Monitoring Processes

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Monitoring Processes

  1. 1. Monitoring Processes<br />Andrew Hingston<br />switchsolutions.com.au<br />PREPARED?<br />
  2. 2. Business as usual?<br />2<br />Percentage of Invoices Unpaid After One Month<br />
  3. 3. Today<br />1. Process control charts<br />2. I and MR charts<br />3. X-bar and R charts<br />4. p charts<br />5. c charts<br />6. Monitoring processes<br />7. Process capability<br />3<br />Course<br />1. Understanding data<br />2. Monitoring processes<br />3. Exploring relationships<br />
  4. 4. 4<br />Why<br />monitorprocesses<br />? <br />
  5. 5. 5<br />1<br />ProcessControl<br />
  6. 6. Statistical Process Control Chart<br />6<br />
  7. 7. Control limits are NOT spec limits!<br />Control Limits<br />Defined by 3 sigma<br />Normal Distribution<br />Control Charts<br />Recalculated when process changes<br />7<br />Customer Spec. Limits<br />Defined by customer<br />Identifies defects<br />Histograms and boxplots<br />Change when customers requirements do<br />
  8. 8. Type of data?<br />8<br />DiscreteCounting(defects)<br />ContinuousMeasuring(time, $, length)<br />IndividualObservations(X)<br />Occurrence<br />Count(c)<br />Categorical<br />Proportions(p)<br />SubgroupAverages( )<br />I chartsMR charts<br />X-bar chartsR charts<br />p charts<br />c charts<br />
  9. 9. Western electric rules<br />Three sigma1<br />Two sigma 2 of 3<br />One sigma 4 of 5<br />Shifts8<br />9<br />Trends 8<br />Too quiet 15<br />Too noisy14 zig-zag<br />Too variable 8<br />
  10. 10. 10<br />2<br />I chartMR chart<br />
  11. 11. I chart<br />I is for Individuals<br />Tracks individual data<br />Continuous measurement data<br />11<br />
  12. 12. 12<br />Revenue<br />($ millions)<br />revenue.csv<br />
  13. 13. I chart for revenue<br /><ul><li>mydata = read.csv("revenue.csv"); attach(mydata)
  14. 14. qcc(Revenue, type = "xbar.one")</li></ul>13<br />
  15. 15. MR chart<br />MR is for Moving Range<br />Tracks absolute difference<br />Use before I chart<br />14<br />
  16. 16. 15<br />RevenueandPreviousRevenue<br />revenue_adj.csv<br />
  17. 17. MR chart for revenue<br /><ul><li>adjdata = read.csv("revenue_adj.csv"); attach(adjdata)
  18. 18. lab=seq(2,30,length=29)
  19. 19. qcc(adjdata, type = "R", labels=lab)</li></ul>16<br />
  20. 20. Continuous data<br />Data normally distributed<br /><ul><li>shapiro.test(X) # Normal if p-value > 0.05</li></ul>20+ data points<br />Assumptions<br />17<br />
  21. 21. Steps …<br />Collect 20+ data<br />Stabilise MR chart<br />Stabilise I chart<br />Check normal<br />Extend control limits<br />Plot live data<br />Make decisions<br />18<br />
  22. 22. Extending limits to new data<br /><ul><li>qcc(Revenue[1:20], type = "xbar.one", newdata=Revenue[21:30])</li></ul>19<br />
  23. 23. 20<br />3<br />X-bar chartR chart<br />
  24. 24. X-bar chart<br />X-bar is for subgroup mean<br />Tracks samples or subgroup means<br />Continuous measurement data<br />21<br />
  25. 25. 22<br />Hotel room<br />cleaning times<br />(minutes)<br />hotel.csv<br />
  26. 26. 23<br />X-bar chart for room cleaning times<br /><ul><li>mydata = read.csv("hotel.csv"); attach(mydata)
  27. 27. qcc(mydata, type = "xbar")</li></li></ul><li>24<br />R chart<br />R is for subgroup range (max  min)<br />Tracks max  min for subgroups<br />Use before X-bar chart<br />
  28. 28. R chart for room cleaning times<br /><ul><li>mydata = read.csv("hotel.csv"); attach(mydata)
  29. 29. qcc(mydata, type = "R")</li></ul>25<br />
  30. 30. Continuous data<br />Means normally distribution<br /><ul><li>shapiro.test(X) # Normal if p-value > 0.05</li></ul>20+ subgroups<br />Assumptions<br />26<br />
  31. 31. Steps …<br />Collect 20+ subgroups<br />Stabilise R chart<br />Stabilise X-bar chart<br />Check means normal<br />Extend control limits<br />Plot live data<br />Make decisions<br />27<br />
  32. 32. 28<br />4<br />p chart<br />
  33. 33. 29<br />p chart<br />p is for proportions<br />Tracks % fails in subgroups<br />Discrete counting data<br />
  34. 34. 30<br />Call centre<br />Unsatisfactory<br />Callscalls.csv<br />
  35. 35. 31<br />p chart for unsatisfactory calls<br /><ul><li>mydata = read.csv("calls.csv"); attach(mydata)
  36. 36. qcc(Unsatisfactory, sizes=Total, type = "p")</li></li></ul><li>Fail or pass only<br />Probabilities constant<br />Fails don’t cause fails<br />Average pass and fail > 5<br />20+ subgroups<br />Assumptions<br />32<br />
  37. 37. Subgroup sizes can vary<br />Results in wobbly control limits<br />33<br />
  38. 38. Steps …<br />Collect 20+ subgroups<br />Stabilise p chart<br />Extend control limits<br />Plot live data<br />Make decisions<br />34<br />
  39. 39. 35<br />5<br />c chart<br />
  40. 40. 36<br />c chart<br />1 2 3 4 5 6 7<br />c is for count<br />Tracks # fails<br />Unknown and constant opportunities<br />
  41. 41. 37<br />Biscuit<br />Complaints<br />complaints.csv<br />
  42. 42. c chart for biscuit complaints<br /><ul><li>mydata = read.csv("complaints.csv"); attach(mydata);
  43. 43. qcc(Complaints, type="c")</li></ul>38<br />
  44. 44. Fail or pass only<br />Passes unknown<br />Constant opportunity<br />Fails don’t cause fails<br />Average fail > 5<br />20+ subgroups<br />Assumptions<br />39<br />
  45. 45. Steps …<br />Collect 20+ subgroups<br />Stabilise c chart<br />Extend control limits<br />Plot live data<br />Make decisions<br />40<br />
  46. 46. Why?I and MR charts<br />X-bar and R charts<br />p charts<br />c charts<br />Recap<br />41<br />
  47. 47. 42<br />6<br />Monitoring<br />
  48. 48. 43<br />INTERPRETATION<br />Special<br />Common<br />REALITY<br />Common<br />Special<br />
  49. 49. Monitoring live data<br />44<br />
  50. 50. 45<br />7<br />Capability<br />
  51. 51. Control limits are NOT spec limits!<br />Control Limits<br />Defined by 3 sigma<br />Normal Distribution<br />Control Charts<br />Recalculated when process changes<br />46<br />Customer Spec. Limits<br />Defined by customer<br />Identifies defects<br />Histograms and boxplots<br />Change when customers requirements do<br />
  52. 52. 47<br />Process vs Service<br />Upper Service Level<br />Lower Service Level<br />Process Capability Index<br />Minimum of ,<br />How many 3 stddevs from nearest service level?<br />6 SIGMA if > 2<br />
  53. 53. Why?I and MR charts<br />X-bar and R charts<br />p charts<br />c charts<br />Monitoring live data<br />Process capability<br />Recap<br />48<br />
  54. 54. 49<br />8<br />Exercises<br />
  55. 55. Exercises in R<br />Exercise 2 R&D<br />Exercise 5 Vial weights<br />Exercise 8 Sound chips<br />Exercise 11 DVD rentals<br />50<br />

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