Process Performance and Quality

Transcription

1 Chapter 5 Process Performance and Quality

2 Evaluating Process Performance Identify opportunity 1 Define scope 2 Document process 3 Figure 5.1 Implement changes 6 Redesign process 5 Evaluate performance 4

3 The Costs of Poor Quality Prevention Costs Appraisal Costs Internal Failure Costs External Failure Costs

4 TQM Wheel Customer satisfaction Figure 5.2

5 Customer-Driven Definitions of Quality Conformance to Specifications Value Fitness for Use Support Psychological Impressions

6 Employee Involvement Cultural Change Teams

7 Deming Plan Wheel Act Do Check Figure 5.3

8 Statistical Process Control SPC: The application of statistical techniques to determine whether a process is delivering what a customer wants Evaluating the performance of processes requires a variety of data gathering approaches. Variation of outputs Common causes Random, or unavoidable sources of variation within a process Characteristics of distributions» Mean the average observation» Spread the dispersion of observations around the mean» Shape whether the observations are symmetrical or skewed Common cause variation is normally distributed (symmetrical) and stable (the mean and spread do not change over time).

9 Common Causes Mean x = n i =1 n x i Standard Deviation/ Spread σ = ( x x ) 2 i n 1

10 Statistical Process Control Variation of outputs Assignable causes Any cause of variation that can be identified and eliminated. Change in the mean, spread, or shape of a process distribution is a symptom that an assignable cause of variation has developed. After a process is in statistical control, SPC is used to detect significant change, indicating the need for corrective action. spread do not change over time).

11 Assignable Causes Average (a) Location Grams Figure 5.4

12 Assignable Causes Average (b) Spread Grams Figure 5.4

13 Assignable Causes Average (c) Shape Grams Figure 5.4

14 Statistical Process Control Performance measurements Variables service or product characteristics measured on a continuous scale Advantage: if defective, we know by how much the direction and magnitude of corrections are indicated. Disadvantage: precise measurements are required. Attributes a characteristic counted in discrete units, (yes- no, integer number) Used to determine conformance to complex specifications, or when measuring variables is too costly Advantages: Quickly reveals when quality has changed, provides an integer number of how many are defective Requires less effort, and fewer resources than measuring variables Disadvantages: Doesn't show by how much they were defective, the direction and magnitude of corrections are not indicated Requires more observations, since each observation provides little le information

15 Statistical Process Control Sampling Complete inspection Used when Costs of failure are high relative to costs of inspection Inspection is automated Sampling plans Used when Inspection costs are high Inspection destroys the product Sampling plans include Sample size, n random observations Time between successive samples Decision rules that determine when action should be taken Sampling distributions Sample means are usually dispersed about the population mean according to the normal probability distribution (reference the central limit theorem described in statistics texts).

16 Sample Means and the Process Distribution Mean Distribution of sample means Process distribution Figure 5.5 Time

17 Statistical Process Control Control charts Used to judge whether action is required A sample characteristic measured above the upper control limit (UCL) or below the lower control limit (LCL) indicates that an assignable cause probably exists. Steps for using a control chart: Take a random sample, measure the quality characteristic, and calculate a variable or attribute performance measure. Plot the statistic; if it falls outside the control limits, look for assignable causes. Eliminate the cause if it degrades performance. Incorporate the cause if it improves performance. Recalculate the control chart. Periodically repeat the procedure.

18 Statistical Process Control Control charts Indicators of out of control conditions A trend in the observations (the process is drifting) A sudden or step change in the observations A run of five or more observations on the same side of the mean (If we flip a coin and get heads five times in a row, we become suspicious of the coin or of the coin flipping process.) Several observations near the control limits (Normally only 1 in 20 observations are more than 2 standard deviations from the mean.)

19 Control Charts UCL Nominal LCL Figure 5.6 Assignable causes likely 1 2 Samples 3

20 Using Control Charts for Process Improvement Sample the process When changes are indicated, find the assignable cause Eliminate problems, incorporate improvements Repeat the procedure

21 Control Chart Examples Variations UCL Nominal LCL Sample number Figure 5.7 (a)

22 Control Chart Examples Variations UCL Nominal LCL Sample number Figure 5.7 (b)

23 Control Chart Examples Variations UCL Nominal LCL Sample number Figure 5.7 (c)

24 Control Chart Examples Variations UCL Nominal LCL Sample number Figure 5.7 (d)

25 Control Chart Examples Variations UCL Nominal LCL Sample number Figure 5.7 (e)

26 UCL R = D 4 R LCL R = D 3 R Statistical Process Control Control charts for variables Process performance characteristics include variables, which are measured over a continuum. Range charts Monitor process variability First remove assignable causes of variation. While process is in control, collect data to estimate the average range of output that occurs. To establish the upper and lower control limits for the R-chart, R we use Table 5.1, which provides two factors; D3 and D4. These factors establish the UCLR and LCLR at three standard deviations above and below.

27 Control Charts for Variables West Allis Industries

28 Control Charts Special Metal Screw for Variables Sample Sample Number R x _ Example 5.1

29 Control Charts Special Metal Screw for Variables Sample Sample _ Number R x = Example 5.1

30 Control Charts Special Metal Screw for Variables Sample Sample _ Number R x ( = )/4 = Example 5.1

31 Control Charts Special Metal Screw for Variables Sample Sample _ Number R x R = = x = Example 5.1

32 Control Charts for Variables Control Charts Special Metal Screw R-Charts R = UCL R = D 4 R LCL R = D 3 R Example 5.1

33 Example 5.1 Table 5.1 Control Chart Factors Control Charts for Variables Factor for UCL Factor for Factor Size of and LCL for LCL for UCL for Sample x-charts R-Charts R-Charts (n) (A 2 ) (D 3 ) (D 4 )

34 Control Charts for Variables Control Charts Special Metal Screw R-Charts R = D 4 = UCL R = D 4 R D 3 = 0 LCL R = D 3 R Example 5.1

35 Control Charts for Variables Control Charts Special Metal Screw R-Charts R = D 4 = D 3 = 0 UCL R = D 4 R LCL R = D 3 R UCL R = (0.0021) = in. Example 5.1

36 Control Charts for Variables Control Charts Special Metal Screw R-Charts R = D 4 = D 3 = 0 UCL R = D 4 R LCL R = D 3 R UCL R = (0.0021) = in. LCL R = 0 (0.0021) = 0 in. Example 5.1

37 Control Charts for Variables Control Charts Special Metal Screw R-Charts R = D 4 = D 3 = 0 UCL R = D 4 R LCL R = D 3 R UCL R = (0.0021) = in. LCL R = 0 (0.0021) = 0 in. Example 5.1

38 Range Chart - Special Metal Screw Figure 5.8

39 Control Charts for Variables Control Charts Special Metal Screw X-Charts UCL x = x = + A 2 R LCL x = x = - A 2 R R = = x = Example 5.1

40 Example 5.1 Table 5.1 Control Chart Factors Control Charts for Variables Factor for UCL Factor for Factor UCL for Sample x-charts R-Charts R-Charts (n) (A 2 ) (D 3 ) (D 4 ) Control Size of Charts Special and LCL for Metal LCL for Screw X-Charts UCL x = x = + A 2 R LCL x = x = - A 2 R R = = x =

41 Control Charts for Variables Control Charts Special Metal Screw x-charts UCL x = x = + A 2 R LCL x = x = - A 2 R R = A 2 = = x = Example 5.1

42 Control Charts for Variables Control Charts Special Metal Screw x-charts UCL x = x = + A 2 R LCL x = x = - A 2 R R = A 2 = = x = UCL x = (0.0021) = in. Example 5.1

43 Control Charts for Variables Control Charts Special Metal Screw x-charts UCL x = x = + A 2 R LCL x = x = - A 2 R R = A 2 = = x = UCL x = (0.0021) = in. LCL x = (0.0021) = in. Example 5.1

44 x-chart Special Metal Screw Figure 5.9

45 x-chart Special Metal Screw Sample the process Find the assignable cause Eliminate the problem Repeat the cycle Figure 5.9

46 Control Charts for Variables Using σ = UCL x = x + zσ x = LCL x = x zσ x σ x = σ/ n Sunny Dale Bank = x = 5.0 minutes σ = 1.5 minutes n = 6 customers z = 1.96 UCL x = (1.5)/ 6 = 6.20 min UCL x = (1.5)/ 6 = 3.80 min Example 5.2

47 Control Charts for Attributes Hometown Bank HOMETOWN BANK

48 Control Charts for Attributes Hometown Bank UCL p = p + zσ p LCL p = p zσ p σ p = p(1 p)/n Example 5.3

49 Control Charts Sample Number Wrong Account Number UCL 6 p = p + 4zσ p LCL 9 p = p -10 zσ p σ p = p(1 - p)/n Total 147 for Attributes Hometown Bank p = n = 2500 Total defectives Total observations Example 5.3

50 Control Charts Sample Number Wrong Account Number UCL 6 p = p + 4zσ p LCL 9 p = p -10 zσ p σ p = p(1 - p)/n Total 147 for Attributes Hometown Bank p = n = (2500) Example 5.3

51 Control Charts Sample Number Wrong Account Number UCL 6 p = p + 4zσ p LCL 9 p = p -10 zσ p σ p = p(1 - p)/n Total 147 for Attributes Hometown Bank n = 2500 p = Example 5.3

52 Sample Wrong Proportion Number Account Number Defective UCL p = p + zσ p LCL p = p - zσ p σ p = p(1 - p)/n Total 147 Control Charts for Attributes Hometown Bank n = 2500 p = Example 5.3

53 Control Charts for Attributes Hometown Bank n = 2500 p = UCL p = p + zσ p LCL p = p zσ p σ p = p(1 p)/n Example 5.3

54 Control Charts for Attributes Hometown Bank n = 2500 p = UCL p = p + zσ p LCL p = p zσ p σ p = ( )/2500 Example 5.3

55 Control Charts for Attributes Hometown Bank n = 2500 p = UCL p = p + zσ p LCL p = p zσ p σ p = Example 5.3

56 Control Charts for Attributes Hometown Bank n = 2500 p = UCL p = (0.0014) LCL p = (0.0014) σ p = Example 5.3

57 Control Charts for Attributes Hometown Bank n = 2500 p = UCL p = LCL p = σ p = Example 5.3

58 p-chart Wrong Account Numbers Figure 5.10

59 p-chart Wrong Account Numbers Sample the process Find the assignable cause Eliminate the problem Repeat the cycle Figure 5.10

60 Control Charts for Attributes Woodland Paper Company

61 Woodland Paper Company Control Charts for Attributes c = 20 z = 2 UCL c = c + z c LCL c = c z c Example 5.4

62 Woodland Paper Company Control Charts for Attributes c = 20 z = 2 UCL c = LCL c = Example 5.4

63 Woodland Paper Company Control Charts for Attributes c = 20 z = 2 UCL c = LCL c = Example 5.4

64 Woodland Paper Company Control Charts for Attributes Example 5.4

65 Woodland Paper Control Charts for Attributes Company Sample the process Find the assignable cause Incorporate the improvement Repeat the cycle Example 5.4

66 Six Sigma Implementation ASQ 6 Sigma Forum Top Down Commitment Measurement Systems to Track Progress Tough Goal Setting Education Communication Customer Priorities

67 International Quality Documentation ISO 9000 ISO Environmental Management Systems Environmental Performance Evaluation Environmental Labeling Life-Cycle Assessment

68 Criteria for Performance Excellence Category 1 Leadership1 Category 2 Strategic 2 Planning Category 3 Customer 3 and Market Focus Category 4 Information 4 and Analysis Category 5 Human 5 Resource Focus Category 6 Process 6 Management Category 7 Business 7 Results 120 points 85 points 85 points 90 points 85 points 85 points 450 points

69 Leadership Leadership system, values, expectations, and public responsibilities Strategic Planning The effectiveness of strategic and business planning and deployment of plans, focusing on performance requirements Customer and Market Focus How the company determines customer and market requirements and achieves customers satisfaction Information and Analysis The effectiveness of information systems to support customer driven performance excellence and marketplace success Human Resource Focus The success of efforts to realize the full potential of the work force to create a high-performance organization Process Management The effectiveness of systems and processes for assuring the quality of products and services Business Results Performance results and competitive benchmarking in customer satisfaction, financials, human resources, suppliers, and operations Business Results