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Asia Pacific 3 Baseline study - FFL and RFL& Shortage Demand in 2009 DMAIC
Citation preview
Asia Pacific1
FINAL REPORTFINAL REPORT
VIETNAM PLANT
BB Project #AP 2165
Active-Activa One Piece Fire Loss Reduction
Asia Pacific2
1. Problem Description Baseline statistics of production loss:
• FF Loss: 26.7% (Y1)• RF Loss: 29.1% (y2)• TTL: 45.3% (y)• Major defects includes 70.5% over total FF defects:
-Clay crack (CC)- loss of total FF pcs (y)-CDT & PHT loss of RF loss …* Shortage Supply vs Demand 15.7% volume around 200 A pcs/month
Project targets FF loss : 18 % RF loss : 21% TT loss : 29.9
B. Executive SummaryB. Executive Summary
Asia Pacific3
Baseline study - FFL and RFL& Shortage Demand in 2009
Trend of FFL
0
10
20
30
40
50
Jan Feb Mar May Jun Jul Aug Sep Oct Nov Dec
%
BSL FFL Target FFL FF Loss%
Trend of RFL
0
20
40
60
80
100
Jan Feb Mar May Jun Jul Aug Sep Oct Nov Dec
%
BSL RFL Target RFL RF Loss %
Trend of TTL
0
20
40
60
80
Jan Feb Mar May Jun Jul Aug Sep Oct Nov Dec
%
Target TTL BSL TTL Total Loss%
D M A I C
Asia Pacific4
Completion Schedule: Define: 01/03/10 Measure: 31/03/10 Analyze: 31/05/10DMAIC Improve: 30/06/10 Control: 31/07/10 Close: 30/09/10
Benefits: (in K USD @ AOP rate)Direct Saving = 214k USD (98 + 117) - Production losses = 98k USD - Sales losses = 117k USD (short supply models)Cash Flow Improvement = 214K USDIndirect Saving = NilProgram Budget: = Nil
Project Leader: Nguyen Phuong (BB)Project Champion: Prasanna MBB: Pittaya Team Members: Do Anh Thai, Nguyen Ngoc Cang, Nguyen Lam , Nguyen T Phong , Nguyen Thanh Hai,,Huynh Anh Tuan ,Le Ngoc Hung
Key Metrics:Primary metric (Y): First Fire Loss = First fire loss (pieces) / total inspection (pieces) Baseline = 26.76% Target = 18% (AOP = Baseline)Secondary metric (y1): Re-fire Loss = Re-fire loss (pieces) / total inspection in re-fire (pieces)Baseline = 29.19% Target = 21% (AOP = Baseline )TTL Baseline = 45.37% Target = 29.90% (vs AOP = 36.2%) = (Clay loss + FF loss + RF loss) / total casting
Key Initiative: Total Fire Loss Reduction for Category Heroes Active & Activa (Vietnam Operations) AP2165
Active-va Fire Loss Reduction - Vietnam
High Fire Losses on Active-Va one piece cause high cost and unstable supply
Count
Fire lossCount
43.0 82.4 100.0
29.19 26.76 11.94Percent 43.0 39.4 17.6Cum %
CL lossFF lossRF loss
30
25
20
15
10
5
0
Active -va fire loss
Coun
t
Perc
ent
FF loss defectCount
8.4 4.6 3.9Cum % 70.5 83.1 91.4 96.1 100.0
16.96 3.02 2.01 1.12 0.94Percent 70.5 12.6
OtherWHPFLECCT
25
20
15
10
5
0
100
80
60
40
20
0
FF loss by defect
Coun
t
Perc
ent
RF loss DefectCount
31.5 10.2 9.8 4.0 1.9Cum % 42.5 74.1 84.2 94.1
38.27
98.1 100.0
28.40 9.16 8.85 3.60 1.75Percent 42.5
OtherPRTLEMattPHTCDT
9080706050403020100
100
80
60
40
20
0
RF loss by defectRF Loss - Pareto
FF Loss - Pareto
D M A I C
Business Errors:- Active –Activa are key models with high/stable demand.
They are strategic products with big benefit. Due to their high losses, supply of these models could not support with high demand.
- Sale revenues loss in EBIT about $ 117k annually. We have shortage around 200 pcs of Active-va / month which are caused by high fire loss.
- Price increase pressure to maintain GM will impact in lost sales & revenue
Barriers:- New model & complex - Lack of skilled workers – Design not complete- Mold
technical & mold condition not stable
Asia Pacific5
Benefit Category (K USD)
DIRECT BENEFITA. Cost Takeout = 97.6
B. Incremental Margin(1) Net Increased Capacity = 116.6(2) New Product Net Sales =
C. Inc Cost Exec =
Total Direct Benefit = 214.3
CASH FLOW BENEFITNet Cash = 214.3
INDIRECT BENEFITA.Cost Avoidance =
B. Cost Savings =
C. Net NVA Reduction = D. Non Financial Benefit=
Project # Project Title BB Champion MBBAP2165 Active-Va One piece Fire loss reduction Nguyen Phuong Prasanna Techarungnirun Pittaya
Detailed Computation by Category
Direct Saving from reduction loss = ((Total pcs FF* cost *( Y BSL- Ytarget) + Total pcs RF *cost *( YBSL - Ytarget))*11.5 month =97690$
Direct saving from Incremental margin= (Total pcs selling * price selling *%margin *% improve)11.5 month =116649$
Total Direct Saving Project = Direct saving from reduction loss + Direct saving from Incremental margin=214340$
AOP Exchange rate = 17,819.65 VND / USD
Microsoft Excel Worksheet
Active-va Fire Loss Reduction - Vietnam D M A I C
Asia Pacific6
2. Solution Strategy Statistical study high-loss models and defect locations Focus team assignment by model Beware of new comers (PF, HL,WH,LE) Apply Six Sigma tools to identify vital factors (Xs) for process
control Key factors to study:
Slip formula, Aging time Mold life , mold condition and design variation Casting system, tools, skill and procedure variations Daily communications among key processes
B. Executive SummaryB. Executive Summary
Asia Pacific7
Support Activities Daily loss court meeting and trouble shooting
- FF major defects- Defect by models- Relevant defect locations- Focus team assignment by product group- Trouble shooting database and follow up- Defect traceability road-map and data collection- Defect location map recording by location
B. Executive SummaryB. Executive Summary
Asia Pacific8
3. Summary of Six Sigma Tool ApplicationMeasurement Phase
CTQ flow chart
Process flow chart
Process mapping (KPIVs, KPOVs)
C&E Matrix
GR&R (A-RF-L)
Baseline data (FFL, RFL, TTL, % defects)
Analysis Phase FMEA summary
SPC charts
Multi-vari studies( T-test, One way Anova, …)
Paretos and location
B. Executive SummaryB. Executive Summary
Asia Pacific9
3.Six Sigma Tool Application
Improvement Phase
GR&R (FF result: A,defect CC & LE)
Data mining experiment (interactions of Humidity & Mold life , OPT, Aging time…).
DOE & RSM
Visual communications (Defect location updates)
Training (caster master,)
Casting procedure modification
B. Executive SummaryB. Executive Summary
Asia Pacific10
3.Six Sigma Tool ApplicationControl PhaseA. Control Plan
B. Defect tracing and feedback daily
C. Slip SPC and control sheet
D. Humidity & temperature record
E. POKAYOKE & Visual factory
F. Casting procedure follow-up
G. Mold status record
H. Casting procedures follow-up
Performance trend charts
- FFL,RFL, TTL (Ys)
- % of cast loss, CC, PF and new comers (ys)
B. Executive SummaryB. Executive Summary
Asia Pacific11
4. Results and Conclusions Active –va one piece performance has been improved from w4.Apr 2010 and achieved stable results through Jul.2010
B. Executive SummaryB. Executive Summary
Trend Chart TT Loss
0.0010.0020.0030.0040.0050.0060.00
Time
%
TTL% BSL % Target %
Trend Chart FF Loss
0.0010.0020.0030.0040.00
Time
%
FF loss % BSL FF Target FF
Trend Chart RF Loss
0.0010.0020.0030.0040.00
Time
%
RF loss % BSL RF Target RF
Asia Pacific12
Monthly saving & YTD summary
OUT PUT INCREASE
0
500
1,000
Monthly
$ Total A pcs increase saving
Transfer A
Total A pcsincrease saving
129 34 42 55 210 216 129
Transfer A 556 221 576 498 741 835 723
Jan'10
Feb'10
Mar'10
Apr'10
May'10
Jun'10
Jul'10
DIRECT & INDIRECT SAVING USD
0.0
20000.0
40000.0
60000.0
Monthly
$
Total Direct saving $
Indirect Saving $
Total Direct saving $ 560230051766201533043221314711787048
Indirect Saving $ 276017381536193321412636199238145338
Nov' Dec' Jan' Feb' Mar' Apr' May' Jun' Jul'
Early Project Saving
Asia Pacific13
II. MEASUREMENT PHASEII. MEASUREMENT PHASE
A. Process DescriptionB. Process Map SummaryC. Cause & Effects Fishbone DiagramD. Cause & Effects Matrix SummaryE. GR& R
D M A I C
Asia Pacific14
Drying Spraying
Firing
White inspection
Fail
Pass
Rework
Fai
Pass
Loss Loss
Green finishing& inspection
Mold prep. Mold assembling Bench prep. Pouring
DrainingDemoldingPunching
Casters
Trap glaze
Loading
Glost inspcection
CASTING
ThixoResidue
Slip Making
Model/Mold designMold fitSurface quality
Slip pumping
Circulating timeSlip line design
Leakage
Mold installation
Mold maintenaceBench design
Mold Making
Slip rate Time
CleaningDrying
Predrying
Loss
Feed
back
Glaze dryness
Generator Air Compressure
Sponging
Feed
back
Process Flow ChartProcess Flow Chart
Casting Environment : Humidity & temperature
Critical Process
D M A I CA. Process Description
VC Plumbing Vietnam bird-viewVC Plumbing Vietnam bird-view• Plant Operation: started Jan. 1997Plant Operation: started Jan. 1997• Headcount: approx. 300 employeesHeadcount: approx. 300 employees• Production schedule: 2 shifts x 6 days/week Production schedule: 2 shifts x 6 days/week • Plant Capacity:Plant Capacity:
- Output: avg. 35000“A” pcs/ month- Output: avg. 35000“A” pcs/ month
Asia Pacific15
Process Map FF SummaryD M A I C
Asia Pacific16
Km/l
Personnel
Machines
Materials
Methods
Measurements
Environment
% FF Loss
Caster ,Glost inspector , Kiln operator, Sprayer
+ Operator skill
-Humidity
-Temperature
- Dirty
-Bast Wash
+ Plastic Mold
+ Raw materials Glaze quality
+ Slip Quality
+ Duels
+ Kiln equipment
+ Spray gun
+ Glost equipment
+ Case Mold
+ casting tool
+ Training procedure
+ Standard methods sheet
+ Slip Formula
+ handling
+ Master meter pressure
+ Humidity Meter
+ Mold Life
CAUSE & EFFECT FISHBONE DIAGRAM D M A I C
Asia Pacific17
C&E Metric Summary FFD M A I C
Asia Pacific18
Process Map RFD M A I C
Asia Pacific19
MEASUREMENT STUDY-GR&RD M A I C
Asia Pacific20
GR&R (A-RF-L)D M A I C
MS is Acceptable < 10%
Need Improve more
Asia Pacific21
III. ANALYSIS PHASE
A. FFL% with CCL% correlation B. High volume defect by Location focus C. CCL defect happen high mold lifeD. Caster performance on FFL defect & impact of Aging time to FFLE. CDT & PHT defect loss high ratio on RFL
D M A I C
Asia Pacific22
FFL% vs CCL % correlation
Regression Analysis: FFL % versus CCL %
The regression equation is
FFL % = 5.50 + 1.29 CCL %
Predictor Coef SE Coef T P
Constant 5.496 2.373 2.32 0.033
CCL % 1.2947 0.1680 7.70 0.000
S = 3.56428 R-Sq = 76.7% R-Sq(adj) = 75.4%
Analysis of Variance
Source DF SS MS F P
Regression 1 754.06 754.06 59.36 0.000
Residual Error 18 228.67 12.70
Total 19 982.74
Unusual Observations
Obs CCL % FFL % Fit SE Fit Residual St Resid
8 11.0 28.194 19.755 0.885 8.439 2.44R
High correlation CCL vs FFLCC Defect happen with high ratio >70%FFL
on Active one piece in 2009 Need focus to improve CCL to reduce FFL
D M A I C
Asia Pacific23
CCL % BY HIGH VOLUME LOCATION ON PRODUCT
CCL happen so much on O.A.P.P2,H location
O
Reduce CCL on these location O,A,P,H to improve CCL.
Picture to illustrate Product locationD M A I C
A A
H
P2,P
H
Asia Pacific24
CORRELATION CCL % BY LOCATION VS MOLD LIFE
Two-Sample T-Test and CI: CCT Loss, Mold Life
Two-sample T for CCT Loss
ML N Mean StDev SE Mean
High 5 56.40 7.83 3.5
Low 5 22.00 4.00 1.8
Difference = mu (High) - mu (Low)
Estimate for difference: 34.4000
95% CI for difference: (24.2927, 44.5073)
T-Test of difference = 0 (vs not =): T-Value = 8.75 P-Value = 0.000 DF = 5
Significant Mold life high & Low different CCL
CCL by location A, H,P happen on mold Which one have high mold life . Need focus to find out what’s mold life is best setting for improvement
D M A I C
Asia Pacific25
One-way ANOVA: 1070, 1139, 1187, 1293, 1463, 1659, 1666, 1906, 1907
Source DF SS MS F P
Factor 8 380.1 47.5 1.38 0.254
Error 25 862.9 34.5
Total 33 1243.0
S = 5.875 R-Sq = 30.58% R-Sq(adj) = 8.36% Individual 95% CIs For Mean Based on
Pooled StDev
Level N Mean StDev ---------+---------+---------+---------+
1070 4 11.000 5.824 (--------*-------)
1139 4 11.675 4.167 (--------*-------)
1187 4 14.825 3.686 (-------*--------)
1293 4 7.025 4.225 (--------*--------)
1463 4 7.300 3.032 (-------*--------)
1659 4 9.445 7.852 (-------*--------)
1666 4 9.025 6.989 (--------*--------)
1906 3 18.667 10.599 (---------*---------)
1907 3 10.400 3.940 (---------*---------)
---------+---------+---------+---------+
7.0 14.0 21.0 28.0Pooled StDev = 5.875
CCT Loss Defect by Caster
CCT Loss By caster on Mar’10
CCT loss between casters have big variation .Specially on ID 1659 ,1666,1659,1187 & 1097 with big variation & high mean.On Mar’ these ID caster continue with CCT loss high %. Need improve Caster performance
D M A I CCCL DEFECT WITH CATSERS PERFORMANCE
Asia Pacific26
CCL DEFECT WITH SLIP AGING TIME
Aging
time
95% Bonferroni Confidence Intervals for StDevs
2.9
2.5
1086420
Aging
time
CCL
2.9
2.5
161412108642
F-Test
0.017
Test Statistic 29.62P-Value 0.000
Levene's TestTest Statistic 7.33P-Value
Test for Equal Variances for CCL
On Low aging time 2.5 have variation so much & Mean % CCT defect higher than High aging time 2.9. So if continue maintain and increase more Aging time to standardization. We can improve more defect performance
Significant different
D M A I C
Asia Pacific27
% RFL BY CDT ,PHT & MATT LOSS DEFECT
CDT & PHT loss 75% on RFL
D M A I C
Asia Pacific28
1 2
0
5
10
Kiln
CC
%
Boxplots of CC% by Kiln(means are indicated by solid circles)
Defect loss by kiln No
Two-Sample T-Test and CI: CC%, Kiln
Two-sample T for CC%
Kiln N Mean StDev SE Mean1 53 2.56 2.10 0.292 81 2.86 2.37 0.26
Difference = mu (1) - mu (2)Estimate for difference: -0.30595% CI for difference: (-1.078, 0.468)T-Test of difference = 0 (vs not =): T-Value = - 0.78 P-Value = 0.436 DF = 120
P>0.05No difference
between 2 kilns
D M A I C
Asia Pacific29
FMEA SUMMARYD M A I C
Asia Pacific30
IV. IMPROVEMENT PHASE
A. Rifle Shot study B. Multi-vari Studies on CCT defect by location C. Improve mold condition/modification & Mold life D. Improve Caster performance/allocation & Aging timeE. Multi-vari studies on RF loss CDT& PHTF. Data mining DOE on effects of Mold life & HumidityG. Defects monitoring and daily feedbackH. Casting procedures follow-up
D M A I C
Asia Pacific31
D M A I C
CCT LOSS BY LOCATION NEED TO IMPROVE
I> O location need to improve
II> A location need to improve
Iii> H location need to improve
IV> P2,P location Need to improve
Asia Pacific32
Reduction CCL on location O
Location O : 24% on CCL %Rifle Shot study
-Poor corner need
=> Change radius dia
-Poor moving short pad
-=> Change pad by soft pillow
-Thickness not enough
Make thicker more 3mm
Visual oil check
Soft pillowVisual Oil check
D M A I C
Asia Pacific33
Reduction CCL on location O
One-way ANOVA: Before, After
Source DF SS MS F P
Factor 1 293.2 293.2 4.80 0.043
Error 17 1038.1 61.1
Total 18 1331.3
S = 7.814 R-Sq = 22.02% R-Sq(adj) = 17.44%
Individual 95% CIs For Mean Based on
Pooled StDev
Level N Mean StDev --+---------+---------+---------+-------
Before 11 12.770 9.552 (---------*--------)
After 8 4.813 4.237 (-----------*----------)
--+---------+---------+---------+-------
0.0 5.0 10.0 15.0
Pooled StDev = 7.814
Significant improve CCL at O location
D M A I C
Before W3 of Apr’10 After is from W4 of Apr- W4 of jul’10
Asia Pacific34
Reduction CCL on location A
Rifle Shot study Location A : 20 % on CCT %
-Humidity at location so hard to control , Especially at high mold life times
Cheese cloth cover
Air fan to dry
Green finishing on transfer dryer date
Standard loose mold
Cheese cloth cover
D M A I C
Air Fan to dry
Asia Pacific35
Reduction CCL on location A
One-way ANOVA: A Before, A After
Source DF SS MS F P
Factor 1 300.7 300.7 5.84 0.042
Error 8 411.8 51.5
Total 9 712.5
S = 7.174 R-Sq = 42.21% R-Sq(adj) = 34.98%
Individual 95% CIs For Mean Based on
Pooled StDevLevel N Mean StDev -------+---------+---------+---------+--
A Before 5 27.309 7.893 (----------*----------)
A After 5 16.341 6.376 (---------*----------)
-------+---------+---------+---------+--
14.0 21.0 28.0 35.0
Pooled StDev = 7.174
Significant improve CCL at A location
D M A I C
Before W1 of May After is from w2 of May – w2 of Aug’10
Asia Pacific36
Rifle Shot study
Reduction CCL on location H (Foot core)
=>New comer : need to improve mold design : make spagless for foot core .
=> Back up foot core ( 2 foot core for 1 mold set
D M A I C
Asia Pacific37
Reduction CCL on location H (Foot core)
Two-Sample T-Test and CI: Before, After
Two-sample T for Before vs After
N Mean StDev SE Mean
Before 7 13.83 7.03 2.7
After 8 8.75 2.01 0.71
Difference = mu (Before) - mu (After)
Estimate for difference: 5.07901
95% CI for difference: (-1.65354, 11.81156)
T-Test of difference = 0 (vs not =): T-Value = 1.85 P-Value = 0.114 DF = 6
Have improve but not so much . NPD research & develop mold technical .How to spagless foot core to control mold humidity
D M A I C
Asia Pacific38
D M A I C
•Most of location be reduced CCT loss , just remain P2,P location still not yet improve .
•So let take action to improve P2,P location in next step
Asia Pacific39
Reduction CCL on location P2& P
Rifle Shot study
Do experiment on 1 bench with 1 opt . Result after one month
Two-sample T for before_1 vs after_1
N Mean StDev SE Mean
before_1 8 0.2275 0.0423 0.015
after_1 4 0.06750 0.00957 0.0048
Difference = mu (before_1) - mu (after_1)
Estimate for difference: 0.160000
95% CI for difference: (0.123757, 0.196243)
T-Test of difference = 0 (vs not =): T-Value = 10.18 P-Value = 0.000 DF = 8
Significant improve
D M A I C
Location P2,P: is hollow .
=> Modified solid
Result
Asia Pacific40
Observation
Indiv
idual
Value
161412108642
3.0
2.9
2.8
_X=2.9263
UCL=3.0504
LCL=2.8021
Observation
Movin
g Ran
ge
161412108642
0.16
0.12
0.08
0.04
0.00
__MR=0.0467
UCL=0.1525
LCL=0
I-MR Chart of Aging time1
Increase Aging time
Before After
D M A I C
Aging time Ave: 2.92 day
After is from w1 of Jun still Jul’10
Asia Pacific41
Improve Caster Performance
Rifle Shot study
Data
40
30
20
10
0
ter, 1463 before, 1463 After, 1293 before, 1293 After, 1632 After, 1070 before, 107
One-way ANOVA: 1907 Before, 1907 After, 1666 before, 1666 After, ...
Source DF SS MS F P
Factor 18 2092.3 116.2 3.94 0.000
Error 35 1031.4 29.5
Total 53 3123.7
S = 5.429 R-Sq = 66.98% R-Sq(adj) = 50.0
Individual 95% CIs For Mean Based on
Pooled StDev
Level N Mean StDev ----+---------+---------+---------+-----
1907 Before 3 17.937 8.362 (----*----)
1907 After 3 4.127 3.606 (----*-----)
1666 before 3 6.754 1.537 (-----*----)
1666 After 3 3.733 3.607 (----*----)
1139 before 3 7.810 6.769 (-----*----)
1139 After 3 6.746 1.787 (-----*----)
1463 before 3 11.333 4.163 (----*-----)
1463 After 3 8.238 5.918 (----*----)
1293 before 3 25.317 14.444 (----*----)
1293 After 3 1.111 1.925 (----*----)
1632 After 2 3.095 0.337 (------*-----)
1070 before 3 3.333 5.774 (-----*----)
1070 after 3 3.000 2.646 (----*-----)
1659 before 3 11.746 4.399 (-----*----)
1659 After 3 3.333 5.774 (-----*----)
1187 Before 3 6.667 2.309 (-----*----)
1187 After 3 5.333 0.577 (----*-----)
1184 Before 1 25.000 * (--------*--------)
1184 After 3 7.953 2.228 (-----*----)
Significant improvement Caster Performance
=>Run WCP effectively
-=> Standard casting procedure
-=> PP program Effectively & Incentive
D M A I C
Asia Pacific42
CCL After VS Before Take Action
Two-Sample T-Test and CI: Before, After
Two-sample T for Before vs After
N Mean StDev SE Mean
Before 19 15.31 3.95 0.91
After 13 8.61 2.14 0.59
Difference = mu (Before) - mu (After)
Estimate for difference: 6.70320
95% CI for difference: (4.48224, 8.92417)
T-Test of difference = 0 (vs not =): T-Value = 6.18 P-Value = 0.000 DF = 28
Test Mold life
Normal distribution
Significantly CCL improve before vs After is from May – Jul’10
D M A I C
Asia Pacific43
Reduction CDT & PHT defect Loss on RF Loss D M A I C
Data
Special programNormal Program
24
22
20
18
16
14
12
10
Boxplot of Normal Program, Special program
Two-Sample T-Test and CI: Normal Program, Special program
Two-sample T for Normal Program vs Special program
N Mean StDev SE Mean
Normal Program 7 20.73 1.67 0.63
Special program 7 12.24 1.99 0.75
Difference = mu (Normal Program) - mu (Special program)
Estimate for difference: 8.48571
95% CI for difference: (6.32515, 10.64628)
T-Test of difference = 0 (vs not =): T-Value = 8.64 P-Value = 0.000 DF = 11
Rifle Shot study
Normal Kiln program
Special Kiln program
Set up special kiln program to run for Some heavy & complex model including : Active to reduce CDT ,PHT
Significant different between Kiln program
Asia Pacific44
Reduction CDT & PHT defect loss on RF Loss D M A I C
Two-Sample T-Test and CI: PHT% Before, PHT % After
Two-sample T for PHT% Before vs PHT % After
N Mean StDev SE Mean
PHT% Before 6 6.92 1.69 0.69
PHT % After 7 4.663 0.833 0.32
Difference = mu (PHT% Before) - mu (PHT % After)
Estimate for difference: 2.25212
95% CI for difference: (0.45997, 4.04427)
T-Test of difference = 0 (vs not =): T-Value = 2.97 P-Value = 0.021 DF = 7
CDT & PHT Improve from Nov’09 to Jul’10
Good trend
Asia Pacific45
DOE OBJECTIVE
The DOE was started from 7-Jun To 12 Jul ’10
Key Variable Used : Mold Life , OPT , Humidity , Slip formula, Slip Aging time
Research interaction of Mold life , Humidity impact to CC Loss defect on Active one piece
Find out Best combination between mold Life VS humidity with the best result on Active one piece
D M A I C
Asia Pacific46
Data mining –CC% vs FACTORS (DOE)Data mining with coded factors
D M A I C
Asia Pacific47
DOE-CCL VS MOLD LIFE & HUMIDITY
Tabulated statistics: Mold life, Humidity
Rows: Mold life Columns: Humidity
off on All
45 0.1633 0.0467 0.1050
0.05774 0.04041 0.07791
3 3 6
51 0.2033 0.1233 0.1633
0.00577 0.03786 0.05007
3 3 6
57 0.2233 0.1367 0.1800
0.01155 0.00577 0.04817
3 3 6
63 0.2833 0.1767 0.2300
0.01155 0.03215 0.06229
3 3 6
69 0.3033 0.2700 0.2867
0.02309 0.03464 0.03204
3 3 6
75 0.3267 0.3033 0.3150
0.03512 0.02309 0.02950
3 3 6
All 0.2506 0.1761 0.2133
0.06485 0.09394 0.08806
18 18 36
Cell Contents: Result : Mean
Result : Standard deviation
Mold life frome 69-75 got high CCL .So that why we just focus mold life from 57-63
D M A I C
Asia Pacific48
ANOVA: Result versus Mold life, Humidity
Factor Type Levels Values
Mold life fixed 6 45, 51, 57, 63, 69, 75
Humidity fixed 2 off, on
Analysis of Variance for Result
Source DF SS MS F P
Mold life 5 0.188033 0.037607 32.57 0.000
Humidity 1 0.049878 0.049878 43.19 0.000
Error 29 0.033489 0.001155
Total 35 0.271400
S = 0.0339822 R-Sq = 87.66% R-Sq(adj) = 85.11%
DOE-CCL VS MOLD LIFE & HUMIDITY
Mold life & Humidity impact so much to CC defect loss 70% & 18.3%
D M A I C
Asia Pacific49
Model Cast date cast pcs/dayMold lifeHumidityHumidity1 OPT Slip formulaAging time loss pcs Result StdOrder RunOrder Blocks CenterPt StdOrder_1RunOrder_12010 7-Jun 13 45 off -1 1632 B-36 68 2 0.23 1 1 1 1 1 12010 8-Jun 14 51 off -1 1632 B-36 68 3 0.21 2 2 1 1 2 22010 9-Jun 13 57 off -1 1632 B-36 68 3 0.23 3 3 1 1 3 32010 10-Jun 14 63 off -1 1632 B-36 68 4 0.29 4 4 1 1 4 42010 11-Jun 15 69 off -1 1632 B-36 68 4 0.33 5 5 1 1 5 52010 12-Jun 15 75 off -1 1632 B-36 68 4 0.33 6 6 1 1 6 62010 13-Jun 14 45 on 1 1632 B-36 68 0 0.07 7 7 1 1 7 72010 14-Jun 14 51 on 1 1632 B-36 68 2 0.14 8 8 1 1 8 82010 15-Jun 15 57 on 1 1632 B-36 68 2 0.13 9 9 1 1 9 92010 16-Jun 14 63 on 1 1632 B-36 68 3 0.14 10 10 1 1 10 102010 17-Jun 13 69 on 1 1632 B-36 68 3 0.23 11 11 1 1 11 112010 18-Jun 14 75 on 1 1632 B-36 68 4 0.29 12 12 1 1 12 122010 19-Jun 15 45 off -1 1632 B-36 68 1 0.13 13 13 1 1 13 132010 20-Jun 15 51 off -1 1632 B-36 68 2 0.2 14 14 1 1 14 142010 21-Jun 13 57 off -1 1632 B-36 68 2 0.23 15 15 1 1 15 152010 22-Jun 14 63 off -1 1632 B-36 68 4 0.29 16 16 1 1 16 162010 23-Jun 14 69 off -1 1632 B-36 68 4 0.29 17 17 1 1 17 172010 24-Jun 14 75 off -1 1632 B-36 68 5 0.36 18 18 1 1 18 182010 25-Jun 15 45 on 1 1632 B-36 68 1 0.07 19 19 1 1 19 192010 26-Jun 13 51 on 1 1632 B-36 68 1 0.08 20 20 1 1 20 202010 27-Jun 14 57 on 1 1632 B-36 68 2 0.14 21 21 1 1 21 212010 28-Jun 15 63 on 1 1632 B-36 68 3 0.19 22 22 1 1 22 222010 29-Jun 14 69 on 1 1632 B-36 68 4 0.29 23 23 1 1 23 232010 30-Jun 15 75 on 1 1632 B-36 68 5 0.33 24 24 1 1 24 242010 1-Jul 15 45 off -1 1632 B-36 68 2 0.13 25 25 1 1 25 252010 2-Jul 15 51 off -1 1632 B-36 68 2 0.2 26 26 1 1 26 262010 3-Jul 14 57 off -1 1632 B-36 68 2 0.21 27 27 1 1 27 272010 4-Jul 15 63 off -1 1632 B-36 68 4 0.27 28 28 1 1 28 282010 5-Jul 14 69 off -1 1632 B-36 68 4 0.29 29 29 1 1 29 292010 6-Jul 14 75 off -1 1632 B-36 68 4 0.29 30 30 1 1 30 302010 7-Jul 14 45 on 1 1632 B-36 68 0 0 31 31 1 1 31 312010 8-Jul 13 51 on 1 1632 B-36 68 2 0.15 32 32 1 1 32 322010 9-Jul 14 57 on 1 1632 B-36 68 2 0.14 33 33 1 1 33 332010 10-Jul 14 63 on 1 1632 B-36 68 3 0.2 34 34 1 1 34 342010 11-Jul 14 69 on 1 1632 B-36 68 4 0.29 35 35 1 1 35 352010 12-Jul 14 75 on 1 1632 B-36 68 4 0.29 36 36 1 1 36 36
DOE – CC% vs Mold life ,humidity ,OPT ( 3 input, 2 level )
Data mining work sheet ( Red- code)D M A I C
Asia Pacific50
Factorial Fit: Result versus Mold life, Humidity, OPT NOTE * This design has some botched runs. It will be analyzed using a
regression approach.
Estimated Effects and Coefficients for Result (coded units)
Term Effect Coef SE Coef T P
Constant 0.20979 0.004923 42.61 0.000
Mold life 0.04046 0.02023 0.001441 14.04 0.000
Humidity -0.07208 -0.03604 0.004923 -7.32 0.000
OPT 0.00708 0.00354 0.004923 0.72 0.478
Mold life*Humidity 0.00718 0.00359 0.001441 2.49 0.019
Mold life*OPT -0.00361 -0.00180 0.001441 -1.25 0.221
Humidity*OPT -0.02125 -0.01062 0.004923 -2.16 0.040
Mold life*Humidity*OPT -0.00461 -0.00230 0.001441 -1.60 0.121
S = 0.0278501 R-Sq = 92.00% R-Sq(adj) = 90.00%
Analysis of Variance for Result (coded units)
Source DF Seq SS Adj SS Adj MS F P
Main Effects 3 0.235499 0.203099 0.0676998 87.28 0.000
2-Way Interactions 3 0.012202 0.012202 0.0040674 5.24 0.005
3-Way Interactions 1 0.001981 0.001981 0.0019811 2.55 0.121
Residual Error 28 0.021718 0.021718 0.0007756
Lack of Fit 16 0.009668 0.009668 0.0006042 0.60 0.830
Pure Error 12 0.012050 0.012050 0.0010042
Total 35 0.271400
Main effect Mold life , Humidity
Interaction : Mold life & Humidity .
DOE – CC% vs Mold life ,humidity ,OPT ( 3 input, 2 level )D M A I C
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Factorial Fit: Result versus Mold life, Humidity
* NOTE * This design has some botched runs. It will be analyzed using a
regression approach.
Estimated Effects and Coefficients for Result (coded units)
Term Effect Coef SE Coef T P
Constant 0.21333 0.005011 42.57 0.000
Mold life 0.04200 0.02100 0.001467 14.31 0.000
Humidity -0.07444 -0.03722 0.005011 -7.43 0.000
Mold life*Humidity 0.00838 0.00419 0.001467 2.86 0.007
S = 0.0300661 R-Sq = 89.34% R-Sq(adj) = 88.34%
Analysis of Variance for Result (coded units)
Source DF Seq SS Adj SS Adj MS F P
Main Effects 2 0.235098 0.235098 0.117549 130.04 0.000
2-Way Interactions 1 0.007375 0.007375 0.007375 8.16 0.007
Residual Error 32 0.028927 0.028927 0.000904
Lack of Fit 8 0.006394 0.006394 0.000799 0.85 0.569
Pure Error 24 0.022533 0.022533 0.000939
Total 35 0.271400
Reduce model DOE – red code . OPT exclude
D M A I C
Mold life, Humidity & Interaction ML & Humidity impact to CC loss
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Humidity
Mea
n
onoff
0.300
0.275
0.250
0.225
0.200
0.175
0.150
Moldlife5763
Interaction Plot (data means) for Result
Mold life , humidity effect so much to CC%
DOE – Fractional factorial ( red code )
Let do with 2 factors , 3 replicates with 3 center point ( mold life 57& 63)
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Factorial Fit: Result versus mold life, humidity
Estimated Effects and Coefficients for Result (coded units)
Term Effect Coef SE Coef T P
Constant 0.19417 0.011286 17.20 0.000
mold life 0.01167 0.00583 0.011286 0.52 0.614
humidity -0.06444 -0.03222 0.009215 -3.50 0.004
mold life*humidity -0.00833 -0.00417 0.011286 -0.37 0.718
Ct Pt -0.02250 0.019547 -1.15 0.270
S = 0.0390950 R-Sq = 51.77% R-Sq(adj) = 36.93%
Analysis of Variance for Result (coded units)
Source DF Seq SS Adj SS Adj MS F P
Main Effects 2 0.0190972 0.0190972 0.0095486 6.25 0.013
2-Way Interactions 1 0.0002083 0.0002083 0.0002083 0.14 0.718
Curvature 1 0.0020250 0.0020250 0.0020250 1.32 0.270
Residual Error 13 0.0198694 0.0198694 0.0015284
Lack of Fit 1 0.0017361 0.0017361 0.0017361 1.15 0.305
Pure Error 12 0.0181333 0.0181333 0.0015111
Total 17 0.0412000
DOE –Full factorial s
Ct Pt not significant & curvature not significant
Let use RSM to analysis
D M A I C
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Response Surface Regression: Result versus mold life, Humidity1
The analysis was done using coded units.
Estimated Regression Coefficients for Result
Term Coef SE Coef T P
Constant 0.171667 0.015960 10.756 0.000
mold life 0.005833 0.011286 0.517 0.614
Humidity1 -0.032222 0.009215 -3.497 0.004
mold life*mold life 0.022500 0.019547 1.151 0.270
mold life*Humidity1 -0.004167 0.011286 -0.369 0.718
S = 0.03909 R-Sq = 51.8% R-Sq(adj) = 36.9%
Analysis of Variance for Result
Source DF Seq SS Adj SS Adj MS F P
Regression 4 0.021331 0.021331 0.005333 3.49 0.038
Linear 2 0.019097 0.019097 0.009549 6.25 0.013
Square 1 0.002025 0.002025 0.002025 1.32 0.270
Interaction 1 0.000208 0.000208 0.000208 0.14 0.718
Residual Error 13 0.019869 0.019869 0.001528
Lack-of-Fit 1 0.001736 0.001736 0.001736 1.15 0.305
Pure Error 12 0.018133 0.018133 0.001511
Total 17 0.041200
Not different with inferential applied
DOE – RSM ( contour plot)D M A I C
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Tabulated statistics: mold life, Humidity
Rows: mold life Columns: Humidity
off on All
57 0.2300 0.1350 0.1825
0.00000 0.00707 0.05500
2 2 4
60 0.1850 0.1500 0.1675
0.02665 0.00894 0.02633
6 6 12
63 0.2900 0.1650 0.2275
0.00000 0.03536 0.07500
2 2 4
All 0.2150 0.1500 0.1825
0.04790 0.01700 0.04833
10 10 20
Cell Contents: result : Mean
result : Standard deviation
Count
Conclusion of DOE
Compare loss cost
1459.53
773.55
0.00
500.00
1000.00
1500.00
2000.00
Loss cost
$
CCT loss on ML >55 ML used at 55
compare cost by Mold life used
$1,532
$309 $287
$2,127$2,089
$421 $391
$2,901
$0
$1,000
$2,000
$3,000
$4,000
$ mold life used 75mold life used 55
mold life used 75 1531.6 308.62 287.04 2127.26mold life used 55 2088.5 420.85 391.42 2900.81
Cost of mold Labour cost for mold making
Labour cost for operation Total cost
Monthly cost comparison between mold life and loss reduction
Average cost of CCT due to mold life > 55 casting times. This will happen only when we use mold life > 55 times (not happen every month)
This incremental cost happens every month with the same amount
Microsoft Excel Worksheet
1,460
774Average monthly saving
= $ 686
5 5 c a s t i n g t i m e s
7 5 c a s t i n g t i m e s
C h a n g e m o l d e v e r y 7 5 c a s t i n g t i m e s
A v e r a g e C C T d e f e c t s o f 5 2 % h a p p e n d u r i n g m o l d
l i f e o f 5 5 – 7 5
M o l d l i f e l i n e
M o n t h 0
M o n t h 3
M o n t h 6
M o n t h 9
M o n t h 1 2
M o n t h 2 . 3
A s s u m p t i o n s
1 . A v g v o l u m e = 1 1 7 0 c a s t i n g / m o n t h o r 1 0 7 6 f i r i n g / m o n t h
2 . T i m i n g o f h i g h C C T d e f e c t = 0 . 8 4 m o n t h / e a c h 7 5 - c a s t i n g t i m e m o l d l i f e
= 3 . 3 4 m o n t h s / y e a r
A n n u a l c o s t c o m p a r i s o n b e t w e e n m o l d l i f e a n d l o s s r e d u c t i o n
C C T r e d u c t i o n 1 0 0 % f r o m c u t t i n g m o l d l i f e t o 5 5 1 0 0 %
M o l d l i f eC C T l o s s o n M o l d
l i f e > 5 5
I n c r e m e n t a l m o n t h l y c o s t
f r o m m o l d
M o n t h l y c o s t f r o m
C C T
# M o n t h i m p a c t /
y e a r
A n n u a l I m p a c t
7 5 5 2 % 5 , 2 3 7 3 . 3 4 1 7 , 5 1 4 . 3 3
5 5 0 % 7 7 3 . 5 5 1 2 . 0 0 9 , 2 8 2 . 5 8
S a v i n g f r o m r e d u c i n g m o l d l i f e t o 5 5 c a s t i n g t i m e s 8 , 2 3 1 . 7 5
Best Setting : Here We see at Mold life 60 result still in target , so we select mold life 60 we will saving mold life cost 90 $ / 2 months . So Mold life 60 with Humidity on is best setting
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V. CONTROL PHASE
A. Control Plan B. Defect tracing and feedback daily C. Slip SPC and control sheetD. Humidity & temperature recordE. POKAYOKE & Visual factoryF. Casting procedure follow-upG. Mold status recordH. Caster Performance record
C
D M A I CC
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CONTROL PLAN D M A I CC
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DEFECT TRACEBILITY DATABASE D M A I CC
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DEFECT TRACEBILITY SHEET
Daily check and record all defective pcs for quick tracing, actions and feedback
D M A I CC
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Trouble shooting Analysis
DEFECT TRACING AND FEEDBACK D M A I CC
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MOLD STATUS DAILY MONITORING D M A I CC
Broken mold
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DAILY SLIP CONTROL SHEET D M A I CC
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SLIP ONLINE CONTROL D M A I CC
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HUMIDITY & TEMPERATURE Record Da
ta
DA 9h MayDA 9h junDA 9h JulDA 9h Aug
80
70
60
50
40
30
Boxplot of DA 9h Aug, DA 9h Jul, DA 9h jun, DA 9h May
Data
DA 14h MayDA 14H JunDA 14h julDA 14H Aug
90
80
70
60
50
40
30
20
Boxplot of DA 14H Aug, DA 14h jul, DA 14H Jun, DA 14h May
Data
DA 5h MayDA 5h JUnDA 5h JUlDA 5h Aug
80.0
77.5
75.0
72.5
70.0
67.5
65.0
Boxplot of DA 5h Aug, DA 5h JUl, DA 5h JUn, DA 5h May
D M A I CC
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POKAYOKE APPLY & VISUAL FACTORY
Preventive risk CC defect while trap glaze at Spray Dept
Fix location when run Air Fan
D M A I CC
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DAILY CASTING PROCEDURE FOLLOW-UP
Base on Slip property , mold life to set up casting record according daily
D M A I CC
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Caster Performance Tracking
Daily ,Weekly & monthly Individual caster performance be tracked & feedbacked
D M A I CC
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D M A I CCFMEA SUMMARY
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VI. RESULTS SUMMARY
A. FFL ,RFL & TTL% PERFORMANCEB. PROJECT SAVING
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Performance FFL % compare before Action
One-way ANOVA: FFL % before, FFL% After
Source DF SS MS F P
Factor 1 463.2 463.2 44.66 0.000
Error 10 103.7 10.4
Total 11 567.0
S = 3.221 R-Sq = 81.70% R-Sq(adj) = 79.87%
Individual 95% CIs For Mean Based on
Pooled StDev
Level N Mean StDev ---------+---------+---------+---------+
FFL % before 7 26.116 3.910 (----*-----)
FFL% After 5 13.513 1.733 (-----*-----)
---------+---------+---------+---------+
15.0 20.0 25.0 30.0
Pooled StDev = 3.221
PROJECT RESULTS
SignificantlyImproved from Apr-jul’10
Normal distribution
Target 18% Significantly archived
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Result RF Loss after took action
One-way ANOVA: RFloss Bfeore_1, RFloss After_1
Source DF SS MS F P
Factor 1 2046 2046 11.88 0.004
Error 13 2239 172
Total 14 4286
S = 13.12 R-Sq = 47.74% R-Sq(adj) = 43.73%
Individual 95% CIs For Mean Based on
Pooled StDev
Level N Mean StDev ---------+---------+---------+---------+
RFloss Bfeore_1 10 39.09 15.71 (-------*------)
RFloss After_1 5 14.31 2.21 (----------*---------)
---------+---------+---------+---------+
12 24 36 48
Pooled StDev = 13.12
Boxplot of RFloss Bfeore_1, RFloss After_1
Significant improved from Nov’09 to jul’10
PROJECT RESULTS
Normal distribution
Target 21% Significantly archived
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Result TT Loss after took action Significant improved from W1 of Apr to w4 of Jul’10
PROJECT RESULTS
One-way ANOVA: Before, After
Source DF SS MS F P
Factor 1 2487.4 2487.4 34.67 0.000
Error 22 1578.4 71.7
Total 23 4065.7
S = 8.470 R-Sq = 61.18% R-Sq(adj) = 59.41%
Individual 95% CIs For Mean Based on
Pooled StDev
Level N Mean StDev ---+---------+---------+---------+------
Before 14 47.838 10.639 (-----*-----)
After 10 27.188 3.446 (------*------)
---+---------+---------+---------+------
24.0 32.0 40.0 48.0
Pooled StDev = 8.470
Normal distribution
Target 29.1% Significantly archived
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SAVING RESULT
AP2165 - MONTHLY PROJECT SAVING