1. Monitoring Processes Andrew Hingston switchsolutions.com.au PREPARED?

2. Business as usual? 2 Percentage of Invoices Unpaid After One Month

3. 3 Special cause variation Common cause variation

4. 4 INTERPRETATION Special Common REALITY Common Special

5. 5 Why monitorprocesses ?

6. Today 1. Process control charts 2. I and MR charts 3. X-bar and R charts 4. p charts 5. c charts 6. Monitoring processes 7. Process capability 6 Course 1. Understanding data 2. Monitoring processes 3. Exploring relationships

7. 7 1 ProcessControl

8. Statistical Process Control Chart 8

9. Control limits are NOT spec limits! Control Limits Defined by 3 sigma Normal Distribution Control Charts Recalculated when process changes 9 Customer Spec. Limits Defined by customer Identifies defects Histograms and boxplots Change when customers requirements do

10. Type of data? 10 DiscreteCounting(defects) ContinuousMeasuring(time, $, length) IndividualObservations(X) Occurrence Count(c) Categorical Proportions(p) SubgroupAverages( ) I chartsMR charts X-bar chartsR charts p charts c charts

11. Western electric rules Three sigma1 Two sigma 2 of 3 One sigma 4 of 5 Shifts8 11 Trends 8 Too quiet 15 Too noisy14 zig-zag Too variable 8 > library (qcc) > qcc.options("run.length"= 8)

12. Why? Recap 12

13. 13 2 I chartMR chart

14. I chart I is for Individuals Tracks individual data Continuous measurement data 14

15. 15 Revenue ($ millions) revenue.csv > mydata = read.csv(“revenue.csv”) > attach(mydata) > mydata

16. I chart for revenue 16 > qcc(Revenue, type = “xbar.one”)

17. Manual one and two sigma limits 17 > values = qcc(mydata$Revenue, type = "xbar.one") > CL = values$center; SD = values$std.dev > abline(h=CL+SD, lty=3); abline(h=CL+2*SD, lty=3) > abline(h=CL-SD, lty=3); abline(h=CL-2*SD, lty=3)

18. MR chart MR is for Moving Range Tracks absolute difference Use before I chart 18

19. 19 RevenueandPreviousRevenue revenue_adj.csv > adjdata= read.csv(“revenue_adj.csv”) > attach(adjdata) > adjdata

20. MR chart for revenue 20 > lab = seq(2,30) > qcc(adjdata, type = “R”, labels =lab)

22. Steps … Collect 20+ data Stabilise MR chart Stabilise I chart Check normal Extend control limits Plot live data Make decisions 22

23. Extending limits to new data 23 > qcc(Revenue[1:20], type = "xbar.one", newdata=Revenue[21:30])

24. Why?I and MR charts Recap 24

25. 25 3 X-bar chartR chart

26. X-bar chart X-bar is for subgroup mean Tracks samples or subgroup means Continuous measurement data 26

27. 27 Hotel room cleaning times (minutes) hotel.csv > mydata = read.csv(“hotel.csv”) > attach(mydata) > mydata

28. 28 X-bar chart for room cleaning times > qcc(mydata, type = “xbar”)

29. 29 R chart R is for subgroup range (max  min) Tracks max  min for subgroups Use before X-bar chart

30. R chart for room cleaning times 30 > qcc(mydata, type = “R”)

32. Steps … Collect 20+ subgroups Stabilise R chart Stabilise X-bar chart Check means normal Extend control limits Plot live data Make decisions 32

33. Why?I and MR charts X-bar and R charts Recap 33

34. 34 4 p chart

35. 35 p chart p is for proportions Tracks % fails in subgroups Discrete counting data

36. 36 Call centre Unsatisfactory Callscalls.csv > mydata = read.csv(“calls.csv”) > attach(mydata) > mydata

37. 37 p chart for unsatisfactory calls > qcc(Unsatisfactory, sizes=Total, type = "p")

38. Fail or pass only Probabilities constant Fails don’t cause fails Average pass and fail > 5 20+ subgroups Assumptions 38

39. Subgroup sizes can vary Results in wobbly control limits 39

40. Steps … Collect 20+ subgroups Stabilise p chart Extend control limits Plot live data Make decisions 40

41. Why?I and MR charts X-bar and R charts p charts Recap 41

42. 42 5 c chart

43. 43 c chart 1 2 3 4 5 6 7 c is for count Tracks # fails Unknown and constant opportunities

44. 44 Biscuit Complaints complaints.csv > mydata = read.csv(“complaints.csv”) > attach(mydata) > mydata

45. c chart for biscuit complaints 45 > qcc(Complaints, type="c")

46. Fail or pass only Passes unknown Constant opportunity Fails don’t cause fails Average fail > 5 20+ subgroups Assumptions 46

47. Steps … Collect 20+ subgroups Stabilise c chart Extend control limits Plot live data Make decisions 47

48. Why?I and MR charts X-bar and R charts p charts c charts Recap 48

49. 49 6 Monitoring

50. Type of data? 50 DiscreteCounting(defects) ContinuousMeasuring(time, $, length) IndividualObservations(X) Occurrence Count(c) Categorical Proportions(p) SubgroupAverages( ) I chartsMR charts X-bar chartsR charts p charts c charts

51. 51 INTERPRETATION Special Common REALITY Common Special

52. Monitoring live data 52

53. 53 7 Capability

54. Control limits are NOT spec limits! Control Limits Defined by 3 sigma Normal Distribution Control Charts Recalculated when process changes 54 Customer Spec. Limits Defined by customer Identifies defects Histograms and boxplots Change when customers requirements do

55. 55 Process vs Service Upper Service Level Lower Service Level Process Capability Index Minimum of , How many 3 stddevs from nearest service level? 6 SIGMA if > 2

56. Why?I and MR charts X-bar and R charts p charts c charts Monitoring live data Process capability Recap 56

57. PROJECT 57 1. Business process? 2. Data? 3. Control chart?

58. 58 8 Exercises

59. Exercises in R Exercise 2 R&D Exercise 5 Vial weights Exercise 8 Sound chips Exercise 11 DVD rentals 59

60. THANKS Feedback please! 60