ISQA 572/ 449 Models for Quality Control/ Process Control and Improvement

1. ISQA 572/ 449Models for Quality Control/ Process Control and Improvement Dr. David Raffo Tel: 725-8508, Fax: 725-5850 Email: davidr@sba.pdx.edu

2. Agenda • Announcements • Questions • Quality Tools • Statistics • Control Charts (Variables)

3. Customer Customer Requirements Marketing & Sales Product Design Planning Forecasting Capacity Schedule Process Design Distribution Whse & transport, by channel Order Entry Production Quality Control Materials Management Supply Chain Management, Purchasing, Inventory Control Overview of Operations Management Support from Accounting, Finance, Human Resources, Information Systems

4. Dimensions of Quality Exhibit 2-1

5. Quality Tools

6. Tools for Process ImprovementExhibit 4-8

7. Flow Chart SymbolsExhibit 4- 9

8. Eat Lunch Get Ingredients Make Sandwich Process for a PBJ Sandwich • At what level of detail should a process diagram be developed? • Is this process diagram helpful? • For what purposes can it be used? • Can it be used for training? For process improvement?

9. Process for a PBJ Sandwich Get from refridg Open jelly? Get plate and knife from cupb Get bread from shelf Get PB from pantry Get from cupb Put bread on plate Spread jelly on other to desired on other Spread PB to desired thickness on one piece of bread Open PB jar

10. Process Diagrams • What is the purpose? • Communication? • Training? • Process Redesign? • ISO 9000? • Forming consensus?

11. Defective Items Bad Rubber Poor adhesion Cracks Voids Impurities Cuts Other Number of Defects 91 128 9 36 15 23 12 Pareto Diagram Example

12. Ways to Use Pareto Diagrams • Focus on the principal aspect of a problem. • Decide the objective of your improvements and your improvement items. • Predict the possible effectiveness of the improvement. • Evaluate the importance of causes.

13. Ways to Use Pareto Diagrams • Understand the effectiveness of the improvement. • Make easy improvements right away. • Evaluate monetary losses rather than units or cases. • Improve your explanations and records. Source: Ozaki & Asaka, Handbook of Quality Tools, Productivity Press, 1990.

14. Pareto Analysis of Complaints

15. Pareto Chart: Errors Exhibit 4-13

16. Ways to Use Cause & Effect Diagrams • Help guide discussion. • Aid in studying the problem. • Understand the actual situation. • Manage factors. • Create and revise manufacturing/service standards. Source: Ozaki & Asaka, Handbook of Quality Tools, Productivity Press, 1990.

17. Fishbone Chart - Truck Delivery Failures Exhibit 4-15

18. Ways to Use Check Sheets • Search for cause • Check-up on the results of an improvement. • Make certain the defect does not recur. • Discuss trends, data distributions, proportions, etc. Source: Ozaki & Asaka, Handbook of Quality Tools, Productivity Press, 1990.

19. Statistics

20. Statistics Notes • Population - collection of all possible elements/ values that are being measured • Sample - subset of population. • When evaluating studies, it is critical to understand how data was sampled and analyzed.

21. Data Collection • Deductive statistics - describe a population or group of data • Inductive statistics - deal with a limited amount of data or representative sample • Variables data - can be measured (continuous) • Attributes data - characteristics that are observed to be either present or absent

22. Measurement • Accuracy - distance from correct value • Precise - consistent and repeatable • Measurement Error - difference between value measured and it’s true value • Example: A part is found to be outside the tolerance limit. Where are possible sources of error?

23. Graphical Tools to Analyze Data • Frequency Diagrams (freq by time) • Histograms (#bins=sqrt n) • Important characteristics: shape, location, and spread • Shape: symmetry/ skewness (p148), kurtosis (peak), modes • Location: mean, median, mode (f4.26) • Spread: standard deviation (p148), range

24. Histogram: Machine Setup Time Exhibit 4-12

25. Notes on HW • Focus on how to create Frequency diagrams and Histograms. • FreqD - goal is to present data in diagram • Checklist of points (do not skip cells) • Histogram - goal is to characterize shape of distribution represented by data • Choice of number of cells and cell interval is important

26. Scatter Diagram : Cure timeExhibit 4-17

27. Run Diagram : Outer Diameters Exhibit 4-18

28. Run Chart of Customers Waiting Exhibit 4S-2

29. Variation • Kinds of variation • With-in piece • Piece to piece • Time to time • Variation is studied by sampling the product • Chance causes - hopefully small, random, cannot be avoided or explained (yet) • Assignable causes - probably larger variations that can be tied to a specific cause

30. Variation • How do we measure variation for a term paper or software product? • What variable attributes can one measure?

31. Normal Curve TM 4-15

32. Central Limit Theorem Illustrated TM 4-6

33. Normal Distribution • Z=(Xi - X-bar)/s • Central Limit Theroem

34. The Central Limit Theorem TM 4-5

35. Bolt Length Process Output Exhibit 4-6

36. Control Charts for Variables

37. SPC: Control Limits TM 4-8

38. Control Chart Uses • Decision making tools • Provide timely information on recently produced parts. • Helps determine process capability • Problem solving tools • Help locate and investigate causes of questionable quality • Aid operator in monitoring process

39. Control Chart Uses • Aid in process design and improvement • Data from current process can be evaluated against design targets • Help study changes made to process (with skilled interpretation)

40. Common Measures • Manufacturing - length, height, viscosity, color, temperature, and velocity • Service - # of errors, # of incorrect trx., delivery times, checkout times, cycle time, • Software - effort, deliverable dates, # defects by type, etc.

41. X-bar & R Charts • What is the goal of measurement? • Is it total product performance or just one particular dimension? • Select characteristic to be measured to achieve goal (consider collection cost) • Choose rational sub-group & size • Need a homogeneous sub-group produced under the same conditions (machine, operator, mold, etc.)

42. X-bar and R Charts • Sampling is typically more frequent when control charts are first used. • Important to know the following: • Who will be collecting the data? • What aspect of the process is to be measured? • Where in the process will the sample be taken?

43. X-bar and R Charts • When (how freq) will the process be sampled? • Why is this sample being taken? • How will data be collected? • How many samples and what sub-group size?

44. X-bar Chart Guidelines • Large sub-group size is more sensitive to variations in the process average and increases inspection costs. • Sub-group sizes smaller than 4 do not create a representative distribution of sub-group averages. • When sub-group size exceeds 10, use the (s) chart rather than the (R) chart.

45. and R Charts TM 4-9

46. Process Control Chart FactorsExhibit 4-22

47. and R Charts TM 4-10

48. and R Charts TM 4-11

49. Developing a Control Chart 1. Take 20-30 random samples of size n where n depends on type of control chart 2. For each sample calculate sample statistic such as X-bar, R or p. 3. Plot the sample statistics sequentially

50. Developing a Control Chart 4. Calculate grand means and control limits 5. Evaluate results and recalculate control limits if necessary.