A decision framework for maximising lean manufacturing performance

Informa Ltd Registered in England and Wales Registered Number: 10729 Registered office: Mortimer House,37-41 Mortimer Street, London W1T 3JH, UK

International Journal of Production Research

Publication details, including instructions for authors and subscription information:http://www.tandfonline.com/loi/tprs20A decision framework for maximising leanmanufacturing performance

Varun Ramesh & Rambabu Kodali

a

a

a

Mechanical Engineering Group, Birla Institute of Technology and Science, Pilani-333031,Rajasthan, India

Version of record first published: 26 Aug 2011.

To cite this article: Varun Ramesh & Rambabu Kodali (2012): A decision framework for maximising lean manufacturingperformance, International Journal of Production Research, 50:8, 2234-2251To link to this article: http://dx.doi.org/10.1080/002073.2011.5665PLEASE SCROLL DOWN FOR ARTICLE

Full terms and conditions of use: http://www.tandfonline.com/page/terms-and-conditionsThis article may be used for research, teaching, and private study purposes. Any substantial or systematicreproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form toanyone is expressly forbidden.The publisher does not give any warranty express or implied or make any representation that the contentswill be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug doses shouldbe independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims,proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or indirectly inconnection with or arising out of the use of this material.InternationalJournalofProductionResearchVol.50,No.8,15April2012,2234–2251

Adecisionframeworkformaximisingleanmanufacturingperformance

VarunRameshandRambabuKodali*

MechanicalEngineeringGroup,BirlaInstituteofTechnologyandScience,Pilani-333031,Rajasthan,India

(Received26May2010;finalversionreceived14February2011)

Sincethedevelopmentoftheoriginalvaluestreammapping(VSM)byTaichiOhnoatToyota,anumberofauthorshavesuggestedseveraladditionalVSMtoolstounderstandandimprovethevaluestreamthroughwastereduction.AsinglebestVSMtool,thougheffectiveindealingwithacertainwastetype,becomesredundantasotherwastestakecentrestageand/ororganisationalprioritieschange.Toovercomethis,adecisionframeworkbasedonanovelformulationoftheintegratedanalyticalhierarchyprocess(AHP)–pre-emptivegoalprogramming(PGP)hasbeenproposed.ThisframeworknotonlyguaranteesaccurateselectionofanidealVSMtool,basedonthecurrentorganisation’spriorities,butalsoaidsthedecisionmakertoarriveattheoptimumimplementationsequenceofachosensetofVSMtoolstoidentifyandreduceallwastespresentinthesystem,therebymaximisingorganisationalperformanceintheshortesttimeframe.

Keywords:leanmanufacturing;waste;VSM;decisionframework;leancompetitivepriorities;leanperfor-mancemetrics;AHP;PGP

Downloaded by [Columbia University] at 18:35 05 January 2013 1.Introduction

Theterm‘leanmanufacturing’wasfirstusedinThemachinethatchangedtheworldbyJamesWomackandDanielJones(1991)todescribethemanufacturingphilosophypioneeredbyToyota.ThephilosophyhasbeenpractisedatToyotaunderthenameofToyotaProductionSystem(TPS),whichhasitsoriginsinKichiroToyoda’s(thefounderofToyota)workwaybackin1934buthasonlyrecently(since1990)receivedglobalattention.Inanutshell,leanmanufacturingistheendlesspursuitofeliminatingwaste(Shingo19).Wasteisanythingthataddscost,butnotvalue,toaproduct(Ohno1988).Toyotacategorisesthedifferenttypesofwasteobservedarounditsplantintosevencategories,alsoknownasthe‘sevendeadlywastes’(Shingo1988).Severalauthorshavepresentedvariousalternativetypologiesfordifferenttypesofwastepresentinasystem;Dennis(2007)hasincludedthewasteofknowledgedisconnectioninadditiontothesevenwastes.LikerandMeier(2006)haveintroducedthewasteofunusedcreativity.TheKaizenInstitute(2010)proposedreprioritisationwasteasanadditionalcategory.Hinesetal.(1998)haveintroducedpowerandandenergy,humanpotential,environmentalpollution,unnecessaryoverheadsandinappropriatedesign.Inthepresentresearch,onlythesevenwastesasoriginallyproposedbyShingo(1988)havebeenconsidered.Thesearethewastescreatedby:overproduction;inventory;waiting;overprocessing;transportation;excessivehumanmotion;anddefects.

1.1Valuestreammappingasatoolagainstwaste

TheconceptofusingavisualtooltorepresenttheflowofmaterialandinformationasameanstoidentifyandeliminatewastewasoriginallyintroducedbyTaichiOhno.Originally,thismethodologywaspassedoninToyotathroughthelearningbydoingprocess–mentorstrainedmenteesbyassigningthemtoprojects–andremainedunknowntotheoutsideworldatlarge.MikeRotherandJohnShookchangedthat,intheirLearningtosee(1999).AccordingtoRotherandShook,avaluestreamisdefinedasalltheactions(bothvalueaddedandnon-valueadded)currentlyrequiredtobringaproductthroughthemainflowsessentialtoeveryproduct:theproductionflowfromrawmaterialintothearmsofthecustomerandthedesignflowfromconcepttolaunch.

AnalternativeviewtoVSMhasalsobeenfoundintheliterature.AsproposedbyHinesetal.(1998),valuestreammanagementisamorestrategicandholisticapproachtowasteidentificationandremoval.Byborrowingtechniquesfromdiversefieldssuchasengineering,logisticsandoperationsresearch,HinesandRich(1997)have

*Correspondingauthor.Email:proframbabukodali@gmail.com

ISSN0020–73print/ISSN1366–588Xonlineß2012Taylor&Francis

http://dx.doi.org/10.1080/002073.2011.5665http://www.tandfonline.com

InternationalJournalofProductionResearch2235

compiledasetofsevendetailedvaluestreammappingtools(Table1)toidentifymicro-levelwasteandsubsequentlyreduceit.However,applicationofinappropriatemappingtoolsmayresultintheadditionalwastageofresourcessuchastimeandmoney,andthereductionofemployees’confidenceintheleanphilosophy.Hence,HinesandRichhavealsoproposedadecisionheuristicforselectionofvaluestreammappingtechniquesusingtheValueStreamAnalysisTool(VALSAT)approach.Aspartoftheheuristic,HinesandRichhavedevelopedacorrelationmatrixbetweenvariouswastetypesandthesevenvaluestreammappingtools.Thecurrentpaperfurtherdevelopsthematrixtoencompassleanvaluestreammapping(currentstatemap/futurestatemap),asshowninTable2.Thispresentstheorganisationwiththeopportunitytocomparetheperformanceofvaluestreammanagementtoolswith

Table1.Thesevenvaluestreammappingtools.Mappingtool

1.Processactivitymapping

Description

Anindustrialengineeringtoolforthestudyofflowprocess,identificationofwaste,considerationofprocessrearrangement,considerationofflowrearrangement,analysisoftheoccurrenceofactivity.

Portraysinasimplediagramthecriticallead-timeconstraintsforaparticular

process.Itmayplotasimplediagramindicatingcumulativeinventoryleadtimeversusprocessleadtimeatvariousstages.Ithelpsintargetingeachofthe

individualinventoryamountsandleadtimeatdifferentstagesforimprovement.Plotsthenumberofproductvariantsateachstageofthemanufacturingprocess.Helpsindecidingwheretotargetinventoryreductionandmakingchangestotheprocessingofproducts.Providesoverviewofthecompany.

Thistoolisdesignedtoidentifyqualityproblemsintheorderfulfilmentprocessorthewidersupplychain.Themapshowstheoccurrenceofthethreetypesofdefects(productdefects,scrapdefectsandservicedefects)inthevaluestream.

Thisgraphshowsthebatchsizeoftheproductatvariousstagesoftheproductionprocess(withinacompanyorsupplychain).Itcanalsoshowtheinventoryholdingatvariousstagesintime.

Decisionpointanalysisisofparticularusefor‘T’plantsorforsupplychainsthatexhibitsimilarfeatures,althoughitmaybeusedinotherindustries.Itindicatesthepointatwhichproductsstopbeingmadeaccordingtoactualdemandandinsteadaremadeagainstforecastalone.

Usefulinunderstandingwhataparticularsupplychainlookslikeatanovervieworindustrylevelandhowitoperates;inparticular,indirectingattentiontoareasthatmaynotbereceivingsufficientdevelopmentattention.

Downloaded by [Columbia University] at 18:35 05 January 2013 2.Supplychainresponsematrix

3.Productionvarietyfunnel4.Qualityfiltermapping

5.Demandamplificationmapping6.Decisionpointanalysis

7.Physicalstructuremapping

Source:Hinesetal.(1997),adaptedfromSinghetal.(2006).

Table2.7þ1valuestreammappingtools.

Mappingtools

ProcessactivitymappingLHHHMHL

SupplychainresponsematrixMHHL

Productionvarietyfunnel

LMM

L

H

HQualityfiltermapping

L

Demandamplificationmapping

MM

DecisionpointanalysisMM

L

LM

LPhysicalstructuremapping

LeanvaluestreammappingHMLMH

WastesOverproductionWaitingTransport

Over-processingInventoryMotionDefects

Notes:H¼Highcorrelationandusefulness;M¼Mediumcorrelationandusefulness;L¼Lowcorrelationandusefulness.Tools1–7adaptedfromHinesandRich(1997).

2236V.RameshandR.Kodali

Downloaded by [Columbia University] at 18:35 05 January 2013 leanmaterialandinformationflowmappingand,basedontheirpriorities,tochoosethebesttoolforwasteidentificationandremoval.

TheVALSATprocess,thoughsimpleinnature,ispronetojudgementalinconsistencycreatedbythebiasedviewofthedecisionmakerswhileallocatingemphasistodifferentalternatives(Singhetal.2006).Toovercometheseshortfalls,Singhetal.haveproposedafuzzy-based,multi-preference,multi-personandmulti-criteriadecision-supportheuristicfortheselectionofvaluestreammappingtools.

Thoughtheframeworkprovidesalogicalandrationalmethodologyforselectionofdetailedvaluestreammappingtools,itfailstoaddresslong-termwastereduction.Accordingtoleanprinciples,ifamanufacturingplantwantstodeliverthehighestquality,atthelowestcost,intheshortesttimetoitscustomersandalsocontinuouslyimproveonthem,thenitmustcontinuouslyidentifyandremoveallthewastepresentinthesystem.AlthoughasingleVSMtoolmaybeeffectiveindealingwithacertaintypewastethatcurrentlyisofthehighestprioritytotheorganization,itbecomesincreasinglyredundantasotherwastestakecentrestageand/ororganisationalprioritieschange.Thereisnoone-timesolutionforhighestqualityatthelowestcostintheshortesttime.Thisisthefundamentalproblemfacedbythepreviouslydescribeddecisionprocess.Hence,thereexistsaneedtodevelopaframeworkthatnotonlyguaranteesselectionofthemostsuitableVSMtoolbasedonthecurrentorganisation’sprioritiesbutalsoproposesthebestsequenceofapplicationofVSMtoolstoremoveotherwastespresentinthesystemandmaximisetheperformancemeasuresoftheorganisation.

Thisistheobjectiveofthecurrentpaper:todevelopadecisionframeworkfortheselectionofthebestsequenceofVSMtoolapplicationtomaximisetheperformanceofaleanmanufacturerbyremovalofallwastetypesintheshortesttimeandwiththeminimumexpenditureofresources.Todoso,thepaperdescribesanovelapproachtointegratedAHP-PGPmodellingusinganiterativealgorithmtosolvetheprioritisedgoaloptimisation.

2.Anoverviewofthecaseorganisation

Theorganisationconsideredinthiscasestudyisamedium-sized,originalequipmentmanufacturer(OEM)formanyautomobilemanufacturerslocatedinthenorthernpartofIndia.Itmanufacturesdifferenttypesofautomobilecomponent(baseplates,separatorassemblies,notchbacks,etc.).Thesepartsarepredominantlyusedbymajorautomobilemanufacturers(forbothtwo-wheelandfour-wheeldrivevehicles)acrossthecountry.Table3presentsasummaryofthecaseorganisation.

Theorganisationiscurrentlyfacingalotofproblemsintermsofnotbeingabletomeetitscompetitivepriorities.Afewofthecriticalproblemsarehighvarietyandlowvolume,quality,deliveryandcostofproduction.Hence,theorganisationchosetostreamlineitsprocessestoachieveitsobjectivesofimprovingproductivity,quality,deliveryandminimumcost.Theorganisationhasastrongfocusonthesafetyandmoraleofitsworkforceandwouldliketoachieveitsbusinessobjectiveswithoutcomprisingitssafetystandards.

Topmanagementwasopentoimplementingmanagementphilosophiessuchasleanmanufacturing.Thisisbecause,asanOEMtosomeofthebiggestautomobilemanufacturersinthecountry,theyhadobtainedtheISO9000certificationofquality,whichhadproducedgoodresultsinthepastintermsofstandardisingvariousprocessesaswellasreducingdefects.Hence,theywerecontemplatingimplementingsuchmanufacturingmanagementpracticesandphilosophies.Thefirststeptostreamliningitsprocesswastoidentifyappropriatemappingtoolstomapthecurrentorganisationalprocessesandidentifywaste.TheissueherewashowtochoosethebestVSMtoolstomapandreduceallthevariousformsofwasteintheorganisationwiththeleastexpenditureoftimeandenergy.

Table3.Summaryofcaseorganisation.IndustrycharacteristicsIndustrytypeIndustrysectorProduct

Producttype

ProductionvolumeandvarietyCompanyvisionMission

Detailsaboutthecaseorganisation

DiscretetypemanufacturingManufacturing

Differenttypesofautomobilepartsfortwo-wheelandfour-wheeldrivevehiclesCriticalcomponents

Highvarietyandlowvolume

Tobeastarperformerandmarketleader

Continuousimprovementofproducts,processesandpeople

InternationalJournalofProductionResearch2237

Thisdilemmawasresolvedbytheapplicationofthedevelopeddecisionframeforleantoolselectionsoastomaximisetheperformanceofleanmanufacturing.

3.Developmentofthedecisionframework

ThedecisionframeworkselectsthebestsequenceofVSMtoolapplicationtomaximisetheperformanceofaleanmanufacturerbyremovalofallwastetypesintheshortesttimeandwiththeminimumexpenditureofmoneyandenergy.

Thestepsinvolvedinthedecisionframeworkprocessareasfollows:(1)Identificationofperformancemeasures(PM)forleanmanufacturingthatneedtobemaximised.(2)IdentificationofVSMtoolsfortheleanmanufacturerthatwillaidintheimprovementprocess.

(3)SelectionofVSMtoolforcurrentpriority:usingAHPtoselectthebestVSMtooltomaximisethecurrent

prioritywithminimumexpenditureoftimeandeffort.

(4)IdentificationofperformancemetricsforeachPMandprioritisationofthemwithrespecttothe

correspondingPM.

(5)SelectionofVSMtoolsforfuturepriorities:usingPGPtoidentifythebestsequenceofVSMtoolapplication

tomaximiseallPMsbyidentificationandsubsequentreductionofallwastetypeswithminimumexpenditureoftimeandmoney.PGPalsooutputstheoptimumvalueofperformancemetricsthatmaximisethecorrespondingPMunderthegivenconstraint.

(6)Comparisonoftheoptimumvalueofthemetricswithrealtimeperformancemetricstohelpdecidewhena

certainVSMtoolhasattainedthelimitofitsabilitytoeffectimprovementandthatitistimetoapplythenextVSMtoolinthesequenceasdefinedbyPGP.Figure1showstheflowchartforthedecisionframework.

Downloaded by [Columbia University] at 18:35 05 January 2013 Figure1.Flowchartfordecisionframework.

2238V.RameshandR.Kodali

3.1Performancemeasurement

Theobjectiveofleanmanufacturingistomaximiseperformancebyeliminatingwaste.Hence,performancemeasuresandwasteserveastheattributesandsub-attributesoftheAHPmodel,describedinalatersection,whichaidthedecisionmakerinconvertinginformation(knowledge,judgements,values,opinions,needs,wants,etc.)tonumericalvaluesbyestablishingpriorities.Thesewillalsoserveaselements(objectivefunctionandconstraints)forthePGPmodelandhencedefiningacomprehensivesetofperformancemeasuresthatprovideanall-roundviewoftheorganisation’sprioritiesandpreferencesisthemostcriticalsectionofthedecisionframework.

Inleanphilosophy,measuresgiveasenseofdirectionaswemovefromthecurrentstatetothefuturestatebykeepingouractionsalignedwiththebusiness’slong-termgoals(Rother2010).Dennis(2007)hassuggestedsixprimarycustomerfocusorperformancemeasures(PM)foraleanmanufacturer:productivity,quality,cost,delivery,safetyandandenvironmentandmorale.TherealsoexistsaneedtodefineanewsetofleanperformancemetricssoastomeasuretheintensityofthePMalongdifferentdimensions.Traditionalmetrics(relatedtomassproduction)cannotbeusedinaleanenvironmentbecausethesewillcausepeopletoresistchangingthehabitsforwhichtheywerepreviouslyrewardedandhenceperformancewilldepreciateaccordingly(TotalSystemsDevelopmentInc.2001).Themostcommonmetricinmassmanufacturingisthedesiretoproduceasmuchaspossibletoimprovetheefficiencyorutilisationofpersonnelandequipment.Thisleadstothewastecreatedbyoverproduction,ifwhatisproducedisnotsynchronisedwithwhatisrequired(customerdemand).Idealefficiency(lean)istomakeexactlywhatisneeded,whenitisneeded,inthequantitiesrequiredandatthelowestcost.

Downloaded by [Columbia University] at 18:35 05 January 2013 3.2Leanperformancemetrics

Aperformancemetricisaverifiablevariablethatisexpressedineitherquantitativeorqualitativeterms.Neelyetal.(2005)definedperformancemetricsasavariableusedtoquantifytheefficiencyandeffectivenessofanaction.DaumandBretscher(2004)extendedthedefinitionofperformancemetrictoincludeaqualitativeaspectbecausedifferentstakeholdersputdifferentvaluesonthesameoutcome,whichcannotbequantified.Also,intangiblemeasurestoalargeextentcannotbequantified,andthusrequireaqualitativemetric(Lev2001).Aperformancemetricshouldbebasedonanagreed-uponsetofdataandawell-understoodandwell-documentedprocessforconvertingthatdataintothemetric.Giventhedataandprocess,independentsourcesshouldbeabletoarriveatthesamemetricvalue(Melnyketal.2004).Tointerpretmeaningfromametric,however,itmustbecomparedtoatarget(Mahindhar2005).Basedontheseparameters,29keyperformancemetricsforleanmanufacturingwereidentified(TotalSystemsDevelopmentInc.2001;AnandandKodali2008)throughathoroughliteraturesurvey(Table4).TheperformancemetricsforthesixPMofleanmanufacturingaredescribedbelow:Leanmetricsforproductivity:

(1)WaitKanbanTime(WKT):Inaleanmanufacturingcompany(usingKanbanorpullmethods),production

mustbestoppedtoavoidthewasteofoverproduction.ThelineisinWaitKanbanTimemodewhenitawaitstheordertoresumemakingtheproductsrequiredbythecustomer.ConsistentlyhighamountofWaitKanbanTimeindicatesanimbalancebetweenprocesses,causedbygreatercapacitythanrequiredtomeetthecustomerdemand.

(2)Partsperlabourhour(PLH):Thisconsidersthevariabilityoftheworkforce,aswellastheoverallefficiency

oftheproductionline.Thiscalculationisthetotalnumberofgood(non-defective)partsproduced,dividedbythetotallabourhoursworked,minusanyschedulednon-productiontime(includingWaitKanbanTime)(3)Totalpartsproduced(TPP):Thisisnecessarytounderstandtheproductionprocessbecauseall

measurements,suchasyieldorscraprate,arecomparedtothetotalpartsproducedandexpressedasapercentageofthistotal.

(4)Linestoptime(LST):Thisistheamountoftimethelineisstoppedforanyreasonotherthanequipment

downtimeorWaitKanbanTime.Causesincludepartshortage,qualityissues,etc.ItisnormallyreportedasapercentageofstandardoperatingtimeminusWaitKanbanTime.

(5)Equipmentdowntime(EDT):Itisthepercentageoftimethemachineisunabletoproduceproductduring

scheduledoperationtime.

(6)Shortingcustomerprocess(SCP)and:Thismeasureindicatesaprocess’sabilitytoproducewhatisneeded,

whenitisneededandaccordingtotheprocessTakttime.Itshouldbeusedonlyifthereisanongoingproblem,orifitisthebestmeasureofprocesscapability.Itcanbecalculatedbydividingtheamountofstoptimebystandardoperationtime,minustheWaitKanbanTime.

InternationalJournalofProductionResearch

Table4.ListofkeyPMmetricsforleanmanufacturing.

Performancemetric

123456710

WaitKanbanTimePartsperlabourhourTotalpartsproducedLinestoptime

EquipmentdowntimeShortingcustomerprocessStoppingsupplierprocessChangeovertimeCustomersatisfactionDefectsrepairedinprocessYieldScrap%ScrapcostTotalinventoryLabourcontentRawmaterialvarianceMisseddeliverycyclesQuickresponsetocustomerDeliveryreliability

Numberofwork-relatedinjuriesLostworkdays

NumberofmedicalvisitsWork-relatedrestrictionsEmploymentsecurity

EmployeetrainingandanddevelopmentNumberofawardsandandrewardsdisbursedEmployeeinvolvementCultureGovernance

p

ppp

pppppppppppppp

pp

Quantitative

ppp

ppppp

pQualitative

Reference

2239

11121314151617181920212223242526272829

TotalSystemsDevelopmentInc.(2001)TotalSystemsDevelopmentInc.(2001)TotalSystemsDevelopmentInc.(2001);Anandetal.(2008)

TotalSystemsDevelopmentInc.(2001)TotalSystemsDevelopmentInc.(2001);Anandetal.(2008)

TotalSystemsDevelopmentInc.(2001)TotalSystemsDevelopmentInc.(2001)TotalSystemsDevelopmentInc.(2001);Dennis(2007);AnandandKodali(2008);Shingo(19)

TotalSystemsDevelopmentInc.(2001);Anandetal.(2008)

TotalSystemsDevelopmentInc.(2001);AnandandKodali(2008)

TotalSystemsDevelopmentInc.(2001);AnandandKodali(2008)

TotalSystemsDevelopmentInc.(2001)TotalSystemsDevelopmentInc.(2001);AnandandKodali(2008)

TotalSystemsDevelopmentInc.(2001);AnandandKodali(2008)

TotalSystemsDevelopmentInc.(2001);AnandandKodali(2008)

TotalSystemsDevelopmentInc.(2001)TotalSystemsDevelopmentInc.(2001);AnandandKodali(2008)

TotalSystemsDevelopmentInc.(2001)TotalSystemsDevelopmentInc.(2001);AnandandKodali(2008)

TotalSystemsDevelopmentInc.(2001)TotalSystemsDevelopmentInc.(2001)TotalSystemsDevelopmentInc.(2001)TotalSystemsDevelopmentInc.(2001);Dennis(2007)

TotalSystemsDevelopmentInc.(2001);Dennis(2007);AnandandKodali(2008)TotalSystemsDevelopmentInc.(2001);Dennis(2007);AnandandKodali(2008)TotalSystemsDevelopmentInc.(2001);Dennis(2007);AnandandKodali(2008)TotalSystemsDevelopmentInc.(2001);Dennis(2007)Dennis(2007)

Dennis(2007);ThompsonandWallace(1996)

Downloaded by [Columbia University] at 18:35 05 January 2013 (7)Stoppingsupplierprocess(SSP):Thisisthesameastheshortingcustomerprocess(SCP).

(8)Changeovertime(C/T):Alengthychangeovertimecontributestoalackofefficiency.Timelostisgenerally

compensatedforbyrunninglargerbatchsizes.Itisthetimerecordedinminutesfromthelastgoodofoneparttypetothefirstgoodofanotherparttype.Leanmetricsforquality:

(9)Customersatisfaction(CUS):Thisisdefinedasthenumberofdefectivepartsreturned

fromthefollowingprocessorcustomer.Itiscalculatedasapercentageofthetotalpartsproducedbyaprocess.

2240V.RameshandR.Kodali

(10)Defectsrepairedinprocess(DEF):Productsarecategorisedaseitheracceptablepartsasproduced(firstrun

orfirsttimethrough),partsrequiringreworkorscrapparts.Thesecondcategoryofpartsisrecordedinthismetricandrepresentedasapercentageoftotalpartsproduced.

(11)Yield(YLD):Thisreferstothepercentageoftotalpartsproducedthatareacceptedasis,i.e.withoutany

rework.

(12)Scrap%(SCR):Thesepartsrequireexcessivereworkandhencearescrappedandrecycledintoraw

materials(ifpossible)thanrectifiedbackintotheflow.Thismetricisrelatedtodefectsrepairedinprocess(DEF)andYield(YLD)bythefollowingequation:

SCR¼1ÀYLDÀDEF

Leanmetricsforcost:

(13)Scrapcost(SCC):Totalcostassociatedwithscrappedparts/discardedmaterials.

(14)Totalinventory(TIN):Thetotalcostassociatedwithanystandinginventory(finishedgoods,WIP,raw

materials).Traditionally,inventoryhasbeenviewedasanassetbutleanphilosophyfocusesoncompletereductionofalltypesofinventory.

(15)Labourcontent(LCN):Foraleanmanufacturer,labourisafixedcost.Thisisderivedfromthepractice

withinToyotaoflifetimeemployment.LabourcontentisthenumberofworkersrequiredtosuccessfullyoperatealineafterTakttimeandstandardisedworkconsiderations.

(16)Rawmaterialvariance(RMV):Standardsaredevelopedfortheamountofrawmaterialperpart.Totalraw

materialconsumptionshouldbecomparedtothestandardquantity-per-partmultipliedbythetotalpartsproduced,whichwillprovideavariancetothestandard.Thismultipliedbytheaveragecostofrawmaterialsgivesthevarianceincostterms.Highvariancemaybecausedbyover-processingwaste,highscraprateormaterialwastage(spillage,excesstrimscrap).Leanmetricsfordelivery:

(17)Misseddeliverycycles(MDC):Thismetricmeasurestheefficiencyofmaterialhandlersworkinginapull-basedproductionoperation.Failuretocompleteadeliverycycleresultsinfluctuationsinthematerialprocess,andthecustomerprocessdoesnothavesufficientpartsattheoperationtocontinueproduction.Excessmisseddeliverycyclesalsoincreasesthenumberofproductionkanbansdepositedinthesupplyoperation,whichmayoverwhelmit.

(18)Quickresponsetocustomer(QRC):Thismetricmeasurestheabilityoftheprocesstomeetfluctuationsin

customerdemand.Thenumberofordersoverandabovetheaverageordersizeisrecordedandthenumberofsuchordersmetismeasuredasapercentageofthelatter.

(19)Deliveryreliability(DRL):Deliveryreliabilityisamorecomprehensivemetricascomparedtopastdue

ordersusedintraditionalmanufacturingsetups.Itincludesthepercentageofordersdeliveredontimethatarecorrectandcomplete(notpartialshipment).Thisnumberprovidesthemostaccuratereflectionofcustomersatisfaction.Leanmetricsforsafety:

(20)Numberofwork-relatedinjuries(INJ):Thesetypicallyfallintotwobroadcategories–accidentsorsudden

injuries,includingcuts,contusions,etc.,andcumulativetraumadisordersorrepetitivemotioninjuries,includingcarpeltunnelsyndromeandthoracicoutletsyndrome.

(21)Lostworkdays(LWD):Operationaldowntimeresultingfromwork-relatedinjuriesofpersonneland/or

healthandandsafetyhazards.

(22)Numberofmedicalvisits(MDV):Numberofvisitstoamedicalfacilityduetoinjuriesand/orpossibleorreal

healthhazards.

(23)Work-relatedrestrictions(WRR):Numberofwork-relatedrestrictionsresultingfromthegeneral

operationalenvironmentintheplant.Leanmetricsformorale:

(24)Employmentsecurity(EMS):Jobsecurityisoneofthecorevaluesofaleanmanufacturer.Lean

manufacturingdoesnotmeaneliminatingjobs.Itmeansreducingthelabourcontentofoperationssothat

ð1Þ

Downloaded by [Columbia University] at 18:35 05 January 2013 InternationalJournalofProductionResearch2241

(25)(26)(27)

(28)

(29)

Downloaded by [Columbia University] at 18:35 05 January 2013 workersarefreetobeinvolvedinimprovementactivitiesaroundtheplant.Thenumberofemploymentcontractsterminatedinafixedperiodisagoodmeasureofthesenseofjobsecurityintheorganisation.

Employeetrainingandanddevelopment(ETD):Numberofemployeetraininganddevelopmentalprogrammesundertakeninanorganisationinafixedperiodoftime.

Numberofawardsandandrewardsdisbursed(A/R):Thenumberofawards(bestemployeeofthemonth,year,etc.),bonusesforperformanceandsoon.

Employeeinvolvement(EMI):Thisisacombinedmeasureofallthegroupactivitiesanaverageemployeemaybeinvolvedin,e.g.number.ofKaizencircles,averagenumberofsuggestionsperemployee,etc.

Culture(CUL):ThecultureofleanproductioncomprisesPDCA,standardisation,visualmanagement,teamwork,intensity,paradoxandleanproductionasadopath.Thelevelofawarenessandintensityofinvolvementoftheemployeesintheseactivitiesdefinesthequalityoftheculture.Sincethisisaqualitativemeasure,aLikertscale-basedquestionnaire(Trochim2006)willbethemostappropriatetooltoquantifythequalityofcultureinanorganisation.

Governance(GOV):Corporategovernanceisdefinedasthesetofprocesses,customs,policies,lawsandinstitutionsaffectingthewayacorporation(orcompany)isdirected,administeredorcontrolled(Wikipedia2010).Inaleanmanufacturer,governancehasacriticalimpactonhowateamisorganised,functionsandbehaves(ThompsonandWallace1996),andsinceateamformstheheartofleanimprovementoperations(TotalSystemsDevelopmentInc.2001;Dennis2007),thequalityofgovernanceisacriticalmeasureofteammorale.ThepreviouslymentionedLikertscale-basedquestionnairemaybeappliedhereaswelltoadjudgethequalityofgovernanceinaleanmanufacturer.

Forthedecisionframework,eachofthe29performancemetricsiscalculatedinrealtimeand,aftercomparingitwithanachievabletargetperformance(Melnyketal.2004),isscaledtoavaluebetween0and1.Thisisdonesoastostandardisethedimensionsforeachperformancemetric.

3.3DevelopmentofAHP

TheAHPhasbeenwellreceivedintheliterature(Roger1987).Applicationsofthismethodologyhavebeenreportedinnumerousfields(Chandra1998).ThegeneralapproachoftheAHPmodelistodecomposetheproblemandtomakepair-wisecomparisonsofalltheelementsonagivenlevelwithreferencetorelatedelementsintheleveljustabove.Ahighlyuser-friendlycomputermodelhasbeendeveloped,whichassiststheuserinevaluatingtheirchoices.TheschematicofthemodelisshowninFigure2.

Figure2.SchematicoftheAHPmodel.

2242V.RameshandR.Kodali

Descriptionofthemodel

Athoroughanalysisoftheproblemisrequired,alongwiththeidentificationoftheimportantattributes/criteriainvolved.Theattributes/criteria(level2)usedinAHPtoachievethegoalarethesixperformancemeasuresofaleanmanufacturer.Theseare:

......

Productivity(PRD)Quality(QUA)Cost(COS)Delivery(DLV)Safety(SFT)Morale(MRL)

Thesevenwastes(refertosection1)arethesub-attributes/criteriaforeachofthePMofleanmanufacturing,i.e.theattributesatlevel2usedintheAHPmodel.Theseare:

.......

Overproduction(OPD)Waiting(WTG)Transport(TRN)

Over-processing(OPR)Inventory(INV)Motion(MOT)Defects(DEF)

Downloaded by [Columbia University] at 18:35 05 January 2013 TheVSMtoolsarethealternativesamongstwhichtheAHPaidsthedecisionmakertochoosethebesttoolfortheirindustrybasedonthecurrentpriority.TheVSMtoolsconsideredinthepresentmodel(seeTable1foradetaileddescription)are:

........

Processactivitymapping(PAM)Supplychainresponsematrix(SCR)Productionvarietyfilter(PVF)Qualityfiltermapping(QFM)

Demandamplificationmapping(DAM)Decisionpointanalysis(DPP)

Physicalstructuremapping:(a)Volumeversus(b)Value(PSM)Leanvaluestreammapping(LVS)

Analyticalhierarchyprocessmethodology

Theanalyticalhierarchyprocess(Satty1982)isapracticalapproachtosolvingrelativelydifficultproblems.TheAHPenablesthedecisionmakertorepresentthesimultaneousinteractionofmanyfactorsincomplex,unstructuredsituations.Judgementswerecollectedthroughacompleteplant-levelsurveyandmultipleroundsofpersonalinterviewswiththetopmanagementofthecaseorganisation.Thesewereprimarilyfocusedonelicitingthepreferencesthattheybelievewereimportantforsustainingthecompany’spositionasamarketleaderintheautomotivepartsindustry.ThesewereincludedinAHPforeachcriterionandsubcriterionforalllevelsofhierarchy.Pair-wisecomparisonsofcriterionateachlevelwasdoneona1–9pointscale:1reflectingequalweightand9reflectingabsoluteimportanceofoneoveranother.

BelowarethestepstofollowinusingtheAHP(Roger1987):(1)Definetheproblemanddeterminetheobjective.

(2)Structurethehierarchyfromthetopthroughtheintermediatelevelstothelowestlevel(Figure2).

(3)Constructasetofpair-wisecomparisonmatricesforeachofthelowerlevels,takingintoconsiderationthe

previouslevelasagoaltowhichthesesub-criteriabelong.LevelIIfactorsaredecomposedintosub-criteria.Ifelement‘a’dominatesoverelement‘b’,thenthewholenumberintegerisenteredinrowA,columnB,andthereciprocalisenteredinrowB,columnA.Iftheelementsbeingcomparedareequal,1isassignedtobothpositions.

À1Þ

judgementstodevelopthesetofmatricesinStep3.(4)Utilisethenðn2(5)Determinetheconsistencyofthepair-wisecomparisonsusingeigenvalues.Todoso,normalisethecolumn

ofnumbersbydividingeachentrybythesumofallentries.Thensumeachrowofthenormalisedvaluesand

InternationalJournalofProductionResearch

Table5.Normalisedcomparisonmatrix.

hPRDi

hPRDihQUAihCOSihDLVihSFTihMRLi

0.4270.1420.0850.0610.1420.142

hQUAi0.50.2150.0240.0310.0430.043

hCOSi0.2080.3750.0420.1250.1250.125

hDLVi0.3000.3000.0140.0430.1290.214

hSFTi0.2810.4690.0310.0310.0940.094

hMRLi0.2850.4750.0320.0190.0950.095

hSUMi2.1461.9760.2280.3100.6280.713

2243

Principalvector

0.3580.3290.0380.0520.1050.119

Notes:Consistencyindex(CI)¼0.1206;Consistencyratio(CR)¼0.0972.

taketheaverage.Thisprovidestheprincipalvector.Table5tabulatesthenormalisedcomparisonmatrix.Tochecktheconsistencyofthejudgements,letthepair-wisecomparisonmatrixbedenotedbyM1andtheprincipalvectorbyM2.

ThendefineM3¼M1ÃM2;and

Downloaded by [Columbia University] at 18:35 05 January 2013 M3

;M2

󰀂max¼averageoftheelementsofM4;

󰀂maxÀN

ConsistencyIndexðCIÞ¼;

NÀ1CI

ConsistencyRatioðCRÞ¼;

RCI

correspondingtoNwhere

RCI:RandomConsistencyIndex;andM4¼

N:Numberofelements

TheRandomIndexTableisasfollows:

NRCI

10

20

30.6

41

51.1

61.24

71.41

81.45

91.51

IfCRislessthan10%,thedecisionmaker’sjudgementmaybeconsistentenoughtogiveusefulweightingestimatesforvariousdecision-makingcriteria.IfCRisgreaterthan10%,thereareinconsistenciesinthejudgements,i.e.inthepair-wisecomparisonmatrix,andtheseshouldbeimprovedsuchthattheCRisalwayslessthan10%.

(6)PerformSteps3–5foralllevelsandclustersinthehierarchy.

(7)Nextexaminetheeffectofsub-criterionoflevelIIIontherespectivecriteriaoflevelII.Theprocedureis

identicaltothepair-wisecomparisonanalysisabove.SeeTable6forattributeweights.

(8)Analysethematrixforthepair-wisecomparisonsofthealternativesforthebottom-mostsub-criteriausing

theapproachabove.Forthepair-wisecomparisonofeachofthealternativesundereachLevelIIIsub-criterion,usethecorrelationmatrixdevelopedbyHinesetal.(1998)betweenthewastesandmappingtool(seeTable2).SeeTable7fordatasummary.

(9)Calculatethedesirabilityindexforeachalternativebymultiplyingeachvaluein‘weightofsub-criteria’

columnbytherespectivevaluein‘criteriaweight’column,andthenmultiplyingbythevalueforeachrespectivealternativeandsummingtheresults(seeTable8).

3.4DevelopmentofPGP

ThedecisionframeworkdevelopedinthisstudyisbasedonanovelintegratedAHP-PGPmodel.IntegratedAHP-PGPapproacheshavebeenextensivelyusedbySchniederjansandGravin(1997),Badri(2001),Radcliffeand

2244

Table6.Weightofattributes.

Weightofsub-criteriaLevelIII0.0900.0470.0260.1790.1250.1390.3940.1410.0690.0900.0270.2150.0580.4000.1030.0420.0650.1290.2280.0290.4030.0590.1230.0650.1450.2210.0260.3620.0610.0310.3630.0310.0810.2900.1420.0440.1820.0670.0290.0950.3400.243

V.RameshandR.Kodali

Sub-criteriaOPD(PRD)WTG(PRD)TRN(PRD)OPR(PRD)INV(PRD)MOT(PRD)DEF(PRD)OPD(QUA)WTG(QUA)TRN(QUA)OPR(QUA)INV(QUA)MOT(QUA)DEF(QUA)OPD(COS)WTG(COS)TRN(COS)OPR(COS)INV(COS)MOT(COS)DEF(COS)OPD(DLV)WTG(DLV)TRN(DLV)OPR(DLV)INV(DLV)MOT(DLV)DEF(DLV)OPD(SFT)WTG(SFT)TRN(SFT)OPR(SFT)INV(SFT)MOT(SFT)DEF(SFT)OPD(MRL)WTG(MRL)TRN(MRL)OPR(MRL)INV(MRL)MOT(MRL)DEF(MRL)

CriterialevelII0.3580.3580.3580.3580.3580.3580.3580.3290.3290.3290.3290.3290.3290.3290.0380.0380.0380.0380.0380.0380.0380.0520.0520.0520.0520.0520.0520.0520.1050.1050.1050.1050.1050.1050.1050.1190.1190.1190.1190.1190.1190.119

PAM0.0820.2710.4050.3920.0740.4120.1620.0820.2710.4050.3920.0740.4120.1620.0820.2710.4050.3920.0740.4120.1620.0820.2710.4050.3920.0740.4120.1620.0820.2710.4050.3920.0740.4120.1620.0820.2710.4050.3920.0740.4120.162

SCR0.1290.2710.0580.0420.1830.1420.0490.1290.2710.0580.0420.1830.1420.0490.1290.2710.0580.0420.1830.1420.0490.1290.2710.0580.0420.1830.1420.0490.1290.2710.0580.0420.1830.1420.0490.1290.2710.0580.0420.1830.1420.049

PVF0.0210.0670.0370.2210.0800.0510.0490.0210.0670.0370.2210.0800.0510.0490.0210.0670.0370.2210.0800.0510.0490.0210.0670.0370.2210.0800.0510.0490.0210.0670.0370.2210.0800.0510.0490.0210.0670.0370.2210.0800.0510.049

QFM0.00.0280.0330.1090.0220.0490.4270.00.0280.0330.1090.0220.0490.4270.00.0280.0330.1090.0220.0490.4270.00.0280.0330.1090.0220.0490.4270.00.0280.0330.1090.0220.0490.4270.00.0280.0330.1090.0220.0490.427

DAM0.1270.1060.0330.0420.1830.0490.0500.1270.1060.0330.0420.1830.0490.0500.1270.1060.0330.0420.1830.0490.0500.1270.1060.0330.0420.1830.0490.0500.1270.1060.0330.0420.1830.0490.0500.1270.1060.0330.0420.1830.0490.050

DPA0.1270.1060.0710.1090.0860.0490.0500.1270.1060.0710.1090.0860.0490.0500.1270.1060.0710.1090.0860.0490.0500.1270.1060.0710.1090.0860.0490.0500.1270.1060.0710.1090.0860.0490.0500.1270.1060.0710.1090.0860.0490.050

PSM0.0220.0260.1990.0420.0400.0490.0490.0220.0260.1990.0420.0400.0490.0490.0220.0260.1990.0420.0400.0490.0490.0220.0260.1990.0420.0400.0490.0490.0220.0260.1990.0420.0400.0490.0490.0220.0260.1990.0420.0400.0490.049

LVS0.4270.1240.1630.0420.3330.1990.1650.4270.1240.1630.0420.3330.1990.1650.4270.1240.1630.0420.3330.1990.1650.4270.1240.1630.0420.3330.1990.1650.4270.1240.1630.0420.3330.1990.1650.4270.1240.1630.0420.3330.1990.165

Downloaded by [Columbia University] at 18:35 05 January 2013 Schniederjans(2003),CebiandBayraktar(2003),Percin(2006)andBhagwatandSharma(2009)toexplorethevariouspreferencesoffirms.Inthesestudies,integratingAHPwiththePGPmodelmadeitpossiblefordecisionmakerstoincorporateadjustedweightingsonselecteddecisioncriteria.Then,thePGPmodelpermittedanaddedprioritystructurereflectingtheaddedmathematicalweighting.ThePGPmethodologyforperformancemeasureimprovementisdevelopedinthefollowingsection.

Performancemetricweightsanalysis

ThefirststepinthePGPmodelbuildingprocessistocalculatetheprioritisedweightsofperformancemetrics.Forthis,thepriorityofagivenperformancemetricwithrespecttoagivenmeasureneedstobeidentified.Apair-wisecomparisonusingthecollectivejudgementsobtainedfromthecaseorganisation,similartotheAHPprocess,

InternationalJournalofProductionResearch

Table7.Datasummary.Sub-criteriaOPD(PRD)WTG(PRD)TRN(PRD)OPR(PRD)INV(PRD)MOT(PRD)DEF(PRD)OPD(QUA)WTG(QUA)TRN(QUA)OPR(QUA)INV(QUA)MOT(QUA)DEF(QUA)OPD(DEF)WTG(DEF)TRN(DEF)OPR(DEF)INV(DEF)MOT(DEF)DEF(DEF)OPD(DLV)WTG(DLV)TRN(DLV)OPR(DLV)INV(DLV)MOT(DLV)DEF(DLV)OPD(SFT)WTG(SFT)TRN(SFT)OPR(SFT)INV(SFT)MOT(SFT)DEF(SFT)OPD(MRL)WTG(MRL)TRN(MRL)OPR(MRL)INV(MRL)MOT(MRL)DEF(MRL)

hPAMi0.0030.0050.0040.0250.0030.0210.0230.0040.0060.0120.0040.0050.0080.021000.0010.0020.00100.00200.0020.0010.0030.0010.0010.0030.0010.0010.0150.0010.0010.0130.00200.0060.0030.0010.0010.0170.005

hSCRi0.0040.0050.0010.0030.0080.0070.0070.0060.0060.00200.0130.0030.0060.0010000.00200.00100.002000.00200.0010.0010.0010.00200.0020.0040.0010.0010.006000.0020.0060.001

hPVFi0.0010.00100.0140.0040.0030.0070.0010.0020.0010.0020.0060.0010.0060000.0010.00100.0010000.0020.00100.001000.0010.0010.0010.0020.00100.00100.0010.0010.0020.001

hQFMi0.002000.0070.0010.0020.060.0030.0010.0010.0010.0020.0010.0560000.001000.0070000.001000.008000.001000.0010.00600.0010000.0020.012

hDAMi0.0040.00200.0030.0080.0020.0070.0060.0020.00100.0130.0010.00700000.00200.00100.001000.00200.0010.00100.00100.0020.0010.0010.0010.002000.0020.0020.001

hDPAi0.0040.0020.0010.0070.0040.0020.0070.0060.0020.0020.0010.0060.0010.0070000.0010.00100.00100.00100.0010.00100.0010.00100.00300.0010.0010.0010.0010.0020.00100.0010.0020.001

hPSMi0.00100.0020.0030.0020.0020.0070.0010.0010.00600.0030.0010.0060000000.001000.0010000.001000.008000.0010.00100.0010.002000.0020.001

2245

hLVSi0.0140.0020.0020.0030.0150.010.0230.020.0030.00500.0240.0040.0220.0020000.00300.0030.0010.0010.00100.00400.0030.00300.00600.0030.0060.0020.0020.0030.00100.0040.0080.005

Downloaded by [Columbia University] at 18:35 05 January 2013 Table8.Decisionindexforthedesirabilityofeachalternative.VSMToolDecisionindex

PAM0.2272

SCR0.107

PVF0.0674

QFM0.1811

DAM0.0799

DPA0.0744

PSM0.0562

LVS0.2068

isadoptedhere.

(1)Inthisstep,each29performancemetricsundergoesapair-wisecomparisondependingonitsrelativeeffect

onthecorrespondingperformancemeasure.Apair-wisecomparisonmatrixsimilartotheapproachdescribedintheAHPprocessandtheprincipalvectorforeachoftheperformancemetricswasrecordedastheweight.ThisanalysiswasperformedontheperformancemetricsforeachPMofleanmanufacturing.

2246V.RameshandR.Kodali

Table9.Resultsofperformancemetricandalternativeanalysis.

Mappingtoolweightageforthemetrics

PerformanceMetricWKTPLHTPPLSTEDTSSPSCPC/CQRCDRLSCCTINLCNRMVCUSDEFYLDSCRINJLWDMDVWRREMSETDA/REMICULGOV

Weightage0.0300.1510.0910.1780.1380.00.00.3050.1430.4290.4290.6250.1250.1250.1250.5010.0770.1590.2630.2730.5330.1280.0670.2210.0690.0490.2210.2210.221

hPAMi0.0320.2750.2750.1770.0830.1420.1420.3300.2570.0390.0610.00.0440.0990.0480.1690.0710.0970.1400.4670.4670.4670.4210.0280.1250.1250.2850.1250.125

hSCRi0.2370.0620.0620.0960.0830.00.00.0500.0480.1650.3550.00.1950.0310.0480.0700.0710.0390.1010.0610.0610.0610.0550.1150.1250.1250.00.1250.125

hPVFi0.0720.0620.0620.0430.0830.00.00.0500.0480.0450.0610.1060.0620.0670.4030.0800.0710.1570.1400.0610.0610.0610.0550.0470.1250.1250.00.1250.125

hQFMi0.0320.0620.0620.2400.4170.3340.3340.0500.0480.0450.0610.4790.0270.0830.2330.3990.5000.4020.4350.0610.0610.0610.0550.3450.1250.1250.1360.1250.125

hDAMi0.1570.0620.0620.0430.0830.00.00.0500.0480.3800.1700.0720.1190.2030.0480.0700.0710.0410.0460.0610.0610.0610.0550.0960.1250.1250.00.1250.125

hDPAi0.1250.0620.0620.0430.0830.00.00.2030.2120.0740.1700.0720.0800.1090.0480.0700.0710.0410.0460.0610.0610.0610.0550.0960.1250.1250.00.1250.125

hPSMi0.0690.0620.0620.0430.0830.00.00.0520.0510.0390.0610.0720.1190.0370.1240.0700.0710.0410.0460.0610.0610.0610.0550.0960.1250.1250.00.1250.125

hLVSi0.2750.3560.3560.3180.0830.2560.2560.2170.2880.2120.0610.0720.30.3700.0480.0700.0710.1810.0460.1680.1680.1680.2510.1750.1250.1250.3090.1250.125

Downloaded by [Columbia University] at 18:35 05 January 2013 (2)Inthenextstep,apair-wisealternativeanalysisforeachofthe29performancemetricswascarriedoutto

evaluatetheimpactofeachmappingtoolinimprovingindividualperformancemetrics.TheresultsforeachcompleteperformancemetricandalternativeanalysisaregiveninTable9.Theweightscalculatedafterthepair-wisecomparisonareonascaleof0to1(andthesumofalltheweightsisequalto1).Thiscanalsobeconsideredasindicatingametric’srelativeimportanceoritsrelativepercentagecontributionintheoverallPM.Thisrelativepercentagecontributionisintegratedwiththeoptimisationmethod,sothattheoptimallevelofeachconsideredperformanceindicatorcanbeidentified.

Descriptionofthemodel

Thepurposeoftheapproachistoaidthedecisionmakerinchoosingwhenandinwhatsequenceanewmappingtoolshouldbeintroducedtoproducethemaximumimprovementwiththeleastexpenditureoftimeandenergy.Eachsubsequentmappingtoolincrementallybuildsonthebenefitsofthepreviouslyappliedmappingtool.ThePGPguaranteestomaximiseeachperformancemeasureundergivenconstraints.

ElementsofPGP

FormathematicalmodellingofthePGP,firstthedecisionvariablemustbedefined,followedbytheobjectivefunction(goal)andconstraintsintermsofthedecisionvariable.

Decisionvariable

Here,theobjectiveistofindthebestsequencefortheapplicationoftheVSMtools,suchthattheperformancemetricsareoptimisedwhilesimultaneouslymaximisingeachoftheperformancemeasuresinthelongterm.Therefore,theperformancemetricsidentifiedfortheperformancemeasuresandprioritisedthroughperformance

InternationalJournalofProductionResearch2247

metricsandalternativeanalysesewillbeconsideredasthedecisionvariables.Theobjectivefunctionsandconstraintsneedtobefurtherdefinedintermsoftheperformancemetrics,i.e.thedecisionvariables.

Inthegoalprogrammingmodel,thedecisionvariablesaredifferentperformancemetrics,whicharerepresentedbyXk(wherekvariesfrom1to29,from1to8forproductivityperformancemetrics,9to12forqualitymetrics,13to16forcostmetrics,17to19fordeliverymetrics,20to23forsafetymetricsand24to29formetricsofmorale).Objectivefunctions

TheideaofthePGPistosetobjectivesinorderofpriority(GhodsypourandO’Brien2001;BhagwatandSharma2009).Inthiscase,theobjectiveistomaximisethesixperformancemeasures,i.e.productivity,quality,cost,delivery,safetyandmorale,intheorderasprioritised.Thesixobjectivefunctionsinorderoftheirpriorityare:(1)(2)(3)(4)(5)(6)

MaximiseMaximiseMaximiseMaximiseMaximiseMaximise

productivity(consistingofperformancemetrics1to8)quality(consistingofperformancemetrics9to12)morale(consistingofperformancemetrics24to29)safety(consistingofperformancemetrics20to23)delivery(consistingofperformancemetrics17to19)cost(consistingofperformancemetrics13to16)

X

Downloaded by [Columbia University] at 18:35 05 January 2013 Thegoalsoftheproblemcanberestatedas:

maximizePi¼

WikXksuchthatk2i,

where,Pi¼Objectivefunctionsandi¼1to6(i¼1forproductivity,i¼2forquality,i¼3formorale,i¼4forsafety,i¼5fordeliveryandi¼6forcost)k¼1,2,3,...,n(n¼numberofperformancemetrics).(Inthepresentstudyn¼29,andk¼1,2,3,4,5,6,7,82(i¼1),k¼9,10,11,122(i¼2),k¼13,14,15,162(i¼6),k¼17,18,192(i¼5),k¼20,21,22,232(i¼4)andk¼24,25,26,27,28,292(i¼3).)Wik¼Performancemetricanalysisweightforthekthperformancemetricoftheithlevel(seeTable9,Column2).

Mathematically,theobjectivesaregivenas:

X

maximiseP1¼W1kXksuchthatk2i¼1,ðProductivityÞð2Þ

maximiseP2¼maximiseP3¼maximiseP4¼maximiseP5¼

XX

W2kXksuchthatk2i¼2,W3kXksuchthatk2i¼3,W4kXksuchthatk2i¼4,

ðQualityÞðMoraleÞðSafetyÞðDeliveryÞðCostÞ

ð3Þð4Þð5Þð6Þð7Þ

X

X

W5kXksuchthatk2i¼5,W6kXksuchthatk2i¼6,

maximiseP6¼

X

Constraints.Inpractice,theconstraintsareontheavailabilityofmanpower,time,energyandtheabilityofthe

decisionmakertokeeptheemployeesmotivatedintheimprovementprocess.Mathematically,theseconstraintshavebeenmodelledasafunctionofthevariousVSMtools.ThereareeightVSMtools:PAM,SCR,PVF,QFM,DAM,DPA,PSMandLVS.Asthetoolshavebeenevaluatedunderdifferentperformancemetrics(Table9),thesehavebeenconsideredasconstraintsinthismodel.ThustheconstraintsonthegoalprogrammingarethedifferentVSMtools(Equation(8))andthedifferentperformancemetrics(Equation(9)).

nXoXX

Wikðk2iÞ,Wlkð8ÞWikXk Min:

k¼1,2,3,4...nðn¼29Þ

l¼1,2,3,4,5,6,7,8ðforeightVSMtoolsÞ

0 Xk 1

ð9Þ

2248V.RameshandR.Kodali

InEquation(8),lrepresentstheeightVSMtoolshence,therewillbeeightconstraintsfortheoptimisationofpriority1,i.e.productivity.

PGPmethodology

UsingTORA(optimisationsoftware),sequentialprogrammingwasinitiatedwiththeoptimisationofthefirstpriority,i.e.productivityandnineconstraints(eightforVSMtoolsandoneonXk).Theiterationcounterwassettoq¼0andinitialise\"¼0.01.Thealgorithmforsequentialoptimisationisasfollows:

Step1:OptimiseithPriority(Pi)underthegivenconstraints.ObtainthebestperformancelevelsforXk(k2i).Setiterationcounterq¼qþ1

Step2:SubstitutetheoptimumvaluesofXk(k2i)intotheqthiterationconstraintstocalculatetheconstraintsforthenextpriority(i¼iþ1).LettheR.H.S.forthelthconstraintoftheqthiterationbeR:H:S:ql

qþ1Þ

(a)IftheR.H.S.foranyoftheconstraintsforthe(qþ1)thiterationfallsbelow\"i.e.R:H:S:ð \"(l¼1,2,3,l

4,5,6,7,8)eliminatetheconstraintfromfurtheriterations.

qþ1Þ

(b)IftheR.H.S.foralltheconstraintsis!\"i.e.R:H:S:ð!\"proceedtoStep3.l

Downloaded by [Columbia University] at 18:35 05 January 2013 Step3:RecordthedifferencebetweentheR.H.S.forthelthconstraint(l¼1,2,3,4,5,6,7,8)oftheqthiterationandthelthconstraint(l¼1,2,3,4,5,6,7,8)ofthe(qþ1)thiteration.LetthedifferencebeDlforthelthconstraint.

ÂÂÃÃqÂÂÃÃððqþ1ÞÞ

g½½fD󰀆󰀆l¼½½R:H:S:󰀆󰀆lÀ½½R:H:S:󰀆󰀆l

ð10Þ

Step4:CalculateDmax¼MaxfDlg(l¼1,2,3,4,5,6,7,8;ignoretheeliminatedconstraints.Recordlcorresponding

toDmax.

(a)IftheDmaxmapstomorethanonel,lhasthesamemaximumvalueforanyiteration.Anyoneofthetools

maybechosendependingontheresultsofthesubsequentiteration.Step5:Fortheqthiteration,theVSMtoolcorrespondingtolobtainedinStep4isthebestsuitedVSMtooltomaximisetheithpriority(Pi).

Step6:Ifq¼6TERMINATE.Or,ifi¼iþ1gotoStep1.

ThealgorithmwasimplementedforthecasemanufacturingorganisationtoidentifytheoptimumlevelsofoperationoftheperformancemetricsandalsotofindthebestsequenceofVSMtoolthatwillmaximisethePMs.Table10tabulatestheresultsattheendofthefirstiterationofthemodel.

InadditiontothebestsequenceofVSMselection,theresultsofPGPwillprovidethevaluesoftheperformancemetricstomaximiseoverallperformance(Table11).TheseoptimisedvaluesoftheperformanceindicatorwillhelpthedecisionmakertoidentifyandfocusonperformancemetricsthatarecrucialforoverallPMofthesystem.ThePGPwillidentifytherequiredoptimisedvaluesoftheperformanceindicatorsasabenchmark.ThedecisionmakerhastoensurethattheactualvaluesofthemetricsneverfallbelowthevaluesobtainedbyPGPoptimisation.Thisshouldbeachievedthroughadequatemappingefforts.OncetheperformancemetricsforaparticularperformancemeasurestabilisearoundthevalueidentifiedbythePGPoptimisation,thedecisionmakermustnowfocustheirattentiononthenextpriorityandchanneltheorganisation’senergytoimplementthemappingtoolcorrespondingtothatpriorityasproposedbythemodel.

ResultsofPGP

FromTable10itcanbeseenthatDlisatamaximumforl¼1andforl¼8,i.e.performanceactivitymapping(PAM)andleanvaluestreammapping(LVS).ThisobservationalsosupportstheresultobtainedfromthepreviousAHPmodel.Hence,thecaseorganisationmustfocusonimprovingitsproductivitybyfirstmappingtheprocessesusingPAMandsubsequentlyadoptingLVS.ContinuingwiththePGPmethodology,thecompletesequenceofVSMtoolapplicationwasfound.ThisisillustratedasaflowchartinFigure3.

TheoptimumvaluesforXk(k2I¼1)areX1¼0:09,X2¼1,X3¼0:45,X4¼1,X5¼1;X6¼0;X7¼0;X8¼0.Accordingtotheresult,themetricsstoppingsupplierprocess½½ðX󰀆󰀆6Þandshortingcustomerprocess(½½X󰀆󰀆7)havenoimpactontheoptimisationofproductivity.Itshowsthatonlysixoutoftheeightmetricscontributetothemaximisationofpriority1(P1).Further,threemetrics–partsperlabourhour(½½X󰀆󰀆2),linestoptime(½½X󰀆󰀆4)andchangeovertime(½½X󰀆󰀆8)–arethemostcriticalasfarasproductivityisconcernedandthetargetmustbetomaintainthematashighalevelaspossible.RefertoTable11foroptimumperformancevaluesofall29metrics.

InternationalJournalofProductionResearch

2249

9lD9479837593272421........00000001S.H16327320672757.........0R0000000À9V33333333211111111XOG........0000000008L33333333211111111XU........C0000000007I955551220010003XM........E0000000006333333332R11111111X/A........0000000005333333332D11111111XT........E0000000004S32558201031111XM........E0000000003R26666665240000002XRW........0000000002V76666667240000001XDM........0000000001D76666667240000001XW........L0000000000J76666667240000001XN........I0000000009L666677662103001100XRD........0000000008C47558741101003002XR........Q0000000007C65555159120000202XDM........0000000006V5535525100420010XM........R00000000053781471N10002103XC........L000000000446328251N02001013XIT........0000000003661877771C00140000XC........S000000000231R445555XC211140000S.........000000000191D448XL110140001Y.........0000000000F7177777777XED000050000.........00000000019S07787777XU510040000C.........000000000.jMFMMAbPMSOARVFAPCSPQDDSVPLVO1GLU1CIM1ER/0ADT9.E0SM1ERR0WVD0MD3W3.L0JN0ILR0DC2R1Q.0CD1MVM0RNC0LNI0TC6C8.S0RC0SDL0YFE0DSU5.C0.TP/GC1PPC0gSnisPuS0SsleTvD1elEeTcnS1aLmP5rP4.oT0freHpL1mPuTmK1.0itWpOecn.ca1ir1temmruoemlmfrb.fitearppTePODownloaded by [Columbia University] at 18:35 05 January 2013 Table10.Objectivefunction(row3)andconstraints(Rows4torow11)forP-quality.2250V.RameshandR.Kodali

Figure3.FlowchartforVSMtoolapplication.

ThefinalinferencefromiterationoneofthePGPmodelisthatPAMfollowedbyLVSmappingmustbeimplementedintheorganisation,withtheobjectiveofimprovingproductivity.EffortsmustbesustaineduntiltherequiredoptimumperformanceofthemetricsasgivenbyPGPisachieved.

4.Resultsandconclusion

Onapracticalfront,thenoveltyoftheproposeddecisionframeworkliesinitsabilitytosimultaneouslyconsiderboththecurrentstateandthefuturestateofasystemthroughaninnovativeformulationoftheintegratedAHP-PGPapproach.ThisallowstheframeworktoguidetheuserintheselectionofthebestsequenceoftheVSMtooltoeliminateallpossibleformsofwastewithintheshortesttimeframe.Additionally,thetheoreticalcontributionoftheapproachliesintheiterativePGPstrategywhichallowstheusertoaccuratelydeterminetheoptimumvaluesofeachperformancemetricthatmaximisesoverallperformance.Together,thedecisionframeworksuccessfullybridgesthegapidentifiedinthemethodsknownatpresentforselectingVSMtools.

Forthepresentmodel,AHPisusedtoselectthebestVSMtoolthatwillgivethehighestrateofimprovementforthecasestudy.Fortheconsideredcase,productivity(PRD)wasfoundtobeofthehighestimportanceand,hence,usingAHP,processactivitymapping(PAM)hasbeenidentifiedasthemostsuitabletooltoachieverapidimprovementsinproductivityontheshopfloorandtomaximisethereturnonthemappingeffort.

ThebestVSMtoolselectedviatheAHPmodelonlyguaranteesthequickestmethodforremovalofwasteinanorganisationdependingonthecurrentorganisationalpreferencesandgoals.Accordingtotheleanphilosophy,oncethetoolhasbeenappliedandtherelevantwaste(s)reduced,theprioritiesoftheorganisationshouldthenrapidlyshifttofocusonanotherPMand,hence,inthelongterm,focusonremovingallformsofwasteandbecomingtrulylean.Thisisthemantraforcontinuousimprovementasproposedbythemodel.

Hence,theoverallapproachsuggestedbythedecisionframeworkistouseAHPtokick-startvaluestreammappingtechniquesinthecaseorganisationandalsobuildmomentumforthewaste-removalprocess.Oncethathasbeenachieved,thebestsequenceofmappingtools,basedontheresultsofPGP,mustbeusedtosustaininterestinwasteidentificationandremovalandalsotomeettheobjectiveofkaizenorcontinuousimprovementinthelongterm.

Forthepresentcase,PAMshouldbesequentiallyfollowedbyLVS.OncetheperformancemetricsfollowingtheshopfloorrestructuringsuggestedbyPAMstabilise,effortsmustbedivertedtoimprovingtheplantusingthecurrentstate–futurestateVSMtechnique,asoriginallysuggestedbyToyota(LVS).Thetargetofthiseffortshouldbetoachieve100%ofthetargetedpartsperlabourhour(PLH),linestoptime(LST),equipmentdowntime(EDT)andchangeovertime(C/T),sincethesemetricshavethehighestimpactonproductivity,whichisourprimarygoal.Qualityisthenextonourchecklist;forthis,PGPsuggestsusingqualityfiltermapping(QFM)toachieveatleast50%oftargetedcustomersatisfaction(CUS).TheotherprioritiesfollowinsequenceandtheappropriatetoolasgivenbyPGPmaybeusedtoachievethem.Targetperformancemustbetoachieve100%ofthemisseddeliverycycles(MDC),employmentsecurity(EMS),employeeinvolvement(EMI),culture(CUL)andgovernance(GOV)metricsbecausethesehavethehighestimpactonmaximisingtheirrespectivePMs.

Inthepresentresearchpaper,thesolutiontotheproblemhasbeenmodelledasamulti-criteriadecision-making(MCDM)approach.SincedatainMCDMproblemsareimpreciseandchangeable(TriantaphyllouandSanchez1997),anaturalextensionofthepresentresearchistoincludesensitivityanalysis.AsDantzig(1963,p.32)stated:‘Sensitivityanalysisisafundamentalconceptintheeffectiveuseandimplementationofquantitativedecisionmodels,whosepurposeistoassessthestabilityofanoptimalsolutionunderchangesintheparameters.’Forthegivenproblem,asproposedbyTriantaphyllouetal.(2007),thiscanbecarriedouttostudytheimpactofchangesintheweightsassignedtothecriteriaandsub-criteriaontheselectionoftheVSMtool.Byknowingwhichdataismorecritical,thedecisionmakercanmoreeffectivelyfocustheirattentiononthemostcriticalpartsoftheproblem.Alternatively,sensitivityanalysiscanalsobeappliedinthedata-gatheringphasetocalculate,withhigheraccuracy,theweightsthataremorecriticalintheinterestofaconstrainedbudget.

Downloaded by [Columbia University] at 18:35 05 January 2013 InternationalJournalofProductionResearch2251

References

Anand,G.andKodali,R.,2008.Performancemeasurementsystemforleanmanufacturing:aperspectivefromSMEs.

InternationalJournalofGlobalisationandSmallBusiness,2(4),371–410.

Badri,M.A.,2001.AcombinedAHP-GPmodelforqualitycontrolsystems.InternationalJournalofProductionEconomics,

72(1),27–40.

Bhagwat,R.andSharma,M.K.,2009.AnapplicationoftheintegratedAHP-PGPmodelforperformancemeasurementof

supplychainmanagement.ProductionPlanningandControl,20(8),678–690.

Cebi,F.andBayraktar,D.,2003.Anintegratedapproachforsupplierselection.LogisticsInformationManagement,16(6),

395–400.

Chandra,S.andKodali,R.,1998.Justificationofjust-in-timemanufacturingsystemsforIndianindustries.Integrated

ManufacturingSystems,9(5),314–323.

Daum,J.H.andBretscher,P.,2004.Measuringperformanceinaknowledgeeconomy:linkingsubjectiveandobjective

measurementintoa‘vector-based’conceptforperformancemeasurement.FourthInternationalConferenceonTheoryandPracticeinPerformanceMeasurementandManagement,28–30July,Edinburgh.

Dantzig,G.B.,1963.Linearprogrammingandextensions.Princeton,NJ:PrincetonUniversityPress.Dennis,P.,2007.Leanproductionsimplified.NewYork:ProductivityPress.

Ghodsypour,S.H.andO’Brien,C.,2001.Thetotalcostoflogisticsinsupplierselection,underconditionsofmultiplesourcing,

multiplecriteriaandcapacityconstraint.InternationalJournalofProductionEconomics,73(1),15–27.

Hines,P.andRich,N.,1997.Thesevenvaluestreammappingtools.InternationalJournalofOperationsandandProduction

Management,17(1),46–.

Hines,P.,etal.,1998.Valuestreammanagement.InternationalJournalofLogisticsManagement,9(1),25–42.

KaizenInstitute,2010.KAIZENleanandgreen.Availablefrom:http://www.leanadvisors.com/index.php/what/green/

lean_and_green

Lev,B.,2001.Intangibles:management,measurement,andreporting.Washington,DC:BrookingsInstitutionPress.Liker,J.K.andMeier,D.,2006.TheToyotawayfieldbook.NewYork:McGraw-Hill.

Mahindhar,V.,2005.Designingtheleanenterpriseperformancemeasurementsystem.Boston,MA:MassachusettsInstituteof

Technology,62–67.

Melnyk,S.A.,etal.,2004.Metricsandperformancemeasurementinoperationsmanagement:dealingwiththemetricsmaze.

JournalofOperationsManagement,22(3),209–218.

Neely,A.,etal.,2005.Performancemeasurementsystemdesign:aliteraturereviewandresearchagenda.InternationalJournalof

OperationsandProductionManagement,25(12),1228–1263.

Ohno,T.,1988.Toyotaproductionsystem:beyondlarge-scaleproduction.Cambridge,MA:ProductivityPress.

Percin,S.,2006.AnapplicationoftheintegratedAHP-PGPmodelinsupplierselection.MeasuringBusinessExcellence,10(4),

34–49.

Radcliffe,L.L.andSchniederjans,M.J.,2003.Trustevaluation:anAHPandmulti-objectiveprogrammingapproach.

ManagementDecision,41(6),587–595.

Roger,N.,1987.JustificationofFMSwiththeanalyticalhierarchyprocess.JournalofManufacturing,7(3),175–182.Rother,M.,2010.Toyotakata.NewYork:McGraw-Hill.

Rother,M.andShook,J.,1999.Learningtosee.Boston,MA:LeanEnterpriseInstitute.

Satty,T.L.,1982.Prioritysettingincomplexproblems:lecturenotesineconomicsandmathematicalsystem.FifthInternational

ConferenceonMultipleCriteriaDecisionMaking,9–13August,Mons,Belgium.

Schniederjans,M.J.andGravin,T.,1997.Usingtheanalyticalhierarchyprocessandmulti-objectiveprogrammingforthe

selectionofcostdriversinactivity-basedcosting.EuropeanJournalofOperationalResearch,100(1),72–80.

Shingo,S.,1988.Non-stockproduction:theShingosystemforcontinuousimprovement.Cambridge,MA:ProductivityPress.Shingo,S.,19.AstudyoftheToyotaproductionsystemfromanindustrialengineeringviewpoint.Cambridge,MA:Productivity

Press.

Singh,R.K.,etal.,2006.Leantoolselectioninadiecastingunit:afuzzy-baseddecisionsupportheuristic.InternationalJournal

ofProductionResearch,44(7),1399–1429.

Thompson,P.andWallace,T.,1996.Redesigningproductionthroughteamworking:casestudiesfromtheVolvoTruck

Corporation.InternationalJournalofOperationsandProductionManagement,16(2),103–118.

TotalSystemsDevelopmentInc,2001.Leanmanufacturing:aplantfloorguide.Dearborn,MI:SocietyofManufacturing

Engineers.

Triantaphyllou,E.andSanchez,A.,1997.Asensitivityanalysisapproachforsomedeterministicmulti-criteriadecisionmaking

Methods.DecisionSciences,28(1),151–194.

Trochim,W.M.K.,2006.Likertscaling.Availablefrom:http://www.socialresearchmethods.net/kb/scallik.phpWikipedia,2010.Corporategovernance,22May.

Womack,J.,etal.,1991.Themachinethatchangedtheworld:thestoryofleanproduction.NewYork:HarperPerennial.

Downloaded by [Columbia University] at 18:35 05 January 2013