1. Chapter 7
Process Control

2. Control Systems Standard or goal Means of measuring accomplishment
Comparison of results with the standard to form the basis for corrective action

3. Visual Inspection
Visual inspection processes incur high error rates because of
Complexity of designs
Mistakes resulting from low incidence of errors
Time pressure

4. Documentation and Audits
Dashboards and scorecards
Standard operating procedures (SOPs)
Quality manual
Internal audits

5. Control in Services Time Number of nonconformances Observable errors
Behavior

6. Statistical Process Control (SPC)
Statistical process control – a methodology for monitoring a process to identify special causes of variation and signal the need to take corrective action when appropriate

7. SPC Metrics
Attribute – a performance characteristic either present or absent in a product or service
Variable – a measurement of the degree of conformance to specifications

8. Control Charts Run chart – a line graph showing data plotted over time
Control chart – a run chart with statistically-determined upper and lower control limits (UCL and LCL)

9. Structure of a Control Chart

10. Control Chart Applications in Service Organizations

11. Controlled Process No points are outside control limits.
The number of points above and below the center line is about the same.
The points seem to fall randomly above and below the center line.
Most points, but not all, are near the center line, and only a few are close to the control limits.

12. Typical Out-of-Control Patterns
Point outside control limits
Sudden shift in process average
Cycles
Trends
Hugging the center line
Hugging the control limits

13. Shift in Process Average

14. Identifying Shifts

15. Cycles

16. Trend

17. Hugging Center Line

18. Hugging Control Limits

19. Histograms vs. Control Charts
Histograms do not take into account changes over time.
Control charts can tell us when a process changes

20. Control Chart Methodology
Prepare
Choose measurement
Determine how to collect data, sample size, and frequency of sampling
Set up an initial control chart
Collect Data
Record data
Calculate appropriate statistics
Plot statistics on chart

21. Next Steps Determine trial control limits
Center line (process average)
Compute UCL, LCL
Analyze and interpret results
Determine if in control
Eliminate out-of-control points
Recompute control limits as necessary

22. Use as a problem-solving tool
Final Steps
Use as a problem-solving tool
Continue to collect and plot data
Take corrective action when necessary

23. Control Charts for Variables Data
x-bar and R-charts
Basic calculations

24. Estimating Short-Term Process Capability
Estimate the standard deviation using control chart data:
This approach is not as accurate as computing the true standard deviation of the observations and is not recommended.

25. Case Study: La Ventana Window Company
La Ventana received some complaints about narrow, misfitting gaps between the upper and lower window sashes
The plant manager wants to evaluate the capability of a critical cutting operation that he suspects might be the source of the gap problem.
The nominal specification for this cutting operation is inches with a tolerance of inch.
Inspect five consecutive window panels in the middle of each shift over a 15-day period and recording the dimension of the cut

26. La Ventana Case Data

27. Control Limit Calculations
n = 5; A2 = and D4 = 2.114

28. Spreadsheet Template

29. Control Charts

30. Interpretation Sample 24 out of control in R-chart
Samples 9, 21, and 24 out of control in x-bar chart
Common characteristic: Shane was process operator
Attribute results to special cause variation and delete these samples
New control limits:

31. Using the Control Chart Template
You may need to rescale the vertical axis of the charts to eliminate blank space and make them more visually appealing. See the appropriate Excel help files for the version you are using. This is rarely necessary.
When a sample is deleted from a data set in the templates, do not enter zero for the data; instead, leave the cells blank (just delete the data using the (“delete” key). The charts are set up to interpolate between non-missing data points.
When deleting sample data, be sure to update the number of samples used in the calculations to compute the statistics or the control charts will not display correctly.

32. Revised Control Charts

33. Process Capability Results

34. New Production Data

35. Using Templates with Additional Data
When adding additional data to the spreadsheet template but to maintain the established control limits, do not change the number of samples in cell E6 on which the control limits were based.
Also, you must modify the formulas in cells C9 and C10 for the calculations of the grand average and average range to use only the column range of the original data so as not to change the calculated center lines or control limits.
The template will still chart all the data, but use control limits established from the original samples.

36. Example Spreadsheet Modifications
In cell C9, change the formula to read: =SUMIF(B13:AE13,“>-99999”,B23:AE23)/$E$6.
C10 should be changed to: =SUMIF(B13:AE13,“>-99999”,B28:AE28)/$E$6.

37. Charts with Additional Data
Increased variation
Upward trend in average

38. x-Bar and s-Charts Standard deviation s-chart control limits
x-bar chart control limits

39. Charts for Attributes
A nonconformance (defect, error) is a single nonconforming quality characteristic of a unit of work.
If a unit of work has one or more nonconformances, we term the entire unit nonconforming.
Attribute charts are used for monitoring nonconformances as well as the number nonconforming

40. Fraction Nonconforming (p) Chart
Collect k samples. Let yi represent the number of nonconforming units in sample i, and ni be the size of sample i.
Compute the fraction nonconforming in each sample, pi = yi /ni
Average fraction nonconforming
Compute standard deviation
Control limits

41. Spreadsheet Template

42. Variable Sample Size
The p-chart will have control limits that vary with the sample size

43. Example Spreadsheet Template

44. Example p-Chart With Variable Sample Size

45. p-Chart With Average Sample Size
Use the average sample size (n-bar) to compute approximate control limits
Use the average sample size method when the sample sizes fall within 25 percent of the average.

46. c-Chart for Nonconformances Per Unit
Collect samples of equal size and count the number of nonconformances
Compute the average number of nonconformances per unit, c-bar
Standard deviation
Control limits

47. u-Chart for Nonconformances Per Unit
Collect samples and count the number of nonconformances
Compute the average number of nonconformances per unit, u-bar
Standard deviation
Control limits

48. Control Chart Design Issues
Basis for sampling
Sample size
Frequency of sampling
Location of control limits

49. Project Review – Control (1 of 2)
Process changes have been adequately documented
Documentation for new process has been completed
Process owners have been assigned responsibility for ongoing control
Process capability for new process has been determined
Systems, such as statistical process control, have been developed to identify out-of-control performance

50. Project Review – Control (2 of 2)
Responsibility for control and adjustment has been given to appropriate process owners
Senior management has been informed of the project results
Team members have been thanked, rewarded, and given appropriate new assignments