Quality and OperationsManagement

“Process Control at Plastron”

Productivity and Yield Analysis

• What are we trying to understand?• Data analysis to be performed?• Inferences to be made?• Recommendations to carry forward?

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

Plastron Division Summary Statistics

MonthTotal

RejectsTotal

ProductionReject

Per CentJanuary 1845 13757 13.41%February 3578 17164 20.85%March 2612 22585 11.57%

Total 8035 53506 15.02%

Plastron Division Summary Statistics

January Defect Pareto

0%2%4%6%8%

10%12%14%16%18%20%

Saw

Delam

Rump

Press

Surf

Edge

Rough

Foam

Wire

Crack

Dirt

Mislay

Glue

Face

Lines

Saw

Trash

Other

% o

f Tot

al D

efec

ts

Plastron Division Summary StatisticsFebruary Defect Pareto

0%5%

10%15%20%25%30%35%40%45%50%

Delam

Saw

Rump

Press

Foam

Edge

Surf

Crack

Wire

Rough

Lines

Mislay

Other

Dirt

Face

Glue

Saw

Trash%

of T

otal

Def

ects

Plastron Division Summary StatisticsMarch Defect Pareto

0%

5%

10%

15%

20%

25%

Rump

Press

Rough

Edge

Delam

Saw

Foam

Lines

Trash

Surf

Saw

Crack

Wire

Mislay

Dirt

Other

Glue

Face%

of T

otal

Def

ects

Plastron Division Summary StatisticsDefects by Workday

DayData Mon Tue Thu Fri Wed Grand TotalCount of Day 11 11 11 6 12 51Sum of Total Rejects 1614 1827 1526 1186 1882 8035Sum of TOTAL PIECES 9864 13362 9642 5851 14787 53506

% Defective 16.4% 13.7% 15.8% 20.3% 12.7% 15.0%Avg Daily Defects 147 166 139 198 157 158Avg Daily Production 897 1215 877 975 1232 1049

Defects by Day

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

1 2 3 4 5

Day of Week

Per C

ent D

efec

tive

Plastron Division Summary StatisticsDefects by Setup vs Non-Setup Day

Setup DayData no yes Grand TotalCount of Setup Day 36 15 51Sum of Total Rejects 5663 2372 8035Sum of TOTAL PIECES 38115 15391 53506

% Defective 14.9% 15.4% 15.0%Avg Daily Defects 157 158 158Avg Daily Production 1059 1026 1049

Plastron Division Summary StatisticsDefects by Daily Volume

Volume CodeData A B C D E Grand TotalCount of Volume Code 13 12 14 7 5 51Sum of Total Rejects 693 1247 3380 1452 1263 8035Sum of TOTAL PIECES 4007 8508 17546 11749 11696 53506

A<500 B<1000 C<1500 D<2000 E>2000% Defective 17.3% 14.7% 19.3% 12.4% 10.8% 15.0%Avg Daily Defects 53 104 241 207 253 158Avg Daily Production 308 709 1253 1678 2339 1049

Defects by Volume

0%

5%

10%

15%

20%

25%

500 1000 1500 2000 2500

Volume

Per C

ent D

efec

tive

Estimating a Baseline Control State

• Compute overall sample p• Derive control charts and identify out of control

pointsUCLp / 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

Plastron Division Summary StatisticsBaseline P-Chart

Inspection Results January thru March 1999

0%5%

10%15%20%25%30%35%40%45%

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51

Ordinal Sample

Per C

ent D

efec

tive

Per Cent Defective Pbar UCL LCL

BaselineControl Chart Data

Shaded cells areAbove UCL or Below LCL

Day DateTotal

RejectsTotal

ProductionPer Cent Defective Pbar UCL LCL

Mon 4-Jan 57 231 24.68% 15.0% 22.1% 8.0%Tue 5-Jan 165 1340 12.31% 15.0% 17.9% 12.1%Wed 6-Jan 144 1619 8.89% 15.0% 17.7% 12.4%Thu 7-Jan 33 179 18.44% 15.0% 23.0% 7.0%Mon 11-Jan 112 1504 7.45% 15.0% 17.8% 12.3%Tue 12-Jan 125 1563 8.00% 15.0% 17.7% 12.3%Wed 13-Jan 147 723 20.33% 15.0% 19.0% 11.0%Thu 14-Jan 63 501 12.57% 15.0% 19.8% 10.2%Fri 15-Jan 33 400 8.25% 15.0% 20.4% 9.7%Mon 18-Jan 7 50 14.00% 15.0% 30.2% 0.0%Tue 19-Jan 308 1319 23.35% 15.0% 18.0% 12.1%Wed 20-Jan 41 357 11.48% 15.0% 20.7% 9.3%Fri 22-Jan 144 1283 11.22% 15.0% 18.0% 12.0%Mon 25-Jan 289 1245 23.21% 15.0% 18.1% 12.0%Wed 27-Jan 106 970 10.93% 15.0% 18.5% 11.6%Thu 28-Jan 71 473 15.01% 15.0% 19.9% 10.1%Mon 1-Feb 250 1088 22.98% 15.0% 18.3% 11.8%Tue 2-Feb 98 560 17.50% 15.0% 19.5% 10.5%Wed 3-Feb 161 486 33.13% 15.0% 19.9% 10.2%Thu 4-Feb 72 278 25.90% 15.0% 21.4% 8.6%Fri 5-Feb 183 1085 16.87% 15.0% 18.3% 11.8%Wed 10-Feb 140 1307 10.71% 15.0% 18.0% 12.1%Thu 11-Feb 302 1298 23.27% 15.0% 18.0% 12.0%Mon 15-Feb 55 503 10.93% 15.0% 19.8% 10.2%Tue 16-Feb 239 1181 20.24% 15.0% 18.1% 11.9%Wed 17-Feb 131 809 16.19% 15.0% 18.8% 11.2%Thu 18-Feb 265 899 29.48% 15.0% 18.6% 11.4%Fri 19-Feb 507 1221 41.52% 15.0% 18.1% 11.9%Mon 22-Feb 458 1228 37.30% 15.0% 18.1% 12.0%Tue 23-Feb 79 529 14.93% 15.0% 19.7% 10.4%Wed 24-Feb 228 2271 10.04% 15.0% 17.3% 12.8%Thu 25-Feb 410 2421 16.94% 15.0% 17.2% 12.8%Mon 1-Mar 97 927 10.46% 15.0% 18.5% 11.5%Tue 2-Mar 116 1305 8.89% 15.0% 18.0% 12.1%Wed 3-Mar 57 510 11.18% 15.0% 19.8% 10.3%Thu 4-Mar 118 882 13.38% 15.0% 18.6% 11.4%Mon 8-Mar 29 377 7.69% 15.0% 20.5% 9.5%Tue 9-Mar 232 2254 10.29% 15.0% 17.3% 12.8%Wed 10-Mar 323 1668 19.36% 15.0% 17.6% 12.4%Thu 11-Mar 37 351 10.54% 15.0% 20.7% 9.3%Tue 16-Mar 368 1967 18.71% 15.0% 17.4% 12.6%Wed 17-Mar 256 1763 14.52% 15.0% 17.6% 12.5%Thu 18-Mar 124 1665 7.45% 15.0% 17.6% 12.4%Fri 19-Mar 202 1442 14.01% 15.0% 17.8% 12.2%Mon 22-Mar 15 265 5.66% 15.0% 21.6% 8.4%Tue 23-Mar 77 1204 6.40% 15.0% 18.1% 11.9%Wed 24-Mar 148 2304 6.42% 15.0% 17.2% 12.8%Thu 25-Mar 31 695 4.46% 15.0% 19.1% 11.0%Fri 26-Mar 117 420 27.86% 15.0% 20.2% 9.8%Mon 29-Mar 245 2446 10.02% 15.0% 17.2% 12.9%Tue 30-Mar 20 140 14.29% 15.0% 24.1% 6.0%

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

Inspection Results January thru March 1999

0%

5%

10%

15%

20%

25%1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51

Ordinal Sample

Per C

ent D

efec

tive

Per Cent Defective Pbar UCL LCL

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

Recommendations forCharlie Hobbs

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

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

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