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AdvancesinProductionEngineering&Management ISSN1854‐6250
Volume13|Number1|March2018|pp69–80 Journalhome:apem‐journal.org
https://doi.org/10.14743/apem2018.1.274 Originalscientificpaper
Risk management in automotive manufacturing process based on FMEA and grey relational analysis: A case study
Baynal, K.a,*, Sarı, T.b,*, Akpınar, B.c aKocaeli University, Department of Industrial Engineering, Kocaeli, Turkey bKonya Food and Agriculture University, Department of International Trade and Business, Konya, Turkey cGeneral Electric, İstanbul, Turkey
A B S T R A C T A R T I C L E I N F O
Riskmanagement is an important issue inmanufacturing companies in to‐day’scompetitivemarket.Failuremodesandeffectsanalysis(FMEA)methodisa riskmanagement tool to stabilizeproductionandenhancemarketcom‐petitiveness by using risk priority numbers (RPN). Although the traditionalFMEA approach is an effectively and commonly used method, it has someshortcomingssuchasassumptionofequalimportanceofthefactors,severity,occurrence and detectability, and not following the ordered weighted rule.Thus, in order to improveRPN, an integratedmethod combining grey rela‐tional analysis (GRA)with FMEA is used in this study. The purpose of thispaperistocontributetoriskmanagementactivitiesbyproposingsolutionstoassembly line problems in an automotivemanufacturing company by usingcombinedGRAandFMEAmethod. In theproposedmethod, theprioritiesofproduction failures were determined by GRA approach and these failureswereminimized by using FMEA technique. The study results indicated theactionsthatleadtoenhancementintheproduct.Theimplementationofcor‐rective/preventiveactivitiesresultedin96%improvementindoorsealcutsproblemcausedbythedoorstepassembly.Doorsealcutsproblemcausedbyinstrument panel assembly and the noisy doorwindowproblem are solvedcompletely.
©2018PEI,UniversityofMaribor.Allrightsreserved.
Keywords:Automotivemanufacturing;Riskmanagement;Failuremodesandeffectanalysis(FMEA);Greyrelationalanalysis(GRA)
*Correspondingauthor:[email protected](Sarı,T.)
Articlehistory:Received12July2017Revised16January2018Accepted20February2018
1. Introduction
Before a newproduct is introduced to amarket, themanufacturing companies probably facemanyproblemsinthestagesofdesign,plan,productionanddelivery.Itisverycriticaltodetectandsolvetheseproblems,beforetheproductreachesacustomer.Somefailuresareeasytode‐tectwhilesomeofthemremainhidden.Thewholeprocessshouldbeevaluatedcarefullyandtheappropriatequalitycontroltechniquesshouldbeusedinordertofindoutthesehiddenfailures.OneofthemosteffectivemethodstodeterminethefailuresinanyprocessisFailureModeandEffectsAnalysis(FMEA).
FMEAcanbeexpressedasaspecificmethodologyinordertoevaluateaprocess,system,ser‐viceordesignforpossiblewaysinwhichfailurecanoccur[1].Risks,problems,concernsorer‐rorsaredifferenttypeoffailures.Failuremodecanbedescribedasaproductfailingtoperformitsdesired function,describedbytheexpectationsof thecustomers.Failureemerges fromthedeviationfromstandardsintheconditionsofmachine,method,materialandworkforce,affect‐ingthequalityofaproductoraprocess.TheFMEAanalysisfollowsawell‐definedsequenceof
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stepsthatincludes(1)failuremode,(2)failureeffects,(3)causes,(4)detectability,(5)correc‐tiveorpreventiveactionsand(6)rationaleforacceptance[2].Today,withtheincreasingcom‐petition in themarket, any deficiency and deviation in product performance result inmarketshareloss.AlthoughthetraditionalFMEAemployingriskprioritynumbers(RPN)isanefficientandeffectivetooltostabilizeproductionandenhancemarketcompetitiveness,ithasbeencriti‐cizedforthefollowingshortcomings:its(1)highduplicationrate,(2)notfollowingtheorderedweightedrule,(3)assumptionofequalimportanceofseverity(S),occurrence(O),anddetecta‐bility(D)and(4)failuretoconsiderthedirectandindirectrelationshipsbetweenthemodesandthecausesoffailure[3].
In thisstudy,an integratedmethodcombiningFMEAandGreyRelationalAnalysis(GRA) isusedinordertoovercometheshortcomingsoftraditionalFMEAmethod.GRAisusedtodeter‐mine the priorities of production failures in an automotivemanufacturing company. The twofailures,doorsealcutandnoisywindowproblems,havebeenminimizedbyusingFMEAmeth‐odology.
2. Literature review
FMEAmethodologyiswidelyusedtomanageriskinindustriessuchasmanufacturing,automo‐tive,andaerospace.VinodhandSanthosh[4]reportedanapplicationofdesignFMEAtoanau‐tomotive leaf springmanufacturing organization in India. Implementation of fuzzy developedFMEAmethodtoaircraftlandingsystem,whichisoneoftheimportantpotentialfailuremodeinaerospaceindustry,hasshownthestrengthofthemethodinmanagingrisk[5].Chang[6]com‐binedgeneralizedmulti‐attributeFMEAandmulti‐attributeFMEAtoimproveLCDmanufactur‐ingprocessinacompanyinTaiwan.SegismundoandMiguel[7]proposedamethodologicalap‐proach to effective risk management in new product development in a Brazilian automakercompanybyusing FMEA technique. Bandukaetal. [8] integrated lean approachwith processFMEAinautomotiveindustry.Liuetal. introducedariskprioritymodelbycombininghesitant2‐tuple linguistic term sets and an extended QUALIFLEXmethod and FMEAmethodology forhandlingahealthcareriskanalysisproblem[9,10].Barkovicetal.usedFMEAmethod in im‐provementofnewspaperproductionsystemquality[11]. GRAhasbeenusedbymanagerstomakedecisionsunderuncertaintyinmanydifferentareassince1982.FengandWang[12]measuredthefinancialperformanceofairwaycompanieswiththehelpofGRA.HsuandWen[13]proposedadesigntodealwiththetrafficandflightfrequencyinairwaysusingGRA.InthestudyofLinandLin,oneofthetechniquesusedforoptimizationofwireerosionsystemwasgreyrelationanalysismethod[14].Wangetal.[15]proposedahybridmethodology using grey relational analysis and experimental design to solve several multi‐criteriadecisionmakingproblemssuchas,aflexiblemanufacturingsystem,arapidprototypingprocess and an automated inspection system. Palanikumar et.al. [16] optimized the results ofpolymermaterialprocesswithgreyrelationanalysismethod.RajeswariandAmirthagadeswa‐ran[17]usedgreyrelationalapproachtoimprovemachinabilitypropertiesofendmillingpro‐cess.Wang [18] developed amodel formeasuring the performance of logistic companies viagreyrelationalanalysismethod.Rameshetal. [19]proposedaneffectivemodel to investigateturningofmagnesiumalloybyusinggreyrelationalanalysismethod. A combinationof FMEAandGRA techniques areusedbyauthors inorder to eliminate theshortcomings of FMEA. Pillay andWang [2] used an integratedmethod combining FMEAandgrey theory to investigate thesystem failures in fuzzyenvironment foranocean‐going fishingvessel in their study. Bagheryetal. [20] implemented process FMEAmethod combiningwithDEA(dataenvelopmentanalysis)andGRAinanautomotivecompanyproducingautopartsforSamand,Peugeot405andPeugeot206.
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3. Materials and methods
In thisstudyacombinedmethodologyof failuremodesandeffectanalysisandgreyrelationalanalysis is used. The priorities of production failureswere determined byGRA approach andfailureswereminimizedbyusingFMEAtechnique.
3.1 FMEA method
FMEAwasfirstdevelopedasanassessmenttool to improvetheevaluationofthereliabilityofmilitarysystemsandweaponsintheUSarmyinthelate1940s.ThismethodwasalsousedforApollo space missions in the 1960s by the National Aeronautics and Space Administration(NASA)[3].Inthelate1970sFMEAwasusedbyFordMotorCompanyinautomotiveproductionprocesses [6]. Because these applications resulted in satisfactory improvements in the FordCompany, themethodhasbeenwidelyused inautomotive industryasa riskassessment tool.TodayFMEA isappliedsuccessfully to industries suchasaircraft, automotive,medicine, semi‐conductorsandfoodindustry.IntheFMEAapproach,foreachofthefailuresidentified(whetherknownorpotential),anestimateismadeofitsoccurrence(O),severity(S)anddetection(D)[1].Occurrenceistheprobabilityofoccurrenceofthefailureanditscause.Detectionisanevalua‐tionprocesstofindpotentialfailuresintheproduct.Severityisanexpressionofimportanceandemergency of potential systemdefaultmode. FMEA technique evaluates the risk of failure byusingRPNs.TheRPNvalueisfoundbytakingtheproductofS,O,andDonascalefrom1to10.HigherRPNvalueindicatesahigherpriority.
3.2 Grey relational analysis
Greyrelationalanalysis isamulti‐criteriadecisionmakingmethodusedbydecisionmakerstotaketherightdecisionundercircumstanceswithlimitedanduncertaindata[21].GRAapproachexploressystembehaviorusingrelationalanalysisandmodelconstructions[2].Thegreysystemprovidessolutionstoproblemswheretheinformationisincomplete,limitedorcharacterizedbyrandomuncertainty.Thegrey theoryhasbecomeapopular techniqueprovidingmultidiscipli‐nary approaches in recent twenty years. The grey relational analysis was first developed byJulongDengin1982[22].Themodelincludesthreetypesofinformationpoints:white,greyorblack.Themaingoal is to transferblackpoints in thesystem to thegreypoints.Greyrelationanalysisconsistsofsixbasicsteps.Thesestepsareexplainedbelow[19,23,24]:
Step1:Constructanormmatrix . It is assumed that therearen data sequences includingmcriteria:
1 2 …1 2 …… … … …1 2 …
(1)
where istheentityinthei‐thdatasequencecorrespondingtothej‐thcriterion.
Step2:Sincemulti‐criteriadecisionmaking(MCDM)problemsmaycontainavariationofdiffer‐ent criteria, the solution needs normalization. Normalization process based on properties ofthreetypesofcriteria,largerthebetter,smallerthebetter,andnominalthebest:
; larger the better (2)
; smaller the better (3)
1| |
,; nominalthebest (4)
isthetargetvalueforthecriterionj,and ≤ ≤ .
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Step3:NormalizethedatasetandgenerateareferencesequencebasedonEq.2toEq.4.Nor‐malizedmatrixisexpressedas :
1 2 …1 2 …… … … …1 2 …
(5)
Step4:Calculateabsolutevaluetable.Thedifferencebetweenanormalizedentityanditsrefer‐encevalueiscalculated.Thedifferenceisshownas∆ .
∆ | | (6)
∆
∆ 1 ∆ 2 … ∆∆ 1 ∆ 2 … ∆… … … …
∆ 1 ∆ 2 … ∆
(7)
Step5:Computegreyrelationalcoefficient ,applyingfollowinggreyrelationalequation:
∆ ∆∆ ∆
(8)
where ∆ ∆ , ∆ ∆ , and ∆ and ∈ 0,1 . isthe distinguishing index and inmost cases it takes the value of 0.5 offeringmoderate distin‐guishingeffect.
Step6:Compute thegreyrelationaldegree.Greyrelationaldegreewhich indicates themagni‐tudeofcorrelationorsimilarity.Theoverallgreyrelationaldegree Γ iscalculatedbytakingaveragevalueofgreyrelationalcoefficientsbyusingthefollowingequation:
Γ (9)
where referstotheweightofthej‐thcriterion.Thesumoftheweightsofallcriteriamustequalto1.
3.3 Integration of grey theory and FMEA method
ThetraditionalFMEAmethodcannotassignthepossibilityofoccurrenceoffailure,itsdetecta‐bilityandseveritycomplywith therealworld.The integrationofgrey theory toFMEAallowsengineersanddecisionmakerstoassignrelativeweightsdependingonresearchandproductionstrategies.Indecisionmakingproblems,thefactorserieswiththehighestgreyrelationdegreegives the best alternative. The greater the relation degreemeans the smaller effect of failuresourceinFMEAapplication.Forthisreason,theincreasingrelativedegreeshowsthedecreaseinriskpriorityofpotentialsourceswhichhavetobeimproved.
4. Case study
Thecasestudywasheld inacarmanufacturing company in theTurkishautomotive industry.Theaimofthestudywastosolvetheassemblylineproblems.Thecompanymainly facedtwotypesofproblems.Thefirstonewasthecardoorsealproblemandthesecondonewasthenoisycarwindowproblem.
4.1 Formulation of problems and causes
Thecardoorsealproblemcanbeexplainedasatearorcutinthesealofthecardoors.Thedoorsealservesasabarrierprotectingtheinnercaragainstdustandwaterfromtheoutside.Ifthe
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sealisdamagedandifthisdamagecannotbedetectedthroughqualitycontrolprocesses,itmaycause severe customer complaints. The noisy carwindowproblem is the annoying noise andshakingproblemwhenthewindowglassmovesupanddown.
ThefactoryhasusedParetoanalysistofindoutthefailuresinmanufacturingandtheiroccur‐renceprobability.Thenumbersabouttheoccurrenceprobability,thecausesoffailuresandthefailuredetectionpointsaregiveninTable1.Thelinepointinthetablerepresentstheproblemsdetectedthroughthecontrolpointsof theassembly line itself.Finalpoint is thepointofade‐tailedcontrolofthecarjustbeforeitleavestheassemblyline.Pre‐qualitypointisacontrolpointfor repairedcarsbeforequalitycontrol.Afterqualitycontrolof theproducts, fiveof themareverycarefullycontrolledindetailataqualitycontrolpoint.Thecompany’sexpertteamhasde‐termined two important production problems and the most probable causes by using FMAEtechnique.Theresultingcausesarelistedbelow:
Causesforcardoorsealcutortearproblemaresummarizedbelow:
Step: TearorcutinthecardoorsealduringthedoorstepassemblyIP: TearorcutinthecardoorsealduringinstrumentpanelassemblyDoorlock: TearorcutinthecardoorsealduringdoorlockassemblySeat: TearorcutinthecardoorsealduringcarseatassemblyOperator: Damagingofthecardoorsealbyoperatorduringplacementoftheseal
Causesfornoisywindowproblemareasfollows:
Rivetposition: TheeffectofthepositionoftherivetofwindowmechanismFixingequipment: TheincompatibilitybetweenwindowmechanismandfixingequipmentHoleposition: Theeffectofthepositionoftherivethole
Table1Problemsandcauses
Failure Cause DetectionPoint
Cut/tearindoor
seal
Line Final Pre‐quality Quality Detailedquality Customer
Step 0 4 0 5 0 2IP 0 5 0 2 0 0Doorlock 0 2 0 2 0 0Seat 0 0 0 2 0 0Operator 0 0 0 2 0 0
Noisy
window Line Final Pre‐quality Quality Detailedquality Customer
Rivetposition 0 88 0 9 0 0Fixingequipment 0 108 0 20 0 0Holeposition 0 190 0 20 0 0
4.2 Probability of occurrence, detectability, severity
Occurrence(O):
Occurrenceistheprobabilityoffailureoccurrenceanditscause.Theprecautionsfordetectingthefailuresarenottakenintoconsiderationinthisstep.Onlythemethodsdeterminedforpre‐venting failureareconsidered. If theprocess isunderstatisticalprocesscontrol, theevolutiondependson thestatisticaldata.Otherwise, intangibledata fromjudgmentsareused forevolu‐tion.Inthefactory,theoccurrenceandthedetectionpointsoffailuresareexpressedbyParetoanalysisandthentransferredtoa"1‐10"scale.Inthetable, theOcolumndescribestheoccur‐renceofprobabilitybetweenthevalues1and10.
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Table2CalculationofOvaluesbasedonParetoanalysis
Failure Cause DetectionPoint Cut/tearindoor
seal
Line Final Pre‐quality Quality Detailedquality Customer O
Step 0 54 0 55 0 12 10IP 0 25 0 32 0 0 7Doorlock 0 12 0 0 0 0 3Seat 0 0 0 12 0 0 3Operator 0 0 0 12 0 0 3
Noisy
window Line Final Pre‐quality Quality Detailedquality Customer O
Rivetposition 0 88 0 9 0 0 7Fixingequipment 0 190 0 20 0 0 10Holeposition 0 109 0 20 0 0 9
Detectability(D):
Detectionisanevaluationprocesstofindthepotentialfailuresinaproduct,beforeitleavestheassemblyline.Thefailureshouldbeacceptedasithasoccurredandthecriteriaforfailuredetec‐tionshouldbedetectedbefore theproducthasbeen introducedtoaconsumer. In the factory,according to results fromPareto analysis, the failures are scored for their detectionpoints tocalculateDvalues.ThescaleusedinthecalculationofDvalueisbelow:
Linepoint: 1‐2Finalpoint: 3‐4Pre‐quality: 5‐6Quality: 7‐8Detailedquality: 9Customer: 10
Sinceallthefailuresaredetectedmorethanonceindifferentpoints,Dvaluesarecalculatedbytheweighedmatrix(Table3)andbasedonQLSParetoanalysis(Table4).
Table3WeightedDvalues
Failure Cause DetectionPoint
Cut/tearindoor
seal
Line Final Pre‐quality Quality Detailedquality Customer D
Step 0 54X4 0 55X8 0 12X10 776IP 0 25X4 0 32X8 0 0 356Doorlock 0 12X4 0 0 0 0 48Seat 0 0 0 12X8 0 0 96Operator 0 0 0 12X8 0 0 96
Noisy
window Line Final Pre‐quality Quality Detailedquality Customer D
Rivetposition 0 88X4 0 9X8 0 0 424Fixingequipment 0 190X4 0 20X8 0 0 920Holeposition 0 109X4 0 20X8 0 0 596
Table4CalculationofDvaluesbasedonQLSParetoanalysis
Failure Cause DetectionPoint
Cut/tearindoor
seal
Line Final Pre‐quality Quality Detailedquality Customer D
Step 0 54X4 0 55X8 0 12X10 6IP 0 25X4 0 32X8 0 0 6Doorlock 0 12X4 0 0 0 0 4Seat 0 0 0 12X8 0 0 8Operator 0 0 0 12X8 0 0 86
Noisy
window Line Final Pre‐quality Quality Detailedquality Customer D
Rivetposition 0 88X4 0 9X8 0 0 4Fixingequipment 0 190X4 0 20X8 0 0 4Holeposition 0 109X4 0 20X8 0 0 5
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Severity(S):
Severity is an expression of importance and urgency of a potential systemdefaultmode. Theonlyevaluationcriterionforseverity istheeffectofa failure.Theseveritydegreesaredefinedaccording to thedegreeof theeffectsonproduct, system, customerand legalobligations.Thefailurescalematrixof the factory’squalitysystem isuseddirectly in thisstudy(Table5).Theseverityvaluesarecalculatedwiththehelpofthistable.Table6givestheSvaluesdeterminedbythecompany’sexpertsusingqualityleadershipsystem(QLS)Paretoanalysis.
Table5Failureincreasematrixforseveritycalculations
FailureSeverity(QualityStandards)
PickLevel
IncreaseLevel
Lineteamleader
Teamleader
Areamanager
Qualityassur‐ancemanager
Factorymanager
CauseorQuality
Blitz(10‐9)Failureeffectsauto/drivercontrol,customersafetyandlegalconditions.
1 X X X 3 X X
Sampling 1 X X X X 3 X
CauseorQuality
A(8‐7)Failureisveryannoyingandcustom‐erfilesacomplainttovendor/service.
1 X X X 3 X
Sampling 1 X X X X 3 X
CauseorQuality
B(6‐5)Failureisannoying,causingcustomerunsatisfactionandcomplaintsofguarantee.
5 X X 8 X 10 X X
Sampling 2 X X X 4 X X
CauseorQuality
C(4‐3)Failureisdetectedbyeducat‐ed/criticalcustomersanditneedslongtimeimprovements.
10 X X 15 X
Sampling 4 X X X 6 X
Table6SeveritycalculationbasedonQLSParetovalues
Failure Cause DetectionPoint
Cut/tearindoor
seal
Line Final Pre‐quality Quality Detailedquality Customer S
Step 0 54 0 55 0 12 7IP 0 25 0 32 0 0 7Doorlock 0 12 0 0 0 0 7Seat 0 0 0 12 0 0 7Operator 0 0 0 12 0 0 7
Noisy
window Line Final Pre‐quality Quality Detailedquality Customer S
Rivetposition 0 88 0 9 0 0 6Fixingequipment 0 190 0 20 0 0 6Holeposition 0 109 0 20 0 0 6
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4.3 Calculation of risk priority number (RPN)
RPNiscalculatedbymultiplicationofO,DandSvalues.RPNshowstherelativeimportanceoffailurecauses.TheresultingrankofRPNvalueshelpthedecisionmakerstodecidewhichcauseshouldbeimprovedfirst.ThehighesttheRPNvaluemeansthefirstrate.TherankingaccordingtoRPNisshowninTable7.
Table7Riskprioritynumbers
Failure Cause O D S RPN Rank
Cut/tearin
doorseal
Step 10 6 7 420 1
IP 7 6 7 294 2
Doorlock 3 4 7 84 6
Seat 3 8 7 168 5
Operator 3 8 7 168 5
Noisy
window Rivetposition 7 4 6 168 5
Fixingequipment 10 4 6 240 4
Holeposition 9 5 6 270 3
4.4 Calculation of grey relational coefficient
TheRPNinTable7aretransferredtogreyRPNvaluesandreorderedbyusinggreyrelationalanalysis.ThenthedifferencematrixisconstructedbyusingEq.8:
1 2 31 2 31 2 31 2 31 2 31 2 31 2 31 2 3
10 6 77 6 73 4 73 8 73 8 77 4 610 4 69 5 6
1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3
9 5 66 5 62 3 62 7 62 7 66 3 59 3 58 4 5
Accordingtodifferencematrix,∆min=2,∆max=9andisassumedas0.5value.Aftercalculationofdifferencematrix,greyrelationalcoefficientsarecalculatedbyusingEq.8.
∆ ∆∆ ∆
2 0.5 99 0.5 9
0.481
Thefollowingmatrixisconstructedbyusinggreyrelationalcoefficients:
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1 2 31 2 31 2 31 2 31 2 31 2 31 2 31 2 3
0.481 0.684 0.6190.619 0.684 0.6191.000 0.867 0.6191.000 0.565 0.6191.000 0.565 0.6190.714 1.000 0.7890.556 1.000 0.7890.600 0.882 0.789
ThelaststepistocalculatetheGreyRPNtodeterminethepriorities.Table8showstheweightsofcostbasedpriorities.
Table8Costbasedweights WO WD WS
Cost 2.6€ 1.3€ 2.6€Weight 0.4 0.2 0.4
GreyrelationaldegreesarefoundbytheformulainEq.8.Thegreyrelationaldegreeofthefirstfailureleveliscalculatedas0.577byusingEq.9.
Γ 0.481 0.4 0.684 0.2 0.619 0.4 0.577
TheweightedgreyRPNvaluesarefoundasfollows:
WeightedGreyRPN=
0.5770.6320.8210.7610.7610.8230.7590.742
TheRPNandGreyRPNvalues are listed comparatively inTable9.As shown in the table, theweightofrivetpositiondiffersfromonemethodtotheother.AccordingtotherankinginTable9, themost importantproblem is thedoorseal cutscausedbystepassembly.Thesecond im‐portantproblemisthesealcutscausedbyinstrumentpanelassembly.Thethirdoneisthenoiseproblemof thewindowcausedbythepositionof thehole.The least importantproblemisde‐finedasnoisywindowproblemcausedbyrivetholeposition.Thepriorityofdecisionmakersistoinitiateimprovementonthesemosturgentproblems.
Table9ThecomparisonofFMEARPNandgreyRPN
Failure Cause O D S FMEARPN Rank GreyRPN Rank
Cut/tearin
doorseal
Step 10 6 7 420 1 0.577 1IP 7 6 7 294 2 0.632 2Doorlock 3 4 7 84 6 0.821 6Seat 3 8 7 168 5 0.761 5Operator 3 8 7 168 5 0.761 5
Noisy
win‐
dow Rivetposition 7 4 6 168 5 0.823 7
Fixingequipment 10 4 6 240 4 0.759 4Holeposition 9 5 6 270 3 0.742 3
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4.5 Results and discussion
The solutions are developed according to the resulting grey RPN ranks. After improvementsbetterresultsinthequalitymetricsareobtained.
Thesolutionfordoorsealcutsproblemcausingfromdoorstepassembly:
Sinceadoorsealpreventswateranddustleakages,atearorcutinthedoorsealcausescus‐tomercomplaints.Therearefivebasiccausesforthedoorsealproblems.BasedonTable9,themostimportantreasonforthisproblemistearorcutindoorsealduringstepassemblyprocess.Whenthedoorstepassemblyprocesswasinspectedindetail,itwasdeterminedthatthesharpcornersofthestepcausedcutsinthesealduringassemblyofthestepbyanopera‐tor.Asapartofcorrectiveorpreventiveactivities,alltheareasofsealtowherethestepcor‐ners hitwere detected.Magnetic protectorsweremade. The operators have begun to usetheseprotectorsinrelevantareasduringassembly.Onemonthlater,qualityrecordsindicat‐edanimportantdecreaseinsealcutsbytherateof96%.
Thesolutionfordoorsealcutsproblemcausingfrominstrumentpanelassembly:
Cutortearindoorsealduringassemblyofinstrumentpanelgetsthesecondrankinpriority.Thesharp‐edgedframeoftheinstrumentpanelswasidentifiedasthecauseforthisproblem.Thecorrectiveandpreventiveactivitiesweredevelopedaseffectivesolutionstotheproblem.Asaresultofcorrectiveorpreventiveactivities,thepotentialtangibleareasofsealwerede‐termined.Magneticprotectorsweredesigned toprotect thesurfaceswhichare likely tobedamaged.Theoperatorshavebeguntousetheseprotectorsinrelevantareasduringassem‐bly.Onemonthlater,qualityrecordsindicatedthatcutsandtearsindoorsealcausedbyin‐strumentpanelassemblywerepreventedbytheratioof100%.
Thesolutionfornoisywindowglassproblemcausingfromholeposition:
Noisywindowglassisaproblemwhichcausesadisturbingnoiseandjoltinthevehicle,whilethewindowismovingupanddown.AccordingtoParetoanalysis,themostimportantreasonwith the third lowestdegree inpriority level is the rivet holeposition.Rivetingprocess inwindowinstallationwere inspected indetailand itwasdetectedthatthedistancebetweenmechanismandtherivetholewastoosmall(2mm).Thisshortdistancecausedthemecha‐nismpartstohittherivetwhichresultedinadisturbingnoiseandjoltinwindows.Asaresultofcorrective/preventiveactivities,thepositionofrivetholewasmovedtoa3mmlowerpo‐sition.Thereforethedistancebecame5mmwhichwassufficientforpreventingthehittingofwindowmechanismparts.Thepreventiveactivitieshaveresultedin100%improvementinthenoiseprobleminonemonth.
Thequality reports indicated that2operatorshave spent48workinghours in amonth todealwithqualityproblemsbeforeimprovements.AfterimplementationofgreyFMEAtechnique,thistimewasreducedto2hourswhichmeanssavingcostby2300Euroinamonthand27600Euroinayear.
5. Conclusion
FMEAiswidelyusedasanefficientdecision‐makingtooltocontrolthestabilityofthemanufac‐turingprocessandtoimproveproductandsystemperformancebydecreasingfailurerate.Alt‐houghthetraditionalFMEA,employingriskprioritynumbers,stabilizeproductionandincreasethemarketcompetitiveness,ithassomelimitationssuchasfailingtoevaluatetherelativerela‐tionshipofeachweightofthoseparameters.InthisstudythelimitationsofFMEAareovercomebyusinganintegratedmethodofgreytheoryandFMEA.First,thepossiblecausesoffailureandtheirdetectionpointsaredeterminedbyFMEA.Second,theprioritiesofthefactors(causes)aredeterminedbyusinggreyRPNvalues.Accordingtotheresultsofcaseapplicationinanautomo‐tivemanufacturing factory, a 96% improvement was achieved for a door seal cuts problemcausedbythedoorstepassembly.A furtherdoorsealcutsproblemcausedbythe instrument
Risk management in automotive manufacturing process based on FMEA and grey relational analysis: A case study
panel assembly was solved completely. As a third improvement, the noisy door window prob-lem, caused by riveting hole position, is prevented by 100 %.
The main advantage of the integrated GRA and FMEA method in this study is the flexibility of assigning weight to each factor in FMEA, providing an effective and consistent methodology to identify weak parts in the component studied. This integrated approach is convenient to deal with risk assessment problems under circumstances where the information is incomplete or uncertain. The processing of linguistic information based on expert knowledge and experience enables a realistic, practical and flexible way to express judgments.
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