qcc - qic presentation, mission possible team
TRANSCRIPT
-
Teams IntroductionManager / FacilitatorTeo Kian Cheow: ManufacturingLeaderArief M Ridwan: Functional TrainingMembersA. Arif Rahman: Knowledge Based TrainingEndang SWL: Functional TrainingIda Marlinda: Human ResourceLiza Dewanti: ProductionTeh Sion Chew: Industrial EngineeringMISSION POSSIBLE
-
Supporting MeetingsMission Possible Team MeetingFortnightly Meetings from January 2002 ~ June 20021. Consultative meetings with management.2. Internal Supplier & Customer meetings (Mini Company Meeting)3. Informal meeting with other external organizations (Benchmark)Team Meeting
*
-
MEDIC ApproachTeam applied systematic approach :
*
-
To achieve breakthrough improvement of Direct Labour Cross-Training Certification from single Model Skill to Multi Model, Multi SkillPre-MedicProject SelectionProject Theme :
-
Business impact of the project
- Overcome flexibility loss due to full production transfer from Singapore to Batam - To realize B.U vision : To be no-1 Contract ManufacturingProject SelectionPre-MedicReason for selecting this project : Breakthrough improvement for customer
satisfaction. - It will cost 3 times more to replace an existing customer Link to business process
- To deliver products and solutions faster - To be competitive in price and cost saving
-
Project ImpactsPre-Medic
-
Project ImpactsPre-Medic
-
Link to Business Process Pre-MedicMission Possible Project
-
Project Milestone chartTo Improve Direct Labour Cross-Training Certification in Production from Single Model Skill to Multi Model, Multi SkillPre-Medic
-
UV1300 Familye g :UV1316MK2, UV1316MK3, UV1316T, UV1315MK2, UV1355MK2, UV1356B, UV1317MK3, UV1316SMK3, UV1336B, UV1336K, etcProblem Analysis1. Product Model Flexibility
Each family model have :
- different processes- different characteristic- different lead time of training
*
-
Data on Current Model Flexibility
-
Graph on Current Model FlexibilityGoodNo of model% of multi model
-
StuffingSoldering processProblem Analysis2. Skill FlexibilityRepair all reject from Alignment, Touch up & other electrical testers
*
-
Data on Current Skill Flexibility
-
Bar Chart on Current Skill Flexibility
-
3. Cross-Training Lead-Time Problem Analysis
*
-
3. Cross-Training Lead-Time Problem Analysis
*
-
Data on Current Cross-Training Lead TimeLead Time (Week)Quantity
-
Bar Chart on Current X-Training Lead Time
-
Plan Change Memo
Sufficient lane to runYesM/P Capable ?NoNo OvertimeYesShift deploymentF/T release the lane to ProductionProduction follow upYesNoNo Current Process 12 Steps FI1200 = 4 Weeks UV1300 = 2 WeeksYesNoYesProblem AnalysisCurrent Cross-Training Process Mapping
-
Target SettingBENCHMARK AGAINST :ModelFlexibilityof TotalWorkforceCross TrainingLead TimeBenchmarked ReferencesSkillFlexibilityof TotalWorkforce
*
-
CurrentGoal Indicator1. Model Flexibility2. Skill Flexibility 3. Cross-Training Lead time25%20.3%2-4-TunerMultimediaTarget Setting
-
Reason of Target SettingBased on a careful study of
model mix vs optimization Benchmark against In-house and intra
Philips companys best practice.Aimed for breakthrough improvement
to meet customers dynamic needsand satisfaction (e.g. C.M. Customers)
-
SMART Target Setting
-
Cause & Effect Diagram
-
Verification on possible causesSignificant : > 70% members agree and > 4 incidents in a monthInsignificant : < 70% members agree or < 4 incidents in a month
-
Verification on possible causesSignificant : > 70% members agree and > 4 incidents in a monthInsignificant : < 70% members agree or < 4 incidents in a month
Verification of Causes
MAINCAUSEVERIFICATIONIMPACT
MANNo chance to learnWeekly production planning shows Cross-Training seldom conducted, therefore theSignificant
direct labor stuck on one model and one skill only
Short term contractF/T records skill inventory had proven, short time contract does not obstructInsignificant
flexibility
Do not want to changeTraining record had proven that direct labor prefer to change if opportunityInsignificant
is given
Absence of Drive51.9% respondent did not have motivation towards Cross-Training becauseSignificant
lack of recognition
MACHINEOld technologyEquipment in production is sufficient to adapt technical changes if requiredInsignificant
Poor skill & knowledgeSifficient training needs was provided for technical person required.Insignificant
Does not impact to flexibility drive.
METHODReactive Cross TrainingProduction reised for Cross-Training based on latest minutes requirement onlySignificant
Less practicing across model or skill will cause low speed towards flexibility
Temporary OperatorTraining record shows that 2.4% temporary operator able to adapt flexibility ifInsignificant
there is a chance
Close to EOCNumber of EOC operator is few compare to total number of manpower workforce.Insignificant
(End of Contract)And the frequency only happen on average, one time per month
Lane used for otherProject have always been communicate before so flexibility drive can be doneInsignificant
projectin other line which is available
MATERIALNot availableMaterial will be issued as planning requiredInsignificant
No plan beforeMaterial issued always match with the planning for LM ( Logistic Management)Insignificant
Sheet2
CAUSEVERIFICATIONRESULT
No chance to learnCross-training is rarely conducted, therefore the production operators stuck onSignificant
one model or one skill only
No recognationNo recognation had been given to multi model / multi skill operators will causeSignificant
less motivate towards cross-training
Pasif Cross TrainingCross-training is conducted based on sudden requirement onlySignificant
Less practicing across model or skill will low speed towards training
Sheet3
-
Cause & Effect Diagram
-
INNOVATIVEEFFECTIVENESSTIME FRAMEPRACTICALITYDefineescribeAlternative of Solutions
-
Implementation
-
A. Model FlexibilityResults EvaluationBeforeImplementation
Implementation
*
-
B. Skill Flexibility% of Multi SkillNo of SkillResults EvaluationBeforeImplementation
Implementation
Chart15
0.00174216032
0.0019801982
0.00199203192
0.0032362462
0.25477707012
0.35348360662
0.38797284192
0.51048951052
good
% multi skill
no of skill
Direct Labour: % of multi skill
MC
MEDIC Fact Report
easure
apGap:
45%Primary Metric
Project Name: Mission Possible
Project Leader: Arief M Ridwan
E-mail: [email protected]
Phone: +62 770 611855 ext 15221.259259259318.703703703718.592592592622.888888888920.6%24.9%31.4%50.3%
Project Start: January 20025745055026187859761031858
Last updated: July 200227272727162243324432
Week:MonthSEPOCTNOVDECFEBMARAPRMAY
% Multi4.7%5.3%5.4%4.4%20.6%24.9%31.4%50.3%
Target50%50%50%50%50%50%50%50%
Benchmark30%30%30%30%30%30%30%30%
No of Model2.02.02.02.03.03.03.03.0
MonthSEPOCTNOVDECFEBMARAPRMAY
% multi skill0.2%0.2%0.2%0.3%25.5%35.3%38.8%51.0%
no of skill22222222
OFO0.00%0.11%0.00%0.02%0.06%0.20%0.03%0.02%
1112200345400438
MonthSEPOCTNOVDECFEBMARAPRMAY
UV Model2.02.02.02.01.71.41.11.0
FI Model4.04.04.04.03.43.02.32.0
CPPM458315491185175159135
HPL420420420420420420420420
ontrol22221.71428571431.42857142861.14285714291
onform44443.428571428632.28571428572
Control Action TablePrimary Metric
WhoWhatWhen
AriefTo create quarterly structuredStart form
cross-training schedule beforeMarch
production required20.6%24.9%31.4%50.3%
LindaTo incorporate HR Direct LabourStart form7859761031858
on-line data bankJune162243324432
MonthFEBMARAPRMAY
EndangTo provide certivicate for theStart form% Multi20.6%24.9%31.4%50.3%
direct labors upon complition ofAprilTarget50%50%50%50%
trainingBenchmark30%30%30%30%
LisaApply on putting Customer smilingOnly forNo of Model3.03.03.03.0
/ sad face infront of their directin cross-training
labor's workplaceterm
AriefTo award the multi skill for theStart form
flexible direct laborsAprilMonthFEBMARAPRMAY
% multi skill25.5%35.3%38.8%51.3%
no of skill2222
OFO0.06%0.20%0.03%0.02%
Financial Cost : EUR 1,385,403200345400440
Financial Impact :MonthFEBMARAPRMAY
Financial Eksplanation :UV Model1.71.41.11.0
FI Model3.73.32.62.3
CPPM185175159135
Approved by :HPL420420420420
M PhaseC Phase1.71428571431.42857142861.14285714291
MBB/BBC :3.71428571433.28571428572.57142857142.2857142857
Process Owner :
MC
111111
#REF!
#REF!
#REF!
#REF!
#REF!
#REF!
Duration
X training time ( Week )
CROSS TRAINING DATA
EDI
1
#REF!
beforeafterfollow up (2)
1
#REF!
beforeafterfollow up (3)
00
00
00
00
M
Description : Improve Direct Labor Cross-Training Certification in Production from Single Model Skill to Multi Model & Multi Skill
Metric : Direct Labor : Multi model % workforce
Characterize Target : 50 % workforce at production floor
Characterize Bencmark : 30 %
Projected Financial Impact : EUR 1,000,000
Process Map FileName :
C
No of model
No of model
% Multi
Target
Benchmark
No of Model
% of multi model
Direct labour : Multi model % workforce
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Secondary Metric
Good
good
UV Model
FI Model
CPPM
Time saving ( week )
quantity
Time to market & Customer PPM
0
0
0
0
0
0
0
0
0
0
0
0
good
good
% multi skill
no of skill
% multi skill
No of skill
Direct Labour: % of multi skill
1
#REF!
0000
0000
0000
0000
No of model
No of model
% Multi
Target
Benchmark
No of Model
% of multi model
Direct labour : Multi model % workforce
00
00
00
00
Secondary Metric
Good
good
UV Model
FI Model
CPPM
Time saving ( week )
quantity
Time to market & Customer PPM
0
0
0
0
0
0
0
0
0
0
0
0
good
% multi skill
no of skill
% multi skill
No of skill
Direct Labour: % of multi skill
MEDIC Fact Report
xplore
valuate
Comtribution on the gabPhase E - Tools Used & Associated Support Files :
Tools used :Associated Support Files :
fishbone diagram
bar chart
WeekNo change to learnpassive x-trgAbsence of drive
Couse23%15%8%
efine solution
escribe modified process
Process Change DescriptionPhase D - Tools Used & Associated Support Files :
Tools used :Associated Support Files :
tree diagram
flowchart
mplement
mprove
Counter measures :Improve Actions:
WhatWhoWhenImpactImpactActivityWhowhenRemark% Comp.
Giving chance to learnLizaStart from23%33%Structure routine modelLizaStart fromDuring training
to other model & skillMarchand skill cross-trainingEndangMarch
program
Proactive change modelLizaStart from15%50%Create cross-trainingLizaStart fromQuarterly
and skillEndangMarchschedule beforeEndangMarch
Shion Cproduction requestShion C
Motivation driveLindaJune8%Provide certivicationAllJuneDone
17%/badge and gathering
function for recognition
Stimulate visualizationArifStart fromAplied during
of customer satisfactionMarchcross-training
on the workforceprograms
ppm good
good ppm
good
good
good
good
0
0
0
Couse
Qty ( unit )
Impact (unit)
111111
#REF!
#REF!
#REF!
#REF!
#REF!
#REF!
Duration
X training time ( Week )
CROSS TRAINING DATA
1
#REF!
1
#REF!
Primary Metric
21.259259259318.703703703718.592592592622.888888888920.6%24.9%31.4%50.3%
5745055026187859761031858
27272727162243324432
MonthSEPOCTNOVDECFEBMARAPRMAY
% Multi4.7%5.3%5.4%4.4%20.6%24.9%31.4%50.3%
Target50%50%50%50%50%50%50%50%
Benchmark30%30%30%30%30%30%30%30%
No of Model2.02.02.02.03.03.03.03.0
Direct labour : % of multi skill
MonthSEPOCTNOVDECFEBMARAPRMAY
% multi skill0.2%0.2%0.2%0.3%25.5%35.3%38.8%51.0%
no of skill22222222
1112200345400438
Time to market, Customer PPM
MonthSEPOCTNOVDECFEBMARAPRMAY
UV Model2.02.02.02.01.71.41.11.0
FI Model4.04.04.04.03.43.02.32.0
CPPM458315491185175159135
HPL420420420420420420420420
22221.71428571431.42857142861.14285714291
44443.428571428632.28571428572
E
Multi model lane % at prod. floor
D
Good
I
- pro active training- create drive (motivation)- new x training arrangement- cutting flowchart mapping by 6 steps.
111
#REF!
#REF!
#REF!
Duration
multi model
Direct labour : Multi model % workforce
11
month
no of model
Direct labour : no of multi model
10
0
#REF!
month
no of skill
Direct labour : no of multi skill
111111
#REF!
#REF!
#REF!
#REF!
#REF!
#REF!
Duration
X training time ( Week )
CROSS TRAINING DATA
1
#REF!
1
#REF!
00
00
00
00
00
00
00
00
Description : Making multi model & multi skill to support efficiensiMetric : Multi model lane % at prod floor
CharacterizeTarget : 50 %
Characterize Bencmark : 30 %
Projected Financial Impact :
Process Map FileName :
No of model
% Multi
Target
Benchmark
No of Model
% of multi model
Direct labour : Multi model % workforce
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Secondary Metric
Good
before
after
good
good
UV Model
FI Model
CPPM
Time saving ( week )
quantity
Time to market, Customer PPM
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
good
good
% multi skill
no of skill
% multi skill
No of skill
Direct Labour: % of multi skill
Primary Metric
18.592592592622.888888888920.6%24.9%31.4%50.3%52.7%54.4%
5026187859761031858820793
2727162243324432432431
MonthNOVDECFEBMARAPRMAYJUNJULAUG
% Multi5.4%4.4%20.6%24.9%31.4%50.3%52.7%54.4%
Target50%50%50%50%50%50%50%50%
Benchmark30%30%30%30%30%30%30%30%
No of Model2.02.03.03.03.03.03.03.0
81829455555
MonthNOVDECFEBMARAPRMAYJUNJULAUG
% multi skill11.0%8.3%25.5%35.3%38.8%51.3%53.7%53.7%
no of skill22222222novdecfebmaraprmayjunjul
55512003454004404404260.00%0.02%0.06%0.20%0.03%0.02%0.03%0.10%
Time to market, Customer PPM
MonthNOVDECFEBMARAPRMAYJUNJULAUG
UV Model2.02.01.71.41.11.01.02.0
FI Model4.04.03.43.02.32.02.02.0
CPPM15491185175159135191
HPL420420420420420420420
221.71428571431.42857142861.1428571429111
443.428571428632.2857142857222
good
Direct labour : Multi model % workforce
111
#REF!
#REF!
#REF!
Duration
multi model
Direct labour : Multi model % workforce
11
month
no of model
Direct labour : no of multi model
10
0
#REF!
month
no of skill
Direct labour : no of multi skill
111111
#REF!
#REF!
#REF!
#REF!
#REF!
#REF!
Duration
X training time ( Week )
CROSS TRAINING DATA
1
#REF!
1
#REF!
00
00
00
00
00
00
00
00
00
Description : Making multi model & multi skill to support efficiensiMetric : Multi model lane % at prod floor
CharacterizeTarget : 50 %
Characterize Bencmark : 30 %
Projected Financial Impact :
Process Map FileName :
No of model
% Multi
Target
Benchmark
No of Model
% of multi model
Direct labour : Multi model % workforce
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Secondary Metric
Good
before
after
follow up
good
UV Model
FI Model
CPPM
Time saving ( week )
quantity
Time to market, Customer PPM
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
good
% multi skill
no of skill
% multi skill
No of skill
Direct Labour: % of multi skill
good
good
good
Direct labour : Multi model % workforce
Direct labour % of multiskill
*
-
C. Cross-Training Lead TimeResults EvaluationTerrestrial TunerMultimedia Tuner
*
-
C. Cross-Training Lead Time vs Customer PPMgoodgoodCross-training lead time (week)qualityResults EvaluationBeforeImplementation
Implementation
Chart14
24105
24180
24154.25
24172
1.71428571433.4285714286185.25
1.42857142863175
1.14285714292.2857142857158.5
12135.25
UV Model
FI Model
PPM
Delivery time Vs Customer PPM
MC
MEDIC Fact Report
easure
apGap:
45%Primary Metric
Project Name: Mission Possible
Project Leader: Arief M Ridwan
E-mail: [email protected]
Phone: +62 770 611855 ext 15221.259259259318.703703703718.592592592622.888888888920.6%24.9%31.4%50.3%
Project Start: January 20025745055026187859761031858
Last updated: July 200227272727162243324432
Week:MonthSEPOCTNOVDECFEBMARAPRMAY
% Multi4.7%5.3%5.4%4.4%20.6%24.9%31.4%50.3%
Target50%50%50%50%50%50%50%50%
Benchmark30%30%30%30%30%30%30%30%
No of Model2.02.02.02.03.03.03.03.0
MonthSEPOCTNOVDECFEBMARAPRMAY
% multi skill7.8%8.3%8.2%6.1%25.5%35.3%38.8%51.0%
no of skill22222222
OFO0.00%0.11%0.00%0.02%0.06%0.20%0.03%0.02%
45424138200345400438
MonthSEPOCTNOVDECFEBMARAPRMAY
UV Model2.02.02.02.01.71.41.11.0
FI Model4.04.04.04.03.43.02.32.0
CPPM458315491185175159135
HPL420420420420420420420420
ontrol22221.71428571431.42857142861.14285714291
onform44443.428571428632.28571428572
Control Action TablePrimary Metric
WhoWhatWhen
AriefTo create quarterly structuredStart form
cross-training schedule beforeMarch
production required20.6%24.9%31.4%50.3%
LindaTo incorporate HR Direct LabourStart form7859761031858
on-line data bankJune162243324432
MonthFEBMARAPRMAY
EndangTo provide certivicate for theStart form% Multi20.6%24.9%31.4%50.3%
direct labors upon complition ofAprilTarget50%50%50%50%
trainingBenchmark30%30%30%30%
LisaApply on putting Customer smilingOnly forNo of Model3.03.03.03.0
/ sad face infront of their directin cross-training
labor's workplaceterm
AriefTo award the multi skill for theStart form
flexible direct laborsAprilMonthFEBMARAPRMAY
% multi skill25.5%35.3%38.8%51.3%
no of skill2222
OFO0.06%0.20%0.03%0.02%
Financial Cost : EUR 1,385,403200345400440
Financial Impact :MonthFEBMARAPRMAY
Financial Eksplanation :UV Model1.71.41.11.0
FI Model3.73.32.62.3
CPPM185175159135
Approved by :HPL420420420420
M PhaseC Phase1.71428571431.42857142861.14285714291
MBB/BBC :3.71428571433.28571428572.57142857142.2857142857
Process Owner :
MC
111111
#REF!
#REF!
#REF!
#REF!
#REF!
#REF!
Duration
X training time ( Week )
CROSS TRAINING DATA
EDI
1
#REF!
beforeafterfollow up (2)
1
#REF!
beforeafterfollow up (3)
00
00
00
00
M
Description : Improve Direct Labor Cross-Training Certification in Production from Single Model Skill to Multi Model & Multi Skill
Metric : Direct Labor : Multi model % workforce
Characterize Target : 50 % workforce at production floor
Characterize Bencmark : 30 %
Projected Financial Impact : EUR 1,000,000
Process Map FileName :
C
No of model
No of model
% Multi
Target
Benchmark
No of Model
% of multi model
Direct labour : Multi model % workforce
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Secondary Metric
Good
good
UV Model
FI Model
CPPM
Time saving ( week )
quantity
Time to market & Customer PPM
0
0
0
0
0
0
0
0
0
0
0
0
good
good
% multi skill
no of skill
% multi skill
No of skill
Direct Labour: % of multi skill
1
#REF!
0000
0000
0000
0000
No of model
No of model
% Multi
Target
Benchmark
No of Model
% of multi model
Direct labour : Multi model % workforce
00
00
00
00
Secondary Metric
Good
good
UV Model
FI Model
CPPM
Time saving ( week )
quantity
Time to market & Customer PPM
0
0
0
0
0
0
0
0
0
0
0
0
good
% multi skill
no of skill
% multi skill
No of skill
Direct Labour: % of multi skill
MEDIC Fact Report
xplore
valuate
Comtribution on the gabPhase E - Tools Used & Associated Support Files :
Tools used :Associated Support Files :
fishbone diagram
bar chart
WeekNo change to learnpassive x-trgAbsence of drive
Couse23%15%8%
efine solution
escribe modified process
Process Change DescriptionPhase D - Tools Used & Associated Support Files :
Tools used :Associated Support Files :
tree diagram
flowchart
mplement
mprove
Counter measures :Improve Actions:
WhatWhoWhenImpactImpactActivityWhowhenRemark% Comp.
Giving chance to learnLizaStart from23%33%Structure routine modelLizaStart fromDuring training
to other model & skillMarchand skill cross-trainingEndangMarch
program
Proactive change modelLizaStart from15%50%Create cross-trainingLizaStart fromQuarterly
and skillEndangMarchschedule beforeEndangMarch
Shion Cproduction requestShion C
Motivation driveLindaJune8%Provide certivicationAllJuneDone
17%/badge and gathering
function for recognition
Stimulate visualizationArifStart fromAplied during
of customer satisfactionMarchcross-training
on the workforceprograms
ppm good
good ppm
good
good
good
good
0
0
0
Couse
Qty ( unit )
Impact (unit)
111111
#REF!
#REF!
#REF!
#REF!
#REF!
#REF!
Duration
X training time ( Week )
CROSS TRAINING DATA
1
#REF!
1
#REF!
Primary Metric
21.259259259318.703703703718.592592592622.888888888920.6%24.9%31.4%50.3%
5745055026187859761031858
27272727162243324432
MonthSEPOCTNOVDECFEBMARAPRMAY
% Multi4.7%5.3%5.4%4.4%20.6%24.9%31.4%50.3%
Target50%50%50%50%50%50%50%50%
Benchmark30%30%30%30%30%30%30%30%
No of Model2.02.02.02.03.03.03.03.0
Direct labour : % of multi skill
MonthSEPOCTNOVDECFEBMARAPRMAY
% multi skill7.8%8.3%8.2%6.1%25.5%35.3%38.8%51.0%
no of skill22222222
45424138200345400438
Time to market, Customer PPM
MonthSEPOCTNOVDECFEBMARAPRMAY
UV Model2.02.02.02.01.71.41.11.0
FI Model4.04.04.04.03.43.02.32.0
PPM105180154172185175159135
HPL420420420420420420420420
22221.71428571431.42857142861.14285714291
44443.428571428632.28571428572
E
Multi model lane % at prod. floor
D
Good
I
- pro active training- create drive (motivation)- new x training arrangement- cutting flowchart mapping by 6 steps.
111
#REF!
#REF!
#REF!
Duration
multi model
Direct labour : Multi model % workforce
11
month
no of model
Direct labour : no of multi model
10
0
#REF!
month
no of skill
Direct labour : no of multi skill
111111
#REF!
#REF!
#REF!
#REF!
#REF!
#REF!
Duration
X training time ( Week )
CROSS TRAINING DATA
1
#REF!
1
#REF!
00
00
00
00
00
00
00
00
Description : Making multi model & multi skill to support efficiensiMetric : Multi model lane % at prod floor
CharacterizeTarget : 50 %
Characterize Bencmark : 30 %
Projected Financial Impact :
Process Map FileName :
No of model
% Multi
Target
Benchmark
No of Model
% of multi model
Direct labour : Multi model % workforce
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Secondary Metric
Good
before
after
good
good
UV Model
FI Model
CPPM
Time saving ( week )
quantity
Time to market, Customer PPM
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
good
good
% multi skill
no of skill
% multi skill
No of skill
Direct Labour: % of multi skill
Primary Metric
18.592592592622.888888888920.6%24.9%31.4%50.3%52.7%54.4%
5026187859761031858820793
2727162243324432432431
MonthNOVDECFEBMARAPRMAYJUNJULAUG
% Multi5.4%4.4%20.6%24.9%31.4%50.3%52.7%54.4%
Target50%50%50%50%50%50%50%50%
Benchmark30%30%30%30%30%30%30%30%
No of Model2.02.03.03.03.03.03.03.0
81829455555
MonthNOVDECFEBMARAPRMAYJUNJULAUG
% multi skill11.0%8.3%25.5%35.3%38.8%51.3%53.7%53.7%
no of skill22222222novdecfebmaraprmayjunjul
55512003454004404404260.00%0.02%0.06%0.20%0.03%0.02%0.03%0.10%
Time to market, Customer PPM
MonthNOVDECFEBMARAPRMAYJUNJULAUG
UV Model2.02.01.71.41.11.01.02.0
FI Model4.04.03.43.02.32.02.02.0
CPPM15491185175159135191
HPL420420420420420420420
221.71428571431.42857142861.1428571429111
443.428571428632.2857142857222
good
Direct labour : Multi model % workforce
111
#REF!
#REF!
#REF!
Duration
multi model
Direct labour : Multi model % workforce
11
month
no of model
Direct labour : no of multi model
10
0
#REF!
month
no of skill
Direct labour : no of multi skill
111111
#REF!
#REF!
#REF!
#REF!
#REF!
#REF!
Duration
X training time ( Week )
CROSS TRAINING DATA
1
#REF!
1
#REF!
00
00
00
00
00
00
00
00
00
Description : Making multi model & multi skill to support efficiensiMetric : Multi model lane % at prod floor
CharacterizeTarget : 50 %
Characterize Bencmark : 30 %
Projected Financial Impact :
Process Map FileName :
No of model
% Multi
Target
Benchmark
No of Model
% of multi model
Direct labour : Multi model % workforce
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Secondary Metric
Good
before
after
follow up
good
UV Model
FI Model
CPPM
Time saving ( week )
quantity
Time to market, Customer PPM
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
good
% multi skill
no of skill
% multi skill
No of skill
Direct Labour: % of multi skill
good
good
good
Direct labour : Multi model % workforce
Direct labour % of multiskill
*
-
Process ChangeBEFORE
Plan Change Memo
Sufficient lane to runYesM/P Capable ?NoNo OvertimeYesShift deploymentF/T release the lane to ProductionProduction follow upYesNoNoYesNoYesNew ProcessFI1200 = 2 WeeksUV1300 = 1 WeekOld ProcessFI1200 = 4 WeeksUV1300 = 2 Weeks
-
Good enthusiasm of Direct Labors (achieved through the motivation drive activities) Good cooperation with production during implementation stage Good commitment from cross functional team members. Good support from Management
Reason of Achieving Results
*
-
Tangible Results
*
-
B. Improved Time to Delivery (Contributed by Cross Training Lead Time Reduction)Tangible Results
*
-
Tangible Results
*
-
Tangible Results
*
-
Tangible Results
*
-
Tangible Results
*
-
Intangible Results
*
-
Standardisation
*
-
Standardisation
*
-
Standardisation3. To provide Certificate for the Flexible Direct Labours upon completion of training.
*
-
Standardisation
*
-
Standardisation
5. To award the multi skill badge for the flexible Direct Labours
*
-
Follow-up Action
*
-
Follow-up ResultNo of skill
good
*
-
LEARNINGWe learned :
The application of Retraining Theory from the academic teaching to industrial environment. Recognize that one of the critical success factors for world-class contract manufacturing is agility. Build-up competent resources to keep abreast in meeting the evolving needs of our customers. Motivating people through fun and innovative way. Using internet to obtain customers information.
Project Learning and Sharing
*
-
SHARINGProject Learning and Sharing
-
SHARINGProject Learning and SharingThrough this sharing of best practices our team hopes to contribute to the corporate drive of Transforming into One Philips (TOP)
-
ACHIEVEMENT THROUGH TEAM WORKAttributes for the success :Cross functional skills contributed to effective team workImprove communicationReduce cross-functional barriersTeam members respect each others ideasDecision making based on consensus and members commitment
Working as a team
*
-
Operational & Functional Skill Applicationhave been embarked to the project :
Approach to Cross Training : Theory & Practical Shop Floor Management Manufacturing Flow & PlanningDirect Labor Cross-Training & MonitoringProblem Solving Analysis & ToolsProject Report & PresentationDirect Labor Enforcement & Recognition SchemeObservation & Time StudyTeam Formed:December 2001Meeting Schedule:Every WednesdayDuration:2 hoursAttendance:96%Duration of Project:6 monthsArief M RidwanFunctional Training Liza Dewanti ProductionArif Rahman,
Knowledge Based Training TEAM PROCEEDING, SKILLS AND ROLESEndang SWLFunctional Training Teh Sion ChewIndustrial EngineeringIda MarlindaHuman ResourceWorking as a team
-
SOCIAL ACTIVITIESWORKING AS A TEAM
*
-
CONCLUSIONOn par with market leader Matsushita (50% vs 50%)
Beat competitor in BIPThomson (50% vs 30%)
*
-
THANK YOU
*Selina Han
Explore & Evaluate:In this phase we are searching for the vital few root causes which disturb our process:(click)Analysed per customer why they did not pay certain invoices (click)Analysed for which reasons CN were made. We saw that more then 50% of our credit note are due to quality issues, meaning that our returned goods process was not under control. (click)We also analysed what the main errors were on our own invoices, which we already sent to our customers. After measuring, we saw that the master data inaccuracy and manual errors were the main reasons. (click)We set priorities by using the pareto analysis. The main root causes are:conclusion: There is an insufficient reconciliation between systems, which is caused by the systems not being linked to each other. The returned goods process (RMA process) is insufficiently controlled. On our invoices too many error occur
(click)
-
*Selina Han
Explore & Evaluate:In this phase we are searching for the vital few root causes which disturb our process:(click)Analysed per customer why they did not pay certain invoices (click)Analysed for which reasons CN were made. We saw that more then 50% of our credit note are due to quality issues, meaning that our returned goods process was not under control. (click)We also analysed what the main errors were on our own invoices, which we already sent to our customers. After measuring, we saw that the master data inaccuracy and manual errors were the main reasons. (click)We set priorities by using the pareto analysis. The main root causes are:conclusion: There is an insufficient reconciliation between systems, which is caused by the systems not being linked to each other. The returned goods process (RMA process) is insufficiently controlled. On our invoices too many error occur
(click)
-
*Selina Han
Explore & Evaluate:In this phase we are searching for the vital few root causes which disturb our process:(click)Analysed per customer why they did not pay certain invoices (click)Analysed for which reasons CN were made. We saw that more then 50% of our credit note are due to quality issues, meaning that our returned goods process was not under control. (click)We also analysed what the main errors were on our own invoices, which we already sent to our customers. After measuring, we saw that the master data inaccuracy and manual errors were the main reasons. (click)We set priorities by using the pareto analysis. The main root causes are:conclusion: There is an insufficient reconciliation between systems, which is caused by the systems not being linked to each other. The returned goods process (RMA process) is insufficiently controlled. On our invoices too many error occur
(click)
-
*Selina Han
Explore & Evaluate:In this phase we are searching for the vital few root causes which disturb our process:(click)Analysed per customer why they did not pay certain invoices (click)Analysed for which reasons CN were made. We saw that more then 50% of our credit note are due to quality issues, meaning that our returned goods process was not under control. (click)We also analysed what the main errors were on our own invoices, which we already sent to our customers. After measuring, we saw that the master data inaccuracy and manual errors were the main reasons. (click)We set priorities by using the pareto analysis. The main root causes are:conclusion: There is an insufficient reconciliation between systems, which is caused by the systems not being linked to each other. The returned goods process (RMA process) is insufficiently controlled. On our invoices too many error occur
(click)
-
*Selina Han
Explore & Evaluate:In this phase we are searching for the vital few root causes which disturb our process:(click)Analysed per customer why they did not pay certain invoices (click)Analysed for which reasons CN were made. We saw that more then 50% of our credit note are due to quality issues, meaning that our returned goods process was not under control. (click)We also analysed what the main errors were on our own invoices, which we already sent to our customers. After measuring, we saw that the master data inaccuracy and manual errors were the main reasons. (click)We set priorities by using the pareto analysis. The main root causes are:conclusion: There is an insufficient reconciliation between systems, which is caused by the systems not being linked to each other. The returned goods process (RMA process) is insufficiently controlled. On our invoices too many error occur
(click)
-
INNOVATIVEEFFECTIVENESSTIME FRAMEPRACTICALITYDefineescribeAlternative of Solutions
-
Verification on possible causesSignificant : > 70% members agree and > 4 incidents in a month)Insignificant : < 70% members agree and < 4 incidents in a month
-
Verification on possible causesSignificant : > 70% members agree and > 4 incidents in a month)Insignificant : < 70% members agree and < 4 incidents in a month
-
Verification on possible causesSignificant : > 70% members agree and > 4 incidents in a month)Insignificant : < 70% members agree and < 4 incidents in a month
-
Verification on possible causesSignificant : > 70% members agree and > 4 incidents in a month)Insignificant : < 70% members agree and < 4 incidents in a month
-
Verification on possible causesSignificant : > 70% members agree and > 4 incidents in a month)Insignificant : < 70% members agree and < 4 incidents in a month
-
Verification on possible causesSignificant : > 70% members agree and > 4 incidents in a month)Insignificant : < 70% members agree and < 4 incidents in a month
-
Verification on possible causesSignificant : > 70% members agree and > 4 incidents in a month)Insignificant : < 70% members agree and < 4 incidents in a month
-
Verification on possible causesSignificant : > 70% members agree and > 4 incidents in a month)Insignificant : < 70% members agree and < 4 incidents in a month
-
Verification on possible causesSignificant : > 70% members agree and > 4 incidents in a month)Insignificant : < 70% members agree and < 4 incidents in a month
-
Verification on possible causesSignificant : > 70% members agree and > 4 incidents in a month)Insignificant : < 70% members agree and < 4 incidents in a month
*
-
Verification on possible causesSignificant : > 70% members agree and > 4 incidents in a month)Insignificant : < 70% members agree and < 4 incidents in a month
-
Verification on possible causesSignificant : > 70% members agree and > 4 incidents in a month)Insignificant : < 70% members agree and < 4 incidents in a month
-
Target SettingBENCHMARK AGAINST :
*
*
*******************************Selina Han
Explore & Evaluate:In this phase we are searching for the vital few root causes which disturb our process:(click)Analysed per customer why they did not pay certain invoices (click)Analysed for which reasons CN were made. We saw that more then 50% of our credit note are due to quality issues, meaning that our returned goods process was not under control. (click)We also analysed what the main errors were on our own invoices, which we already sent to our customers. After measuring, we saw that the master data inaccuracy and manual errors were the main reasons. (click)We set priorities by using the pareto analysis. The main root causes are:conclusion: There is an insufficient reconciliation between systems, which is caused by the systems not being linked to each other. The returned goods process (RMA process) is insufficiently controlled. On our invoices too many error occur
(click)*Selina Han
Explore & Evaluate:In this phase we are searching for the vital few root causes which disturb our process:(click)Analysed per customer why they did not pay certain invoices (click)Analysed for which reasons CN were made. We saw that more then 50% of our credit note are due to quality issues, meaning that our returned goods process was not under control. (click)We also analysed what the main errors were on our own invoices, which we already sent to our customers. After measuring, we saw that the master data inaccuracy and manual errors were the main reasons. (click)We set priorities by using the pareto analysis. The main root causes are:conclusion: There is an insufficient reconciliation between systems, which is caused by the systems not being linked to each other. The returned goods process (RMA process) is insufficiently controlled. On our invoices too many error occur
(click)*Selina Han
Explore & Evaluate:In this phase we are searching for the vital few root causes which disturb our process:(click)Analysed per customer why they did not pay certain invoices (click)Analysed for which reasons CN were made. We saw that more then 50% of our credit note are due to quality issues, meaning that our returned goods process was not under control. (click)We also analysed what the main errors were on our own invoices, which we already sent to our customers. After measuring, we saw that the master data inaccuracy and manual errors were the main reasons. (click)We set priorities by using the pareto analysis. The main root causes are:conclusion: There is an insufficient reconciliation between systems, which is caused by the systems not being linked to each other. The returned goods process (RMA process) is insufficiently controlled. On our invoices too many error occur
(click)*Selina Han
Explore & Evaluate:In this phase we are searching for the vital few root causes which disturb our process:(click)Analysed per customer why they did not pay certain invoices (click)Analysed for which reasons CN were made. We saw that more then 50% of our credit note are due to quality issues, meaning that our returned goods process was not under control. (click)We also analysed what the main errors were on our own invoices, which we already sent to our customers. After measuring, we saw that the master data inaccuracy and manual errors were the main reasons. (click)We set priorities by using the pareto analysis. The main root causes are:conclusion: There is an insufficient reconciliation between systems, which is caused by the systems not being linked to each other. The returned goods process (RMA process) is insufficiently controlled. On our invoices too many error occur
(click)*Selina Han
Explore & Evaluate:In this phase we are searching for the vital few root causes which disturb our process:(click)Analysed per customer why they did not pay certain invoices (click)Analysed for which reasons CN were made. We saw that more then 50% of our credit note are due to quality issues, meaning that our returned goods process was not under control. (click)We also analysed what the main errors were on our own invoices, which we already sent to our customers. After measuring, we saw that the master data inaccuracy and manual errors were the main reasons. (click)We set priorities by using the pareto analysis. The main root causes are:conclusion: There is an insufficient reconciliation between systems, which is caused by the systems not being linked to each other. The returned goods process (RMA process) is insufficiently controlled. On our invoices too many error occur
(click)*
*