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PT. PHILIPS INDUSTRIES BATAM - TUNER FACTORY Presents

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  • 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)*

    *