Quality and Operations Management

1. Quality and OperationsManagement “Process Control at Plastron”

2. Productivity and Yield Analysis • What are we trying to understand? • Data analysis to be performed? • Inferences to be made? • Recommendations to carry forward?

3. Data Analysis • Monthly Summary • production output and reject totals • Pareto Analysis • ordered view of defects by month • trends month to month • Defect rates (several ways to slice and dice) • by day of the week • by setup versus non-setup days • by daily volume • by ordinal (run length) • Per Cent defective control chart

4. Plastron Division Summary Statistics

5. Plastron Division Summary Statistics

6. Plastron Division Summary Statistics

7. Plastron Division Summary Statistics

8. Plastron Division Summary Statistics Defects by Workday

10. Plastron Division Summary Statistics Defects by Setup vs Non-Setup Day

11. Plastron Division Summary Statistics Defects by Daily Volume

13. Estimating a Baseline Control State • Compute overall sample p • Derive control charts and identify out of control points UCLp / LCLp = p  3 [(p)(1-p)/n]1/2 • Eliminate out of control points and recompute new sample average for p • Continue iterations eliminating and recomputing until sample estimates stabilize

14. Plastron Division Summary Statistics Baseline P-Chart

15. Baseline Control Chart Data Shaded cells are Above UCL or Below LCL

16. Estimate of Process Average • Started with original 51 points, • computed overall process average to be 15.02% • find 15 points of out control above UCL • eliminated these and recalculated p = 11.87% • Second iteration • used 11.87% and compare to all 51 points • reveals 20 out-of-control points above UCL • eliminated these and recalculated p = 9.78% • Third iteration • used 9.78% • reveals 26 out-of-control points above UCL • eliminated these and recalculated p = 9.06% • Fourth iteration • used 9.06% • reveals identical 26 out-of-control points • use 9.06% as good or capable estimate of process average

18. Why Find a Good Estimate? • [(p)(1-p)/n] increases with increasing p when p < 0.5 • [(p)(1-p)/n] decreases with increasing p when p > 0.5 • If data contains “unresolved” out-of-control situations then width of the control limits would be over estimated when “true” p < 0.5 • [(p)(1-p)/n] is at maximum value when p = 0.5 • conservative UCL/LCL is p  3 [0.5(0.5)/n]1/2

19. Recommendations forCharlie Hobbs

20. Key Points for Data Analysis • Retroactive data analysis is useful for process audits, capability assessment and opportunity identification • Any retroactive data analysis must then be turned into a recommendation and action plan to achieve process improvement • With aggregate and retroactive data singular versus multiple occurring failures may pollute data • Forward looking process control is more productive

21. Key Points About Process Control • Process control charting based on end-of-line (end-of-day) rejects is inefficient • Inspect early for faster feedback • early detection means less potential scrap in WIP • in process inspection (SPC versus SQC) • use of feedback control • expand control to include product and process checks • Improve effectiveness of process control by linking out-of-control states and assignable causes

22. Summary • Three key process control functions • characterize capability • establish monitors • provide feedback for control • Monitor both product and process • Reduce detection delay • early and in-line inspection • shortened inspection interval • Link out-of-control states to assignable causes