  95% 99.74%

15 A Process Is in Control If … 3-15 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e 1.… no sample points outside limits 2.… most points near process average 3.… about equal number of points above and below centerline 4.… points appear randomly distributed

16 Control Charts for Attributes p-chart uses portion defective in a sample c-chart uses number of defects (non-conformities) in a sample 3-16 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e

17 p-Chart 3-17 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e UCL = p + z  p LCL = p - z  p z=number of standard deviations from process average p=sample proportion defective; estimates process mean  p = standard deviation of sample proportion  p = p(1 - p)n

18 Construction of p-Chart 3-18 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e 20 samples of 100 pairs of jeans NUMBER OFPROPORTION SAMPLE #DEFECTIVESDEFECTIVE :::

19 Construction of p-Chart 3-19 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e UCL = p + z = p(1 - p) n UCL = LCL = LCL = p - z = p(1 - p) n = total defectives total sample observations p =

20 Construction of p-Chart 3-20 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e UCL = p + z = p(1 - p) n 0.10( ) 100 UCL = LCL = LCL = p - z = p(1 - p) n 0.10( ) 100 = 200 / 20(100) = 0.10 total defectives total sample observations p =

21 Construction of p-Chart 3-21 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e

22 p-Chart in Excel 3-22 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e Click on “Insert” then “Charts” to construct control chart I4 + 3*SQRT(I4*(1-I4)/100) I4 - 3*SQRT(I4*(1-I4)/100) Column values copied from I5 and I6

23 c-Chart 3-23 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e UCL = c + z  c LCL = c - z  c where c = number of defects per sample  c = c

24 c-Chart 3-24 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e Number of defects in 15 sample rooms : SAMPLE c = NUMBER OF DEFECTS UCL = c + z  c LCL = c - z  c

25 c-Chart 3-25 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e Number of defects in 15 sample rooms : SAMPLE c = = UCL= c + z  c = = LCL= c - z  c = = 1.99 NUMBER OF DEFECTS

26 c-Chart 3-26 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e

27 Control Charts for Variables 3-27 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e  Range chart ( R-Chart )  Plot sample range (variability)  Mean chart ( x -Chart )  Plot sample averages

28 - © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e 3-28 x-bar Chart:  Known UCL = x + z  x LCL = x - z  x - = = Where  = process standard deviation  x = standard deviation of sample means =  / k = number of samples (subgroups) n = sample size (number of observations) x 1 + x x k k X = = --- n -

29 Observations(Slip-Ring Diameter, cm) n Sample k x-bar Chart Example:  Known 3-29 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e x We know σ =.08

30 x-bar Chart Example:  Known 3-30 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e LCL = x - z  x = - UCL = x + z  x = -x 1 + x x k k X = = ---

31 x-bar Chart Example:  Known 3-31 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e = (.08 / ) 10 = 4.93 _____ = 5.01X = = LCL = x - z  x = - UCL = x + z  x = - = (.08 / ) 10 = 5.09

32 x-bar Chart Example:  Unknown 3-32 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e _ UCL = x + A 2 RLCL = x - A 2 R == _ where x = average of the sample means R = average range value = _

33 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e 3-33 Control Chart Factors n A2D3D Factors for R-chart Sample Size Factor for X-chart

34 x-bar Chart Example:  Unknown 3-34 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e OBSERVATIONS (SLIP- RING DIAMETER, CM) SAMPLE k 12345xR Totals

35 x-bar Chart Example:  Unknown 3-35 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e _ R = ____ ∑ R k _ UCL = x + A 2 R = = x = xkxk ___ _ LCL = x - A 2 R = _

36 x-bar Chart Example:  Unknown 3-36 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e ∑ R k R == = _ ____ _ UCL = x + A 2 R = = _____ x = = = 5.01 cm xkxk ___ = (0.58)(0.115) = 5.08 _ LCL = x - A 2 R = = (0.58)(0.115) = 4.94 _

37 x- bar Chart Example 3-37 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e

38 R- Chart 3-38 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e UCL = D 4 RLCL = D 3 R R = RkRk Where R = range of each sample k = number of samples (sub groups)

39 R-Chart Example 3-39 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e OBSERVATIONS (SLIP- RING DIAMETER, CM) SAMPLE k 12345xR Totals

40 R-Chart Example 3-40 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e Retrieve chart factors D 3 and D 4 UCL = D 4 R = LCL = D 3 R = _ _

41 R-Chart Example 3-41 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e Retrieve chart factors D 3 and D 4 UCL = D 4 R = 2.11(0.115) = LCL = D 3 R = 0(0.115) = 0 _ _

42 R-Chart Example 3-42 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e

43 X-bar and R charts – Excel & OM Tools 3-43 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e

44 Using x- bar and R-Charts Together Process average and process variability must be in control Samples can have very narrow ranges, but sample averages might be beyond control limits Or, sample averages may be in control, but ranges might be out of control An R-chart might show a distinct downward trend, suggesting some nonrandom cause is reducing variation 3-44 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e

45 Control Chart Patterns Run sequence of sample values that display same characteristic Pattern test determines if observations within limits of a control chart display a nonrandom pattern 3-45 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e

46 Control Chart Patterns To identify a pattern look for: 8 consecutive points on one side of the center line 8 consecutive points up or down 14 points alternating up or down 2 out of 3 consecutive points in zone A (on one side of center line) 4 out of 5 consecutive points in zone A or B (on one side of center line) 3-46 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e

47 Control Chart Patterns 3-47 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e UCL LCL Sample observations consistently above the center line LCL UCL Sample observations consistently below the center line

48 Control Chart Patterns 3-48 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e LCL UCL Sample observations consistently increasing UCL LCL Sample observations consistently decreasing

49 Zones for Pattern Tests 3-49 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e UCL LCL Zone A Zone B Zone C Zone B Zone A Process average 3 sigma = x + A 2 R = 3 sigma = x - A 2 R = 2 sigma = x + (A 2 R) = sigma = x - (A 2 R) = sigma = x + (A 2 R) = sigma = x - (A 2 R) = 1313 x = Sample number |1|1 |2|2 |3|3 |4|4 |5|5 |6|6 |7|7 |8|8 |9|9 | 10 | 11 | 12 | 13

50 Performing a Pattern Test 3-50 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e 14.98B—B 25.00BUC 34.95BDA 44.96BDA 54.99BUC 65.01—UC 75.02AUC 85.05AUB 95.08AUA ADB SAMPLExABOVE/BELOWUP/DOWNZONE

51 Sample Size Determination Attribute charts require larger sample sizes 50 to 100 parts in a sample Variable charts require smaller samples 2 to 10 parts in a sample 3-51 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e

52 SPC with Excel 3-52 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e

53 SPC with OM Tools 3-53 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e

54 Process Capability Compare natural variability to design variability Natural variability What we measure with control charts Process mean = 8.80 oz, Std dev. = 0.12 oz Tolerances Design specifications reflecting product requirements Net weight = 9.0 oz  0.5 oz Tolerances are  0.5 oz 3-54 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e

55 Process Capability 3-55 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e (b) Design specifications and natural variation the same; process is capable of meeting specifications most of the time. Design Specifications Process (a) Natural variation exceeds design specifications; process is not capable of meeting specifications all the time. Design Specifications Process

56 Process Capability 3-56 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e (c) Design specifications greater than natural variation; process is capable of always conforming to specifications. Design Specifications Process (d) Specifications greater than natural variation, but process off center; capable but some output will not meet upper specification. Design Specifications Process

57 Process Capability Ratio 3-57 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e Cp=Cp= tolerance range process range upper spec limit - lower spec limit 6  =

58 Computing C p 3-58 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e Net weight specification = 9.0 oz  0.5 oz Process mean = 8.80 oz Process standard deviation = 0.12 oz Cp=Cp= upper specification limit - lower specification limit 6 

59 Computing C p 3-59 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e Net weight specification = 9.0 oz  0.5 oz Process mean = 8.80 oz Process standard deviation = 0.12 oz C p = = = 1.39 upper specification limit - lower specification limit 6  (0.12)

60 Process Capability Index 3-60 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e C pk = minimum x - lower specification limit 3  = upper specification limit - x 3  =,

61 Computing C pk 3-61 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e Net weight specification = 9.0 oz  0.5 oz Process mean = 8.80 oz Process standard deviation = 0.12 oz C pk = minimum x - lower specification limit 3  = upper specification limit - x 3  =,

62 Computing C pk 3-62 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e Net weight specification = 9.0 oz  0.5 oz Process mean = 8.80 oz Process standard deviation = 0.12 oz C pk = minimum = minimum, = 0.83 x - lower specification limit 3  = upper specification limit - x 3  =, (0.12) (0.12)

63 Process Capability With Excel 3-63 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e

64 Process Capability With OM Tools 3-64 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e

65 3-65 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e Copyright 2014 John Wiley & Sons, Inc. All rights reserved. Reproduction or translation of this work beyond that permitted in section 117 of the 1976 United States Copyright Act without express permission of the copyright owner is unlawful. Request for further information should be addressed to the Permission Department, John Wiley & Sons, Inc. The purchaser may make back-up copies for his/her own use only and not for distribution or resale. The Publisher assumes no responsibility for errors, omissions, or damages caused by the use of these programs or from the use of the information herein.


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