Engineering Production Control Strategies: A Guide to Tailor Strategies that Unite the Merits of Push and Pull

182 Pages·2012·3.26 MB·English

by  Christoph Karrer (auth.)

Management for Professionals
For furthervolumes:
http://www.springer.com/series/10101
.
Christoph Karrer
Engineering Production
Control Strategies
A Guide to Tailor Strategies that Unite
the Merits of Push and Pull
ChristophKarrer
McKinsey&Company,Inc.
Sophienstr.26
80333Mu¨nchen
Germany
Email:[e-mail]
ISSN2192-8096 e-ISSN2192-810X
ISBN978-3-642-24141-3 e-ISBN978-3-642-24142-0
DOI10.1007/978-3-642-24142-0
SpringerHeidelbergDordrechtLondonNewYork
LibraryofCongressControlNumber:2012933113
#Springer-VerlagBerlinHeidelberg2012
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Foreword
Today’s global supply chains are getting more and more complex. At the same
time,thedemandoncustomerserviceandthecostpressurecontinuetoincrease.In
thischallengingenvironment,productioncontrolstrategies(PCS)playamajorrole.
They manage the physical material flow on the shopfloor and are therefore a key
driverfordeliveryperformance,inventorylevels,andultimatelyproductioncost.
Identifying and customizing suitable control strategies is a challenging task,
especiallywhenproductionsystems have tocope withvariabledemands, forecast
error,andunstableprocesses.
The focus of this book lies on helping companies with complex and discrete
productionsystems totailoraproduction controlstrategytotheir needs.Thereby,
the mutual merits of “push” and “pull” systems are taken into account,leading to
hybridstrategies.Consequently,thebookaddressespractitionerswhoareinterested
inlookingbehindthescenesandintothephysicsofproductioncontrol.
A real-life case study demonstrates the practical applicability of the presented
framework. I would like to thank the company and the involved managers for
enablingthiscooperation.
Moreover,IwouldliketoexpressmygratitudetoProf.Dr.Hans-OttoGu¨nther
and PD Dr.-Ing. Knut Alicke for their support, our fruitful discussions, and for
beinginspirationalmentors.
Iwouldalsoliketothankmyfamilyfortheirconstantsupportandunderstanding
duringthecreationofthisbook.Idedicatethisbooktothem.
Munich,Germany ChristophKarrer
Customers require on-time delivery at a minimal cost. As a result, companies are
constantly under pressure to cut costs and uphold high levels of service. A major
factor for success in achieving these objectives is the right production control
strategy, one in which two approaches compete in practical application – push
andpull.
In push systems, external signals trigger production orders; these signals typi-
callytaketheformofsophisticated,detailed,planningandschedulingalgorithmsin
environmentswithintegratedplanningsystems.Apullsysteminitiatesaproduction
v
vi Foreword
order internally. The consumption of parts of the next step in the value chain
triggers the release of a signal (for example, a Kanban card), which is then
translatedintoaproductionorder.
Bothproduction controlapproacheshave clearbenefitsanddisadvantages,and
should, therefore, be combined. Many companies have already implemented pull
systems as a part of their Lean manufacturing philosophy; this control strategy is
easy to apply, and it limits work in process. Companies with strong planning
systems prefer push systems to, for example, leverage the forecast. Unfortunately
thepushandpullapproachesareoftenappliedinaverydogmaticwaythatdoesnot
capturethebenefitsofeither.
Inhisexcellentbook,ChristophKarrerpresentsamethodforcombiningthetwo
approaches.Heprovidesasoundtheoreticalfoundationforverifyingthebenefitsof
usingafractionoftheexistingforecasttocontroltheproductionsystem.Theresults
arepromising;costandinventorycanbereducedsignificantly,andahighlevelof
service retained. Thebeautyofhis approach is itsrelative simplicityinpractice –
thereisnoneedforsysteminvestmentorradicalchangesinproductioncontrol.In
addition,theKanbansystem–oftenalreadyinplace–canbeleveragedinorderto
implementtheapproach.
Karrer’s book is aimed at practitioners who contend with high fluctuations in
demandandwhowouldliketofurtherreducetheircostsafterimplementingalean
or an integrated planning system. His approach is a breakthrough – it combines
Lean manufacturing (“pull”) and “algorithmic” detailed planning and scheduling
(“push”),andwillfurtherboostsystemperformance.
Karlsruhe,Germany PDDr.-Ing.KnutAlicke
MasterExpertSupplyChainManagement
McKinsey&Company,Stuttgart
The quest for a good production control strategy (PCS) is as old as industrial
production.Extensiveresearchinthefieldhasledtomanyinnovationsthatenable
today’s production systems. The availability of affordable computer technology,
which led to the introduction of IT-based planning systems, was an important
milestone. Another important step was marked by the diffusion of the Lean
manufacturing philosophy from Toyota, comprising the famous Kanban control
system. However, due to the large variety of existing control strategies and the
complexityoftoday’sindustrialpractice,itisdifficultforpractionerstoselectand
continuouslyupdatetheirPCS.
Theengineeringframeworkpresentedinthisbookoffersvaluablesupport.The
strength of the approach is its integrated and practice oriented perspective. The
problem is approached from a systems engineering angle, taking findings from
currentresearchintoaccount.Theresultingstrategiescombinemeritsof“push”and
“pull”systemsandyetremaininlinewiththephilosophyofLeanmanufacturing.
Berlin,Germany Prof.Dr.Hans-OttoGu¨nther
DepartmentofProductionManagement
TechnicalUniversityofBerlin
Contents
1 Introduction ................................................................ 1
1.1 NeedforaPCSEngineeringFramework .............................. 1
1.2 SuitableIndustrialContextforApplication ............................ 2
1.3 StructureoftheBook ................................................... 3
2 ProductionControlStrategies(PCS) ..................................... 7
2.1 FundamentalConceptsandCoherences ................................ 7
2.1.1 PCSintheBroaderContextofProductionPlanning
andControl ....................................................... 7
2.1.2 ThePush/PullEnigmaandTheirBasicImplementations ....... 8
2.1.3 TheOrderPenetrationPoint .................................... 12
2.1.4 TheInfluenceofthePCSonOperationalPerformance ........ 12
2.2 ReviewofCurrentResearchonPCS .................................. 15
2.2.1 SegmentationofLiterature ..................................... 15
2.2.2 PCSMethodDevelopment ..................................... 16
2.2.3 PCSSelection ................................................... 27
2.2.4 PCSImplementation ............................................ 32
2.2.5 RelatedDesignQuestions ...................................... 33
2.3 SynthesisandPositioningoftheFollowingWork .................... 35
3 AQueuingNetworkBasedFrameworkforPCSEngineering ........ 37
3.1 DesignDriversandInitialComplexityReduction .................... 37
3.1.1 StructuringDesignDrivers ..................................... 37
3.1.2 ComplexityReductionbyDefiningPlanningSegments ....... 38
3.2 GenericModelFormulation ........................................... 41
3.2.1 Notation ......................................................... 41
3.2.2 QueuingNetworkRepresentationofPlanningSegments ...... 41
3.2.3 BasicProperties ................................................. 44
3.2.4 AnalysisoftheResultingSolutionSpace ...................... 46
vii
viii Contents
3.3 MappingProductionSystemVariability .............................. 48
3.3.1 AnIntegratedApproachtoStochasticModeling
ofProductionSystemVariability ............................... 48
3.3.2 DefinitionofaMeasureforProductionSystemVariability ... 51
3.4 MappingofDemandVariability ...................................... 53
3.4.1 StochasticModelingofDemandVariability ................... 53
3.4.2 Excursion:PossibleInvolvementofCustomers ................ 57
4 NumericalOptimizationofControlParametersAlong
aPCSEngineeringProcess ............................................... 59
4.1 ObjectiveFunctionDesign ............................................ 59
4.1.1 SelectionofValuationMeasures ............................... 59
4.1.2 ValueAnalysis .................................................. 60
4.2 ImplementationofaSupportingSimulationFramework ............. 65
4.2.1 IntroductiontoDiscrete-EventSimulationandthe
Platform‘AnyLogic’ ............................................ 65
4.2.2 ImplementationofPlanningSegments ......................... 67
4.2.3 ImplementationoftheDemandModel ......................... 69
4.2.4 ImplementationoftheProductionSystemModel ............. 70
4.3 APCSEngineeringProcesstoOptimizeProduction
ControlParameters .................................................... 72
4.3.1 ApproachandProcessOverview ............................... 72
4.3.2 Step1–KiDetermination ...................................... 75
4.3.3 Step2–OPPjDetermination ................................... 78
4.3.4 Step3–FCTjandSijDetermination ........................... 79
5 InvestigationofthePush/PullIntegration .............................. 85
5.1 InfluencingFactorsandRulesfortheDecisionamong
PushandPull .......................................................... 85
5.1.1 HypothesisandExperimentDesign ............................ 85
5.1.2 DeterminationofRelevantFactorsandDerivation
ofDecisionRules ............................................... 88
5.2 Closed-FormDeterminationofthePush/PullIntegration
ParametersFCT*andS%* ............................................ 90
5.2.1 HypothesisandExperimentDesign ............................ 90
5.2.2 DerivationofaClosed-FormParameterization
forHybridControl .............................................. 92
5.2.3 ExtensiontotheMulti-ProductCase ........................... 95
5.3 AnalysisofthePerformanceIncreaseAchievable
withtheHybridStrategy ............................................... 96
5.3.1 DriversfortheRelevanceoftheDecisionamong
HybridandPureStrategies ..................................... 96
5.3.2 DerivationofaDemandVariabilityMeasuretoCharacterize
EnvironmentsThatFavorHybridStrategies ................... 99
5.4 SummaryofInsights .................................................. 101
Contents ix
6 CaseStudyfromtheElectronicsManufacturingIndustry ........... 103
6.1 CaseIntroductionandSpecificChallenges .......................... 103
6.1.1 IntroductiontotheBusinessandManufacturingProcess ..... 103
6.1.2 IdentificationoftheImprovementNeed
andSpecificChallenges ....................................... 104
6.2 ModelDevelopmentandParameterization ........................... 106
6.2.1 ProductionSystemModel ..................................... 106
6.2.2 ParameterizationofPlanningSegments ....................... 107
6.2.3 ParameterizationoftheDemandModel ....................... 108
6.3 PCSDesign ........................................................... 109
6.3.1 StrategyDerivation ............................................ 109
6.3.2 ImpactEvaluation ............................................. 111
6.4 ImplicationsandImplementationHints .............................. 114
7 ConclusionandFurtherResearch ...................................... 117
7.1 ResearchSummary ................................................... 117
7.2 LimitationsandFurtherResearchDirections ........................ 119
8 Appendix .................................................................. 121
8.1 ListofFigures ........................................................ 121
8.2 ListofTables ......................................................... 123
8.3 ListofAbbreviations ................................................. 124
8.4 ListofNotations ...................................................... 125
8.5 ComplexProductionSystems ........................................ 128
8.6 LiteratureReview ..................................................... 128
8.7 DelimitationagainstOtherGenericPCS ............................. 133
8.8 EvolutionoftheMaximumSizeReductionoftheSolution
Space .................................................................. 134
8.9 SimulationModelImplementation ................................... 135
8.9.1 PlanningSegmentGraphicalRepresentation .................. 135
8.9.2 DemandModel:GraphicalRepresentation .................... 135
8.9.3 JavaCodeExecutedatForecastArrival
(Event‘FCarrival’) ............................................ 137
8.10 PCSEngineeringProcess:NumericalOptimization
ofModelParameters ................................................ 138
8.10.1 SetupsforExperimentsandIllustrations .................... 138
8.10.2 InfluenceofOver-CapacityontheMTFVersus
MTSDecision ............................................... 139
8.10.3 ConvertingUtilityImprovementsintoWIPReductions .... 141
8.11 InvestigationofthePush/PullIntegration .......................... 142
8.11.1 GraphicalRepresentationofExperimentalSetup
inAnylogic .................................................. 142
x Contents
8.11.2 ExperimentsandResultstoDetermineInfluencing
FactorsforUpstreamControl ............................... 147
8.11.3 ExperimentsandResultstoDetermineClosed-Form
SolutionsforFCT*andS%* ................................ 148
8.11.4 ExtensiontoArbitraryForecastErrorDistributions ........ 157
8.11.5 ExperimentalSetupfortheExtensiontothe
Multi-ProductCase .......................................... 161
8.11.6 AnalysisofDriversfortheImpactMagnitude ............. 161
8.12 AnylogicModelofCaseStudy ..................................... 165
8.13 UsedSoftwareProducts ............................................. 169
8.14 AbouttheAuthor .................................................... 170
References .................................................................. 171