1. Process Improvement Dr. Ron Tibben-Lembke

2. Quality Dimensions  Quality of Design Quality characteristics suited to needs and wants of a market at a given cost Continuous, never-ending improvement  Quality of Conformance Predictable degree of uniformity and dependability, in line with target price  Quality of Performance How is product performing in the marketplace? Are customers happy with the product? Durability, service considerations

3. Defining Quality  Hard to define, like art, but you know it when you see it.  Some common terms from your definitions Consistency (conformance) Conformance to a standard Ability of a product or service to meet stated or implied needs (design, performance)

4. Responsibility for Quality  Who’s responsible for quality? Quality of Design Quality of Consistency Quality of Performance

5. Ensuring quality  How can we make sure that we are delivering quality to the customer?

6. SDSA Cycle  Standardize: Get employees to agree on how the process is done, using best practices from each Flowchart the process Key indicators of process peformance  Do- Conduct planned experiments using best- practice methods on trial basis  Study- Collect & analyze data on key indicators to evaluate best-practice methods  Act- standardize best-practice methods and formalize through training

7. PDSA Cycle  Reduce difference between customers’ needs and process performance  Plan: create a plan to improve or innovate the best-practice method from the SDSA cycle  Do: test plan on trial basis  Study: study impact on key measurements  Act: Take appropriate corrective actions

9. Statistics

11. Designed Size 12.5 13 13.5 14 14.5 15 15.5 16 16.5 17 17.5

12. Natural Variation 12.5 13 13.5 14 14.5 15 15.5 16 16.5 17 17.5

13. Process Control Charts  Graph of sample data plotted over time UCL LCL Process Average ± 3  Natural Variation

14. Process Control Charts  Graph of sample data plotted over time UCL LCL Process Average ± 3  Assignable Cause Variation Natural Variation

15. Theoretical Basis of Control Charts As sample size gets large enough (  30)... Central Limit Theorem

16. Theoretical Basis of Control Charts As sample size gets large enough (  30)... sampling distribution becomes almost normal regardless of population distribution. Central Limit Theorem

17. Theoretical Basis of Control Charts Mean Central Limit Theorem Standard deviation

18. Theoretical Basis of Control Charts 95.5% of all  X fall within ± 2   X Properties of normal distribution X

19. Theoretical Basis of Control Charts 95.5% of all  X fall within ± 2   X Properties of normal distribution

20. Theoretical Basis of Control Charts Properties of normal distribution 99.7% of all  X fall within ± 3   X

21. 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

22. Setting Control Limits  Type I error – concluding a process is not under control, when it really is  Type II error – concluding a process is under control, when it really is not

23. Rules for Out of Control Points  Rule 1: Out of control if any point outside control limits  Rule 2: any 2 out of 3 consecutive points fall in one of the A zones on same side of centerline  Rule 3: Any 4 of 5 consecutive points fall in B zone or higher on same side  Rule 4: 8 in a row on same side  Rule 5: 8 or more in a row increasing or decreasing

24. Rules for Out of Control Points  6 An unusually small number of runs above and below the centerline (lots of up, down runs)  Rule 7: 13 consecutive points fall within zone C on either side of centerline

25. Run Tests A B C C B A 3σ3σ 3σ3σ 2σ2σ 2σ2σ 1σ1σ 1σ1σ mean

26. Attributes vs. Variables Attributes:  Good / bad, works / doesn’t  count % bad (C chart)  count # defects / item (P chart) Variables:  measure length, weight, temperature (x-bar chart)  measure variability in length (R chart)

27. p Chart Control Limits # Defective Items in Sample i Sample i Size UCLpz p n p X n p i i k i i k         (1 - p) 1 1

28. p Chart Control Limits # Defective Items in Sample i Sample i Size UCLpz pp) n p X n p i i k i i k        (1 1 1 z = 2 for 95.5% limits; z = 3 for 99.7% limits # Samples n n k i i k    1

29. p Chart Control Limits # Defective Items in Sample i # Samples Sample i Size z = 2 for 95.5% limits; z = 3 for 99.7% limits  UCLpz LCLpz n n k p X n p p i i k i i k i i k        11 1 and n pp)  (1 pp) n  (1

30. p Chart Example You’re manager of a 500- room hotel. You want to achieve the highest level of service. For 7 days, you collect data on the readiness of 200 rooms. Is the process in control (use z = 3)? © 1995 Corel Corp.

31. p Chart Hotel Data No.No. Not DayRoomsReady Proportion 12001616/200 =.080 2200 7.035 320021.105 420017.085 520025.125 620019.095 720016.080

32. p Chart Control Limits n n k i i k    1 1400 7 200

33. p Chart Control Limits 16 + 7 +...+ 16 p X n i i k i i k      1 1 121 1400 0864.n n k i i k    1 1400 7 200

34. p Chart Control Limits Solution p p  308643. n pp)  (1 200.0864 * (1-.0864) p X n i i k i i k      1 1 121 1400 0864.n n k i i k    1 1400 7 200 16 + 7 +...+ 16

35. p Chart Control Limits Solution  086405961460... or &.0268 p p  308643. n pp)  (1 200.0864 * (1-.0864) p X n i i k i i k      1 1 121 1400 0864.n n k i i k    1 1400 7 200 16 + 7 +...+ 16

36. p Chart Control Chart Solution UCL LCL

37. C-Chart Control Limits  # defects per item needs a new chart  How many possible paint defects could you have on a car?  C = average number defects / unit  UCL c z C   c LCLz C  c c