QUALITY IMPROVEMENT TOOLS AND TECHNIQUES

1. QUALITY IMPROVEMENT TOOLS AND TECHNIQUES

2. Module Objectives • Understand the role of problem solving in TQM and use the plan-do-check-act cycle • Explain the primal importance of data in total quality management • Differentiate attribute data from variable data to determine appropriate inputs for TQM statistical tools • Explain the principles and applications of the tools used in TQM systems

3. 7 Step Problem Solving Method 1. Identify the problem - any deviation of the process from its expected performance. 2. Collect and analyze data related to the problem. 3. Identify and explore the causes of the problem, developing alternative explanations. 4. Develop, plan, and implement the solution. 5. Evaluate the effects of the implementation. If the problem is not completely solved, go back to step . 6. Standardize the solution to ensure that its solution is used by everyone. 7. Review the problem solving process to identify how it could solve the next problem better.

4. Plan-Do-Check-Act Cycles PDCA cycle also known as the Deming wheel, describes the sequence managers or workers use to solve problems and improve quality continuously over time.

5. Deming Wheel Act Plan Check Do

6. Data • Variable data • Attribute data

7. Variable Data for Defects per Batch

8. Quality Improvement Tools • Histogram • Process capability analysis • Cause-and-effect diagram • Check sheets • Process flow diagram • Pareto analysis • Process control charts • Acceptance sampling • Scatter diagram

9. Histograms • Center of the distribution • Width of the distribution • Shape of the distribution

10. 100 75 75 50 50 0 35 35 20 20 Bell-Shaped 120 100 50 50 50 50 20 20 20 20 Double-Peaked 85 85 85 80 80 75 75 35 35 10 Plateau Distribution Common Histogram Shapes

11. 80 75 70 70 60 40 35 25 25 20 Comb Distribution 150 125 110 75 60 50 40 30 25 10 Skewed Distribution Common Histogram Shapes

12. Histogram Category Guidelines

13. S C Specification width = p = P Process width Process Capability Analysis

14. Sample Distributions 20 40 S = 20 P = 45-15 = 30 Cp = 20/30 = .67 15 45 A 20 40 S = 20 P = 40-20 = 20 Cp = 20/20 = 1.0 B 20 40 S = 20 P = 36-24 = 12 Cp = 20/12 = 1.67 24 36 C

15. 40 20 S = 20 P = 26-14 = 12 Cp = 20/12 = 1.67 14 26 Problem of Lack of Centering

16. = Cp S / P D - X K = S / 2 = - Cpk ( 1 K ) * Cp Cpk- Improving on the Cp Statistic

17. Calculating Cpk 10 X Bar = 13 D= 15 16 20 S = 10 S/2 = 5 Cp = 10/(16-10)=1.667 K = 2/5 = .4 Cpk = (1-K)*1.667 = 1.00

18. Taguchi Methods Taguchi method is used to identify easily controllable factors and their settings that can minimize variation in product features while keeping the mean values of these features on target.

19. Cause-and-Effect Diagrams • Identify the problem to examine • Identify the major categories of causes • Identify more specific causes • Circle likely causes • Verify the causes

20. Cause-and-Effect Diagram

21. Check Sheets • Reveal patterns and trends • Display data in a form that can be easily read and understood

22. Types of Check Sheets • Attribute check sheets • Variable check sheets • Location check sheets

23. Attribute Check Sheet - Example

24. Location Check Sheet - Example

25. Developing a Check Sheet • Decide on the conditions or events being observed • Select the appropriate form of check sheet to use • Specify the categories to be tracked • Consult with the users • Decide on the appropriate time period over which to collect information

26. Process Flow Diagram Process flow diagramming is a methodology that uses symbols to re[resent the activities and interrelationships contained in an operating process

27. Pareto Analysis • Identify categories about which to collect information • Decide on the time period to cover • Gather data and calculate total frequencies by category for the time period • Sort the categories in descending order based on their percentages • Identify the categories for any qualitative factors • Present the data graphically

28. Pareto Analysis - Example

29. Pareto Analysis - Example

30. Control Charts A control chart plots data collected over time across a set of limits for the upper and lower boundaries of acceptable performance

31. Unacceptable Zone Sample Mean Values Acceptable Zone 3 sigma 99 % Center Line Acceptable Zone 3 sigma Unacceptable Zone Batch Number Limits of Acceptable Performance for Process Control

32. - x R Types of Control Charts Type of Data Cont rol Chart Used Examples Continuous/Indiscrete Measurement (inches, mm) Volume Product weight Power consumed Discrete Number of units with blemished label, damaged package Pn Fraction defective p Number of pin holes in u pieces of plated sheet, differing in area (area or volume is not fixed) Number of pin holes in a c specified area (area is fixed)

33. Constructing an X – R Chart • Initialize the system and collect data to calculate performance limits • Group observations into samples • For each sample, find the sample mean • For each sample, find the range • Calculate the overall mean • Calculate the mean range • Compute control limits • Construct and annotate the control charts • Begin sampling and plotting the x and R values

34. x R 1 12.2 12.3 12.4 11.8 12.7 12.3 0.9 2 12.3 12.1 11.8 12.2 12.3 12.1 0.5 3 12.4 12.7 12.3 12.5 12.3 12.4 0.4 4 12.5 12.3 12.3 12.1 12.1 12.3 0.4 5 12.1 12.4 11.9 12.0 12.3 12.1 0.5 6 12.6 11.8 12.2 11.9 11.9 12.1 0.8 7 11.8 12.1 12.5 12.8 12.5 12.3 1.0 8 12.5 12.8 12.0 12.5 11.9 12.3 0.9 9 12.1 12.3 12.0 11.9 12.1 12.1 0.4 10 11.2 12.3 11.8 11.7 11.9 11.8 1.1 11 11.7 12.2 12.2 11.7 12.1 12.0 0.5 12 12.4 12.2 12.1 12.1 12.1 12.2 0.3 13 11.7 12.1 Data - Example Nominal 1 2 3 4 5 Mean Seek Time = 12 ms Sample # 11.9 11.8 11.9 11.9 0.4 14 11.8 12.2 12.2 12.1 12.2 12.1 0.4 15 11.9 12.3 11.8 11.9 12.1 12.0 0.5 16 12.3 12.4 13.0 12.3 12.2 12.4 0.8 17 11.9 12.6 12.6 12.9 12.1 12.4 0.9 18 11.9 12.0 12.7 12.7 11.9 12.2 0.8 19 11.4 11.6 12.4 11.9 11.8 11.8 1.0 20 11.6 11.8 12.4 12.3 11.2 11.9 1.2

35. n å x i = = 1 i x n Sample Mean

36. Equations for the and R Control Charts x Central Line = x Lower control line = x - A R 2 Upper control line = x + A R 2 R control chart: Central line = R R Lower control limit (LCL) = D 3 R Upper control limit (UCL) = D 4 Control Limits

37. 2 1.88 3.27 0 3 1.02 2.58 0 4 0.73 2.28 0 5 0.58 2.12 0 6 0.48 2.00 0 7 0.42 1.92 0.08 8 0.37 1.86 0.14 9 0.34 1.82 0.18 10 0.31 1.78 0.22 11 0.29 1.74 0.26 12 0.27 1.72 0.28 13 0.25 1.69 0.31 14 0.24 1.67 0.33 15 0.22 1.65 0.35 16 0.21 1.64 0. 36 17 0.20 1.62 0.38 18 0.19 1.60 0.39 19 0.19 1.61 0.40 20 0.18 1.59 0.41 Values for Setting Control Limits n – number in each A2 – X bar limits for D4 – R bar upper D3 – R bar lower 99.7% (3 sigma) limit limit sample

38. Calculated Control Limits - Example

39. X Chart

40. R Chart 1.6 1.4 1.2 1 Range 0.8 0.6 0.4 LCLR 0.2 CLR 0 UCLR 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 R Batch # R Chart

41. Interpreting Control Charts • Trends • Runs • Hugging

42. Acceptance Sampling • Lot-by-lot • Continuous Flow • Special Cases

43. Constructing a Scatter Diagram • Collect paired samples of data that may be related • Lay out the axis lines of the diagram • Plot the data on the diagram • Interpret the data

44. Scatter Diagram Forms