1. utdallas.edu/~metin 1 Quality Management Chapter 8

2. utdallas.edu/~metin 2 Learning Goals u Statistical Process Control u X-bar, R-bar, p charts u Process variability vs. Process specifications u Yields/Reworks and their impact on costs u Just-in-time philosophy

3. utdallas.edu/~metin 3 Steer Support for the Scooter

4. utdallas.edu/~metin 4 Steer Support Specifications Go-no-go gauge

5. utdallas.edu/~metin 5 Control Charts

6. utdallas.edu/~metin 6 Statistical Process Control (SPC) u SPC: Statistical evaluation of the output of a process during production/service u The Control Process –Define –Measure –Compare to a standard –Evaluate –Take corrective action –Evaluate corrective action

7. utdallas.edu/~metin 7 Not just the mean is important, but also the variance Need to look at the distribution function The Concept of Consistency: Who is the Better Target Shooter?

8. utdallas.edu/~metin 8 Statistical Process Control Capability Analysis Conformance Analysis Investigate for Assignable Cause Eliminate Assignable Cause Capability analysis What is the currently "inherent" capability of my process when it is "in control"? Conformance analysis SPC charts identify when control has likely been lost and assignable cause variation has occurred Investigate for assignable cause Find “Root Cause(s)” of Potential Loss of Statistical Control Eliminate assignable cause Need Corrective Action To Move Forward

9. utdallas.edu/~metin 9 Statistical Process Control u Shewhart’s classification of variability: –Common (random) cause –assignable cause u Variations and Control –Random variation: Natural variations in the output of process, created by countless minor factors »temperature, humidity variations, traffic delays. –Assignable variation: A variation whose source can be identified. This source is generally a major factor »tool failure, absenteeism

10. utdallas.edu/~metin 10 Common Cause Variation (low level) Common Cause Variation (high level) Assignable Cause Variation Two Types of Causes for Variation

11. utdallas.edu/~metin 11 Mean and Variance u Given a population of numbers, how to compute the mean and the variance?

12. utdallas.edu/~metin 12 Sample for Efficiency and Stability u From a large population of goods or services (random if possible) a sample is drawn. –Example sample: Midterm grades of OPRE6302 students whose last name starts with letter R {60, 64, 72, 86}, with letter S {54, 60} »Sample size= n »Sample average or sample mean= »Sample range= R »Standard deviation of sample means=

13. utdallas.edu/~metin 13 Sampling Distribution Sampling distribution Variability of the average scores of people with last name R and S Process distribution Variability of the scores for the entire class Mean Sampling distribution is the distribution of sample means. Grouping reduces the variability.

14. utdallas.edu/~metin 14 Normal Distribution Mean  95.44% 99.74% x normdist(x,.,.,0) Probab normdist(x,.,.,1)

15. utdallas.edu/~metin 15 Cumulative Normal Density 0 1 x normdist(x,mean,st_dev,1) prob norminv(prob,mean,st_dev)

16. utdallas.edu/~metin 16 Normal Probabilities: Example u If temperature inside a firing oven has a normal distribution with mean 200 o C and standard deviation of 40 o C, what is the probability that –The temperature is lower than 220 o C =normdist(220,200,40,1) –The temperature is between 190 o C and 220 o C =normdist(220,200,40,1)-normdist(190,200,40,1)

17. utdallas.edu/~metin 17 Control Limits Sampling distribution Process distribution Mean LCL Lower control limit UCL Upper control limit Process is in control if sample mean is between control limits. These limits have nothing to do with product specifications!

18. utdallas.edu/~metin 18 Setting Control Limits: Hypothesis Testing Framework u Null hypothesis: Process is in control u Alternative hypothesis: Process is out of control u Alpha=P(Type I error)= P(reject the null when it is true)= P(out of control when in control) u Beta=P(Type II error)= P(accept the null when it is false) P(in control when out of control) u If LCL decreases and UCL increases, we accept the null more easily. What happens to –Alpha? –Beta? u Not possible to target alpha and beta simultaneously, –Control charts target a desired level of Alpha.

19. utdallas.edu/~metin 19 Type I Error=Alpha Mean LCLUCL  /2  Probability of Type I error The textbook uses Type I error=1-99.74%=0.0026=0.26%. Sampling distribution

20. utdallas.edu/~metin 20 Time Process Parameter Upper Control Limit (UCL) Lower Control Limit (LCL) Center Line Track process parameter over time - mean - percentage defects Distinguish between - common cause variation (within control limits) - assignable cause variation (outside control limits) Measure process performance: how much common cause variation is in the process while the process is “in control”? Statistical Process Control: Control Charts

21. utdallas.edu/~metin 21 Control Chart 0123456789101112131415 UCL LCL Sample number Mean Out of control Normal variation due to chance Abnormal variation due to assignable sources

22. utdallas.edu/~metin 22 Observations from Sample Distribution Sample number UCL LCL 1234

23. utdallas.edu/~metin 23 Parameters for computing UCL and LCL the Table method

24. utdallas.edu/~metin 24 Collect samples over time Compute the mean: Compute the range: as a proxy for the variance Average across all periods - average mean - average range Normally distributed The X-bar Chart: Application to Call Center

25. utdallas.edu/~metin 25 Define control limits Constants are taken from a table Identify assignable causes: - point over UCL - point below LCL - many (6) points on one side of center In this case: - problems in period 13 - new operator was assigned Control Charts: The X-bar Chart The Table method

26. utdallas.edu/~metin 26 Range Control Chart Multipliers D 4 and D 3 depend on n and are available in Table 8.2. EX: In the last five years, the range of GMAT scores of incoming PhD class is 88, 64, 102, 70, 74. If each class has 6 students, what are UCL and LCL for GMAT ranges? Are the GMAT ranges in control?

27. utdallas.edu/~metin 27 Control Charts: X-bar Chart and R-bar Chart For the Call Center

28. utdallas.edu/~metin 28 X-bar and Range Charts: Which? UCL LCL UCL LCL R-chart x-Chart Detects shift Does not detect shift (process mean is shifting upward) Sampling Distribution

29. utdallas.edu/~metin 29 X-bar and Range Charts: Which? UCL LC L R-chart Reveals increase x-Chart UCL Does not reveal increase (process variability is increasing) Sampling Distribution

30. utdallas.edu/~metin 30 Compute the standard deviation of the sample averages stdev(2.7, 2.38, 3.14, 4.18, 3.12, 3.64, 3.36, 5.94, 2.66, 2.6, 3.16, 4.68, 9.62, 5.04, 4.48, 3.3, 3.06, 4.8, 2.1, 2.8, 5.5, 2.1, 4.78, 2.44, 3.1, 4.38, 3.68)=1.5687 Use type I error of 1-0.9974 Control Charts: The X-bar Chart The Direct method

31. utdallas.edu/~metin 31 u Tolerances/Specifications –Requirements of the design or customers u Process variability –Natural variability in a process –Variance of the measurements coming from the process u Process capability –Process variability relative to specification –Capability=Process specifications / Process variability Process Capability Let us Tie Tolerances and Variability

32. utdallas.edu/~metin 32 Process Capability: Specification limits are not control chart limits Lower Specification Upper Specification Process variability matches specifications Lower Specification Upper Specification Process variability well within specifications Lower Specification Upper Specification Process variability exceeds specifications Sampling Distribution is used

33. utdallas.edu/~metin 33 Process Capability Ratio When the process is centered, process capability ratio A capable process has large Cp. Example: The standard deviation, of sample averages of the midterm 1 scores obtained by students whose last names start with R, has been 7. The SOM requires the scores not to differ by more than 50% in an exam. That is the highest score can be at most 50 points above the lowest score. Suppose that the scores are centered, what is the process capability ratio? Answer: 50/42

34. utdallas.edu/~metin 34 Process mean Lower specification Upper specification +/- 3 Sigma +/- 6 Sigma 3 Sigma and 6 Sigma Quality

35. utdallas.edu/~metin 35 Estimate standard deviation: Or use the direct method with the excel function stdev() Look at standard deviation relative to specification limits  ˆ = R / d 2 33 Upper Specification (USL) Lower Specification (LSL) X-3  A X-2  A X-1  A X X+1  A X+2  X+3  A X-6  B X X+6  B Process A (with st. dev  A ) Process B (with st. dev  B ) x  C p P{defect} 1  0.330.317 2  0.670.0455 3  1.000.0027 4  1.330.0001 5  1.670.0000006 6  2.002x10 -9 The Statistical Meaning of Six Sigma

36. utdallas.edu/~metin 36 Use of p-Charts u p=proportion defective, assumed to be known u When observations can be placed into two categories. –Good or bad –Pass or fail –Operate or don’t operate –Go or no-go gauge

37. utdallas.edu/~metin 37 Estimate average defect percentage Estimate Standard Deviation Define control limits =0.052  ˆ = =0.013 UCL= + 3  ˆ LCL= - 3  ˆ =0.091 =0.014 Period n defects p Attribute Based Control Charts: The p-chart

38. utdallas.edu/~metin 38 Attribute Based Control Charts: The p-chart

39. utdallas.edu/~metin 39 Inspection u Where/When »Raw materials »Finished products »Before a costly operation, PhD comp. exam before candidacy »Before an irreversible process, firing pottery »Before a covering process, painting, assembly u Centralized vs. On-Site, my friend checks quality at cruise lines InputsTransformationOutputs Acceptance sampling Process control Acceptance sampling

40. utdallas.edu/~metin 40 Process Step Bottleneck Based on labor and material cost Market End of Process Defect detected Defect occurred Defect detected Cost of defect $ $ $ Based on sales price (incl. Margin) Recall, reputation, warranty costs Defect detected Discovery of Defects and the Costs CPSC, Segway LLC Announce Voluntary Recall to Upgrade Software on Segway ™ Human Transporters The following product safety recall was conducted by the firm in cooperation with the CPSC. Name of Product: Segway Human Transporter (HT) Units: Approximately 6,000 Recall Alert U.S. Consumer Product Safety Commission Office of Information and Public Affairs Washington, DC 20207 September 26, 2003

41. utdallas.edu/~metin 41 Examples of Inspection Points

42. utdallas.edu/~metin 42 The Concept of Yields 90%80%90%100%90% Line Yield: 0.9 x 0.8 x 0.9 x 1 x 0.9 Yield of Resource = Yield of Process =

43. utdallas.edu/~metin 43 Rework / Elimination of Flow Units Step 1Test 1Step 2Test 2Step 3Test 3 Rework Step 1Test 1Step 2Test 2Step 3Test 3 Step 1Test 1Step 2Test 2Step 3Test 3 Rework: Defects can be corrected Same or other resource Leads to variability Examples: - Readmission to Intensive Care Unit Loss of Flow units: Defects can NOT be corrected Leads to variability To get X units, we have to start X/y units Examples: - Interviewing - Semiconductor fab

44. utdallas.edu/~metin 44 Why Having a Process is so Important: Two Examples of Rare-Event Failures Case 1: Process does not matter in most cases Airport security Safety elements (e.g. seat-belts) Case 2: Process has built-in rework loops Double-checking 1 problem every 10,000 units 99% correct “Bad” outcome happens with probability (1-0.99) 3 Good Bad 99% 1% Learning should be driven by process deviations, not by defects “Bad” outcome only happens Every 100*10,000 units

45. utdallas.edu/~metin 45 Rare events are not so rare: Chances of a Jetliner Crash due to Engine Icing u Engine flameout due to crystalline icing: Engine stops for 30-90 secs and hopefully starts again. u Suppose 150 single engine flameouts over 1990- 2005 and 15 dual engine flameouts over 2002- 2005. What are the annualized single and dual engine flameouts? 10=150/15 and 5=15/3 u Let N be the total number of widebody jetliners flying through a storm per year. Assume that engines ice independently to compute N. Set Prob(2 engine icing)=Prob(1 engine icing) 2 (5/N)=(10/N) 2 which gives N=20 u There are 1200 widebody jetliners worldwide. It is safe to assume that each flies once a day. Suppose that there are 2 storms on their path every day, which gives us about M=700 widebody jetliner and storm encounter very year. How can we explain M=700 > N=20? The engines do not ice independently. With M=700, Prob(1 engine icing)=10/700=1.42% and Prob(2 engine icing)=5/700=0.71%. Because of dependence Prob(2 engine icing) >> Prob(1 engine icing) 2. Unjustifiable independence leads to underestimation of the failure probabilities in operations, finance, engineering, flood control, etc.

46. utdallas.edu/~metin 46 Just-in-Time Philosophy u Pull the operations rather than pushing them –Inventory reduction –JIT Utopia »0-setup time »0-non value added operations »0-defects u Discover and reduce process variability

47. utdallas.edu/~metin 47 Push vs Pull System u What instigates the movement of the work in the system? u In Push systems, work release is based on downstream demand forecasts –Keeps inventory to meet actual demand –Acts proactively »e.g. Making generic job application resumes today (e.g.: exempli gratia) u In Pull systems, work release is based on actual demand or the actual status of the downstream customers –May cause long delivery lead times –Acts reactively »e.g. Making a specific resume for a company after talking to the recruiter

48. utdallas.edu/~metin 48 Push/Pull View of Supply Chains Procurement, Manufacturing and Replenishment cycles Customer Order Cycle Customer Order Arrives Push-Pull boundary PUSH PROCESSESPULL PROCESSES

49. utdallas.edu/~metin 49 Kanban Direction of production flow upstreamdownstream Kanban Authorize production of next unit Pull Process with Kanban Cards

50. utdallas.edu/~metin 50 Browser error Number of defects Order number out off sequence Product shipped, but credit card not billed Order entry mistake Product shipped to billing address Wrong model shipped 100 50 Cumulative percents of defects 100 75 50 25 Pareto Principle or 20-80 rule

51. utdallas.edu/~metin 51 It is not enough to look at “Good” vs “Bad” Outcomes Only looking at good vs bad wastes opportunities for learning; especially as failures become rare (closer to six sigma) you need to learn from the “near misses” Reduce Variability in the Process Taguchi: Even Small Deviations are Quality Losses Lower Specification Limit Target value Quality Loss High Low Performance Metric Target value Quality Loss Performance Metric, x Upper Specification Limit Traditional view of Quality loss Taguchi’s view of Quality loss Performance Metric

52. utdallas.edu/~metin 52 Double-checking (see Toshiba) Fool-proofing, Poka yoke (see Toyota) Computer plugs Set the watch 5 mins ahead Process recipe (see Brownie) Recipes help standardize Accommodate Residual (Common) Variability Through Robust Design

53. utdallas.edu/~metin 53 Materials Machines Specifications / information People Vise position set incorrectly Machine tool coordinates set incorrectly Vice position shifted during production Part clamping surfaces corrupted Part incorrectly positioned in clamp Clamping force too high or too low Cutting tool worn Dimensions incorrectly specified in drawing Dimension incorrectly coded In machine tool program Material too soft Extrusion stock undersized Extrusion die undersized Extrusion rate too high Extrusion temperature too high Error in measuring height Steer support height deviates from specification Ishikawa (Fishbone) Diagram

54. utdallas.edu/~metin 54 Summary u Statistical Process Control u X-bar, R-bar, p charts u Process variability vs. Process specifications u Yields/Reworks and their impact on costs u Just-in-time philosophy

55. utdallas.edu/~metin 55 Jesica Santillan died after a bungled heart-lung transplant in 2003. In an operation Feb. 7, Jesica was mistakenly given organs of the wrong blood type. Her blood type was 0 Rh+. Organs come from A Rh- blood type. Her body rejected the organs, and a matching transplant about two weeks later came too late to save her. She died Feb. 22 at Duke University Medical Center. Line of Causes leading to the mismatch On-call surgeon on Feb 7 in charge of pediatric heart transplants, James Jaggers, did not take home the list of blood types Later stated, "Unfortunately, in this case, human errors were made during the process. I hope that we, and others, can learn from this tragic mistake." Coordinator initially misspelled Jesica’s name Once UNOS (United Network for Organ Sharing) identified Jesica, no further check on blood type Little confidence in information system / data quality Pediatric nurse did not double check Harvest-surgeon did not know blood type Process Failure in Healthcare: The Case of Jesica Santillan

56. utdallas.edu/~metin 56 - As a result of this tragic event, it is clear to us at Duke that we need to have more robust processes internally and a better understanding of the responsibilities of all partners involved in the organ procurement process. William Fulkerson, M.D., CEO of Duke University Hospital. - We didn’t have enough checks. Ralph Snyderman, Duke University Hospital Jesica is not the first death in organ transplantation because of blood type mismatch. Process Failure in Healthcare: The Case of Jesica Santillan

57. utdallas.edu/~metin 57 Step 1: Define and map processes - Jaggers had probably forgotten the list with blood groups 20 times before - Persons involved in the process did not double-check, everybody checked sometimes - Learning is triggered following deaths / process deviations are ignored Step 2: Reduce variability - quality of data (initially misspelled the name) Step 3: Robust Design - color coding between patient card / box holding the organ - information system with no manual work-around - let the technology help RFID tagged patients: Tag includes blood type and other info Electronic medicine box: Alarming for the obsolete medicine The Three Steps in the Case of Jesica

58. utdallas.edu/~metin 58 1. Management Responsibility 2. Quality System 3. Contract review 4. Design control 5. Document control 6. Purchasing / Supplier evaluation 7. Handling of customer supplied material 8. Products must be traceable 9. Process control 10. Inspection and testing 11. Inspection, Measuring, Test Equipment 12. Records of inspections and tests 13. Control of nonconforming products 14. Corrective action 15. Handling, storage, packaging, delivery 16. Quality records 17. Internal quality audits 18. Training 19. Servicing 20. Statistical techniques Examples: “The design process shall be planned”, “production processes shall be defined and planned” How do you get to a Six Sigma Process? Do Things Consistently (ISO 9000)

59. utdallas.edu/~metin 59 Zero Inventories Zero Defects Flexibility / Zero set-ups Zero breakdowns Zero handling / non value added Just-in-time Production Kanban Classical Push “Real” Just-in-time Mixed Production Set-up reduction Autonomation Competence and Training Continuous Improvement Quality at the source Organization Methods Principles The System of Lean Production Pardon the French, caricatures are from Citroen.

60. utdallas.edu/~metin 60 Avoid unnecessary inventory To be seen more as an ideal To types of (bad) inventory: a. resulting from defects / rework b. absence of a smooth process flow Remember the other costs of inventory (capital, flow time) Inventory in process Buffer argument: “Increase inventory” Toyota argument: “Decrease inventory” Principles of Lean Production: Zero Inventory and Zero Defects

61. utdallas.edu/~metin 61 7 1 2 3 4 5 6 8 ITAT=7*1 minute 3 1 2 4 ITAT=2*1 minute Good unit Defective unit ITAT: Information Turnaround Time

62. utdallas.edu/~metin 62 Flexible machines with short set-ups Allows production in small lots Real time with demand Large variety Maximize uptime Without inventory, any breakdown will put production to an end preventive maintenance Avoid Non-value-added activities, specifically rework and set-ups Principles of Lean Production: Zero Set-ups, Zero NVA and Zero Breakdowns

63. utdallas.edu/~metin 63 Push: make to forecastPull: Synchronized production Pull: Kanban Visual way to implement a pull system Amount of WIP is determined by number of cards Kanban = Sign board Work needs to be authorized by demand Classical MRP way Based on forecasts Push, not pull Still applicable for low cost parts Part produced for specific order (at supplier) shipped right to assembly real-time synchronization for large parts (seat) inspected at source Methods of Lean Production: Just-in-time

64. utdallas.edu/~metin 64 Inventory Cycle Inventory Production with large batches End of Month Beginning of Month Cycle Production with large batches End of Month Beginning of Month Cycle Production with large batches End of Month Beginning of Month Cycle Production with large batches End of Month Beginning of Month Inventory End of Month Beginning of Month Produce Sedan Produce Station wagon End of Month Beginning of Month Produce Sedan Produce Station wagon End of Month Beginning of Month Produce Sedan Produce Station wagon End of Month Beginning of Month Methods of Lean Production: Mixed Production and Set-up reduction

65. utdallas.edu/~metin 65 Create local decision making rather than pure focus on execution Use machines / tools, but avoid the lights-off factory Automation with a human touch Cross training of workers Develop problem solving skills Organization of Lean Production: Autonomation and Training

66. utdallas.edu/~metin 66 Solve the problems where they occur - this is where the knowledge is - this is the cheapest place Traditional: inspect and rework at the end of the process Once problem is detected, send alarm and potentially stop the production Own ProcessNext ProcessEnd of LineFinal Inspection End User $$$$$ Rework Reschedule very minor minor delay Significant Rework Delayed delivery Overhead Warranty cost recalls reputation overhead Defect found Defect fixed Organization of Lean Production: Continuous Improvement and Quality-at-the-source