An Introduction to Statistical Process Control Charts (SPC)

2. An Introduction to Statistical Process Control Charts (SPC) Steve Harrison Monday 15th July 2013 12 – 1pm Room 6 R Floor RHH

3. Topics • Variation – A Quick Recap • An introduction to SPC Charts • Interpretation • Quiz • Application in Improvement work

4. Variation

5. Common Cause Variation Typically due to a large number of small sources of variation Example: Variation in work commute due to traffic lights, pedestrian traffic, parking issues Usually requires a deep understanding of the process to minimise the variation 5

6. Special Cause Variation Are not part of the normal process. Arises from special circumstances Example: Variation in work commute impacted by flat tire, road closure, ice-storm. Usually best uncovered when monitoring data in real time (or close to that) 6

7. Special Cause - My trip to work 120 Upper process limit 100 Mean 80 Lower process limit Min. 60 40 20 0 Consecutive trips

8. Two Types of Variation Special Cause: assignable cause signal • Common Cause: • chance cause • noise Statistically significant (not good or bad) 8

9. SPC Charts

10. SPC, Statistical Process Control or The Control Chart Elements 1. Chart/graph showing data, running record, time order sequence 2. A line showing the mean 3. 2 lines showing the upper and lower process ‘control’ limits • You only need 25 data points to set up a control chart, but 50 are better if available

11. 80 70 60 50 40 30 20 10 0 F M A M J J A S O N D J F M A M J J A S O N D The Anatomy of an SPC or Control Chart Upper process control limit Mean Lower process control limit

12. Measures of Central Tendency Mean = Average – SPC Chart Median = Central or Middle Value – Run Chart Mode = Most frequently occurring value 12

13. Standard Deviation or σ In statistics, standard deviation shows how much variation exists from the mean. A low standard deviation indicates that the data points tend to be very close to the mean; high standard deviation indicates that the data points are spread out over a large range of values.

14. Standard Deviation and a normal distribution

15. PRACTICAL INTERPRETATION OF THE STANDARD DEVIATION 99.6% will be within 3 s Mean - 3s Mean Mean + 3s 0.4% will be outside 6s in a normal distribution

16. 3s AND THE CONTROL CHART 3s 3s UCL Mean LCL 6s

17. Run Charts vs. SPC Charts Run Chart SPC More Powerful Control lines show the degree of variation Need Special Software Need 25+ data points • Simple • Easy to create in Excel • Less Sensitive • Only need 10 data points

18. 90 80 70 60 50 40 30 20 10 0 F M A M J J A S O N D J F M A M J J A S O Special cause variation N D

19. SPECIAL CAUSES - RULE 1 UCL Point above Upper Control Limit (UCL) MEAN LCL

20. SPECIAL CAUSES - RULE 1 UCL MEAN Or point below Lower Control Limit (LCL) LCL

21. SPECIAL CAUSES - RULE 2 UCL MEAN Eight points above centre line LCL A 1 in 256 chance or 0.3906%

22. SPECIAL CAUSES - RULE 2 UCL Or eight points below centre line MEAN LCL A 1 in 256 chance or 0.3906%

23. SPECIAL CAUSES - RULE 3 UCL Six points in a downward direction MEAN LCL

24. SPECIAL CAUSES - RULE 3 UCL Or six points in an upward direction MEAN LCL

25. SPECIAL CAUSES - RULE 4 UCL Considerably less than 2/3 of all the points fall in this zone MEAN LCL

26. SPECIAL CAUSES - RULE 4 UCL Or considerably more than 2/3 of all the points fall in this zone MEAN LCL

27. Quiz – 1. Does the chart show Special Cause Variation? Common Cause Variation? Both of the above No Variation

28. 2. How many special cause signals are present on this chart? 0 1 2 3 16

29. 3. How many special cause signals are present on this chart? 0 1 2 3 16

30. 4. How many special cause signals are present on this chart? 0 1 2 3 16

31. What use is this? • Evaluate and improve underlying process • Is the process stable? • Use data to make predictions and help planning • Recognise variation • Prove/disprove assumptions and (mis)conceptions • Help drive improvement – identify statistically significant change

32. Example

33. Annotated SPC Charts • One of the most powerful tools for improvement • Describe a process captured over time (as opposed to being a single sample) • Reveal any trends a process might be experiencing • When combined with careful annotation they track the impact of change

34. Why We Want to Annotate Our Charts…

35. Example – Renal DT247J PDSA 2 PDSA 1

36. Application – Responding to Variation 36

37. Responding to Special Cause Variation Identify the cause: If positive then can it be replicated or standardised. If negative then cause needs to be eliminated 37

38. Responding to CommonCause Variation Reduce variation: make the process even more predictable or reliable (and/or) 2. Not satisfied with result: redesign process to get a better result 38

39. Identify the cause: if positive then can it be replicated or standardized. If negative then cause needs to be eliminated Process with special cause variation Reduce variation: make the process even more reliable Not satisfied with result: redesign process to get a better result Process with common cause variation 39

40. Discussion

41. Evaluation • Absolute Rubbish • Terrible • Fairly Bad • Not that Great • Alright • Quite Good • Really Quite Good • Very Good • Excellent • Amazing!

42. Thanks!

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