TheKeystoCBM/PHMSuccessHowtoavoidthepitfallsandplanforawin

IntroductionMaintainingandincreasingassetperformancewhilereducingthecosttomaintain,repair,andreplaceitisakeypriorityfororganizationsacrossallindustries.Downtimeofmission-criticalassetsresultsinhigherrepaircosts,customerdissatisfaction,andlowerrevenue.Tomitigatethisrisk,manycompaniesareturningtoConditionBasedMaintenance(CBM)programs.PredictiveHealthMaintenance(PHM)isanothertermthatiscommonlyusedtorefertotheseprograms.Whilesomeorganizationsseetremendousresultswithimprovedproductivityandcontrolledcosts,othersspendmillionsofdollarsonlytobedisappointedintheimplementation,datareceived,and,ultimatelythevalue.Whatdifferentiatesthetwo?Inthispaper,wewilltakealookatsomeofthemostcommonissuesandchallengesthatcauseCBMdeploymentstofail.CBMDefinedCBMisthenext-phaseculminationintheevolutionofmaintenanceforstrategicassets.Inearlyyears,itwascommonfororganizationstomaintainalloftheirequipmentwitharuntofailurestrategy.Thisapproachmightbeappropriateforthoseassetsthathavelittletonoimpactonproductionand,importantly,createnosafetyissueswhentheyfail.However,formorestrategicallyimportantassets,organizationsprogressedtoatime-basedpreventativestrategywithscheduledmaintenanceandoverhauls.Whileeffectiveinkeepingassetsoperational,thisapproachincursadditionalcosts.Overallproductivitydropsthroughoutthelifeoftheassetduetothedurationofthesescheduledmaintenanceevents,overhaulandpartsreplacementactually“throwsaway”unusedremainingoperationaltime,sparepartsinventoryincreasesinordertofacilitatethistypeofmaintenancepolicy,andmaintenanceexpensesingeneralincreasewithrecurringscheduledmaintenance.It’sfortheseassetsthatCBMadvanced:fromfailure-ortime-basedtocondition-based.CBMisamaintenancestrategyinwhichassetmanagersactivelysupervisethehealthconditionofassetstoperformmaintenanceontimeandonlywhenneeded.CBMusessensorsthatarepurposefullyandstrategicallyplacedonassetstostreamdatatothatquantifiesthecurrenthealthoftheassetandprovideinsightintoimpendingdegradationinthathealth.Thisapproachprovidesanefficient,cost-effectivemaintenancemethodbasedonthetangible,currentconditionofeachmaintainedasset.Equipmentthatisconsistentlyingoodconditiondoesnotneedtobemaintainedasfrequently.CBManticipatesassetsreachinganageorprofilethatresultsindecreasedefficiencyduetochangesinfailuremodesorfrequency.CBMprogramsseektodecreasethenumberofmaintenanceeventsonassetsovertheirlifecyclewhilereducingtheriskofunplannedfailures.CBMcanprovideenoughleadtimetooptimizeplanneduseofmaintenanceresources.Thegoalistoreduceunnecessarymaintenanceandanticipateschedulingofrequiredmaintenancetocontrollifecyclecostsofmaintenance.

TheThreeLevelsofCBMLevelICBMfocusessolelyonreplacingordelayingscheduledmaintenanceuntiltheasset,orcomponentwithintheasset,signalsthatitistimeformaintenance.Thisisareactiveapproachtoassetmanagementasthereisnoprognosticcapability,andthusnotlargelydifferentfromaruntofailapproach.Thesensorsprovidereal-timeinformationastowhetherornottheassetneedsmaintenancenow.Whilethisapproachoftenhasthebenefitofrecognizingfailurejustbeforeithappensandreducingtheriskofcatastrophic,thisreactivetakeisnotaneffectivemaintenancestrategyformission-criticalassetsanddoesnotsaveoncostsinthelongrun.LevelIICBMbeginstointegrateprognostics.Initsbestform,LevelIIincludesadiagnosticevaluationalongwithamathematicalprognosticelement–predictiveanalytics.Thisapproachenablesassetmanagerstoplanwhenmaintenancewillbeneededbasedonanasset’spredictedsymptoms,andwhattypeofmaintenancewillberequired.LevelIIstartsbringingCBM’spromisetolife:maintenancetakesplaceonlywhenrequiredandthelifecyclecostofmaintenanceactivityandmaterialsiscontrolled.Assetmanagersseereliableadvanceindicationofthemaintenanceneedingtobeperformedsothatmaintenanceplanningschedulescanbeoptimizedandmaintenancematerialscanbeprepared.LevelIIIofCBM,themostcomplex,isconnectedtothesupplychain.Thislevelallowsassetmanagerstofurtheroptimizemaintenanceschedules,performmaintenanceonlywhennecessary,andtorightsizeinventoryholdingsovertimethatsupportassetmaintenance.ThislevelincludesallthebenefitsofCBMLevelII–and,infact,isonlypossibletoachievebybuildinguponamatureLevelIIprogram.LevelIIICBMaddstheabilitytooptimizesupplychainsandwarehousingofsparerepairpartsacrossamulti-tieredsupportinfrastructure.Thecostsavingsaloneinright-sizingmaintenanceinventoryfortheentireorganizationdelivershighROI:assetmanagerscanvastlyreducetheamountofinventorytheytraditionallykeeponhand,bothforlocaloperationsandfororganization-widesupport,whentheyhaveenoughleadtimetogetrepairpartswheretheyareneeded,whentheyareneeded.Thisrepresentsatruejust-in-timestrategyrealized.

TheChallengesofCBMThenumberoffailedorsuccessfulCBMimplementationsisunknownandsoistheresultingROIs,yetmostassetmanagersarefamiliarwithoneormoreCBMprojectsthateitherfailedorvastlymissedtheirtargetROI.HerearecommonpitfallsthatderailCBMprograms:PoorsensorimplementationAssensorsandthedatatheytransmitarethecoreofanyCBMprogram,poorimplementationofthesesensorswillperforceinevitablywreakhavocontheinitiative.Whataffectsimplementation?PartialCBMcoverage.WhileCBMprogramscanofferhigherfidelityinformationaboutthehealthofaspecificassetorcomponent,inthemajorityoforganizations,CBMcoversjustasmallportionofassetcomponents–andforthosethatarecovered,onlyspecificfailuremodesaretargetedformonitoring.Implementingacompletesensorprocessingsolution,onecapableofaccuratelyrepresentingthecompleteassethealth,ischallenging.EveninthemostcomprehensiveCBMprograms,onlybetween2%and20%ofthecomponentsinstalledonassetsareequippedforCBM.Dysfunctionalsensorsandpoorsensorplacement.AsCBMdependsonsensordata,theplacementandeffectivenessofthesensor,whetherembeddedortemporary,playsadecidingroleinanydeployment’ssuccess.Ingeneral,therevenue-generating,mission-criticalassetsthatcompaniestargetforCBMareinmotion,andinmanycases,operateinharshenvironments.Asaresult,oftentimestheplacementofthesensorcanbesuboptimalorineffective.Thisisespeciallythecaseofembeddedsensorsthatrequiresignificantdesignattentiontoprovidethenecessaryinformationforaccurate,ongoingassethealthassessments,aswellasfutureprognosticsonimpendingassethealthproblems.SensortechnologyandrobustnessaswellasphysicallimitationsofsensorplacementareallsignificantfactorsthatcanlimitthesuccessofCBMprograms.FalsePositives&FalseNegatives.Healthmonitoringsensorscanproducefalse-positiveindicationsfromtimetotimethatcauseanassettoberemovedfromoperationformaintenancewhenmaintenanceisnotactuallyneeded.Incasesofpoorsensorimplementationorfaultyanalytics,therateoffalsepositivesmaybehighenoughtocauseanunacceptablelevelofunproductivemaintenanceeventsandincreasedmaintenancecosts.FalseNegativesarejustasserious:failuresaremissedproducingafalsesenseofsecuritythatcouldreturnanorganizationtotheunacceptableruntofailuremodel—whichcouldbedrasticallymoreexpensiveinmultipleways(damagepartsandassetsbeyondrepair,increasedowntimeforrepair,etc.)WorkingwithaCBMprognosticsvendorthatcandetect,accuratelyaccountfor,andcompensatefortheseFalsePositivesandFalseNegativesiskeytoanyCBMimplementation.

LackofaholisticapproachJustasCBMmustbedeployedacrossamaterialsegmentofanorganization’sassetstobeeffective,theCBMprogramitselfmustbepartofacomprehensiveapproachtohavetheintendedimpact.Unfortunately,manyorganizationsviewCBMasastand-aloneinitiative,andsuffertheconsequences.Reactivityleadstoinitiativeabandonment.ManycompaniesstartwithCBMLevelI,butasreviewedabove,thisisanineffectivemaintenancestrategyduetoitsreactiveapproach.Itprovideslittleinsightonwhentoperformmaintenanceuntilbreakdown.Lackingreliablewarningofimpendingmaintenanceneeds,requiredrepairpartsarestillstockedasifthecompanywereutilizingatime-basedmaintenancestrategy.TheonlyrealizedbenefitstothisCBMstrategyisthathopefullythenumberofmaintenanceeventsdemandedbytheassetislessened.SeeingnoappreciablebenefitfromCBM,theorganizationabandonstheinitiativeentirely,ratherthanmovingontomoreproactivelevel.Dataquality.ForCBMtobemosteffective,itneedstobepartofanoverallstrategythatleveragesdatafrommultiplesystems,suchasmaintenancedatasystems(CMMS),AssetManagement,ERP,SupplyChain,amongothers.Assuch,managingdataqualityshouldbepartoftheoverallorganizationalapproachandconsideredearlyintheproductlifecycleaswellasmaintainedconsistentlythroughouttheentireprocess.Thisisdoneusingcross-functional,collaborativemethodssothattheinformationobtainedinonesystemorlifecyclestageisavailabletorelevantprocessesinotherlifecyclestages.Informationmustbevisiblethroughoutanorganizationtoensurethatanyandalldecisionsrequiringqualitydataareinformedinatimely,efficient,andaccuratefashion.Aspartofanoverallstrategy,CBMcanhelptheorganizationoptimizeitsinventorystrategy,seamlesslyallowingittotakeappropriateactionfromtheCBMdata.Inessence,CBMmayseemreactiveduetothefactthatafailurecanstilloccur,butitisintruthproactivebecauseanalyzingthedataallowsassetmanagerstoanticipatethefailureaheadoftimemoreaccurately,thereforeadjustingsolutionsmorequickly.WhenCBMisseenasanindependentinitiativeseparatefromtheothersystemsthatdrivetheorganization,itcannotdelivertoexpectations.Hence,companieseitherneverescapefromatime-basedscheduledmaintenancestrategy,orquicklydefaultbacktoit,astheyseenobenefitfromtheirforayintoCBM.WithoutreachingLevelsIIandIII,mostCBMprogramslackthesophisticationtoforecastassethealth,includingfutureperformance,assetmaintenance,andassetrepairparts.Asaresult,maintenancebecomesreactiveratherthanproactive.DataOverloadIfimplementedcorrectly,aCBMprogramgeneratesahighlevelofdata.WhilethisdataholdsthekeytounlockingthepotentialinCBM,italsobringsitsownchallenges.Fortunately,thesetendtobebigdataandpredictiveanalyticsissuesthatcanbeaddressedwiththerightmixofskillsandsoftware.

DataCollectionandStorage.Thereexistenormouschallengesaroundtherawdatageneratedfromthesensorsthemselves.Collectionandstorageofsuchlargeamountsofdataintoabigdatarepositoryrequirescoordinationandasophisticatedbackend.Withoutaddressingthispartofthedatacollectionequation,theorganizationisnotabletoleveragethefullsetofdataforuseindecisionmaking.Takingactiononthedata.OrganizationsthathavemadeitthisfarhavemostlikelystartedseeingsomethebenefitsoftheirCBMimplementation:amuchbetterviewoftheirassets,theircurrenthealth,andsomelevelofvisibilityonupcomingmaintenance.However,someorganizationsfailtotakethenextstepbyfullyleveragingtheirdataforaccuratereliabilityanalysisandfailureprediction.Manyexistingsensorsprovidenodirectionality(trending)information.Thelackofdirectionality,orabilitytoisolateachangeinsensorreadingstoaparticularcomponent,allowsforuncertaintyinthefeedback.So,althoughtheorganizationhasseensomebenefitfromCBM,itisnotyetinapositiontomaximizethepotentialandavoidproductivitylossalongwiththecostsassociatedwithit.Predictiveanalyticscanuncoversavingsusingawidevarietyofdatasources.Sensordatashouldbemarriedwithothersystemdatasuchashistoricalmaintenanceandoperationaldatatobeintelligentlymonitored,processed,andminedforvaliddiagnostics,prognostics,andpredictions.ThecapabilityoftheCBMprognosticstoconsume,clean,andharmonizeallthedataiskeytoproducinghigh-fidelityprognostics.FaultyAnalytics.Organizationsmustconsiderwhattypeofapproachisbestsuitedforthemtopredicttheirfuturemaintenanceneeds.Whilemanyanalyticssolutionprovidersclaimtoprovidepredictiveanalytics,theirmethodscanvarygreatlyandeachtendstofocusonaverynarrowsubsetofapproachestoanalytics.Organizationsshouldconsiderthebreadthofmethodologiessupportedwhenassessingsolutions.Evenwithinthesameindustry,differentorganizationswillrequirevariedapproachestoassessingsensorcapabilitiesanddevelopingadvanceddetection.CBMtechnologymustbecustomizedtodeliverdesiredresults.SuccessfulCBMisrarelycarriedoutwithanoff-the-shelfsolution.Ananalyticssolutionthatsupportsmultiple,customizedapproacheshasamuchhigherprobabilityofsuccess.FororganizationslookingbeyondCBMLevelIIprognosticsandhaveaneyeonCBMLevelIIIforsupplychainintegration,thereisanadditionalaspecttoconsider.Theyshouldconsiderananalyticsplatformthatgoesbeyondoverly-simplifiedpredictiveanalyticsthatdeterminefuturemetricssolelyonhistoricaldataandmoreadvanced,accurateapproachesthatleveragesimulationtoallowassetmanagerstocombinedatafrompasteventswithanticipatedfutureeventstomakethedecisionsthatbestimproveproductionandusagegoals.ThePathForwardOrganizationsconsideringaCBMimplementationmustdosoaspartofalargerholisticapproachtotheirassetmanagementsystemsandprocesses.Thisincludeseverythingfromhow

dataisgathered,dataintegrityacrosssystems,andhowtheresultingdataisleveragedtodeliverbusinessvalue.ThislastpartisthekeytoensuringthatorganizationscanseethepromiseandROIofCBM,insteadoffindingthemselveswithterabytesofdataandnorealanswers.Makingthebestdecisionsaboutthefutureofassetsrequirestherighttools.Predictiveanalyticstechnologycanproducedetailedmodelsofthefuturebasedonasetofever-growingdatafromtheassets.Thevarietyofthesemodelsandsimulationsisvirtuallylimitless,allowinganassetmanagertoseechangesinthebottomlineasfactorsintheirasset’slifecyclearemanipulated.OnceanorganizationhassuccessfullyimplementeditsCBMprogram,itcantakethenextstepintocomprehensiveassetlifecyclemodeling,inwhichmatureCBMprogramsaddgreatervaluethroughahigherfidelitycharacterizationofassetandcomponentfuturebehavior.Lifecyclemodelingsimplifiesthedatamanagementprocessbyconstantlyupdatingoperatingdata,ensuringmaintenancestrategiesandotherkeyperformanceindicatorsarealwaysuptodate.Unliketraditionalforecastingmethodsthatmakepredictionsbytrendinghistoricalgenericprocurementdatawithnoreferencetocausefutureoperations,andaging,lifecyclemodelingenablesplanningbasedonthecurrentconditionofindividualassets,componentaging,plannedusage,intendedoperatingenvironment,logisticsnetworkalreadyinplace,andothersupportsystems.Thisroot-causedrivenplanning,coupledwithnear-realitymodelingofassets,andsupportinfrastructuresenablesahighlevelofaccuracyyearsordecadesintothefuture.LifecyclemodelingcapabilitiesbuildvalueontopofCBMsystemstoformamorecomprehensiveapproachthatbringsgreatervaluewhileseamlesslyintegratingwithmatureCBMprograms.ConclusionIfimplementedcorrectly,CBMprogramscanhelporganizationsincreaseproductivitywhilecontrollingthecostandtimeofmaintenanceofmission-criticalassetswithoutimpactontheiroperations,users,orcustomers.However,organizationsarecounseledtounderstandgoinginwhatthechallengeswillbe,andsetupthemselvesandtheirCBMinitiativesforsuccess.Dataaboutassetsisonlyactionableifit'sunderstandableandusable.Infact,insomeinstances,aCBMbasedmaintenancestrategycanincreasecapitalexpendituresinitially,butthepayoffofimplementingsuchastrategyoverthelifecycleofanassetgrowsovertimewithmoreproductivity,squeezingthemostoutofassetsandequipment.Awell-designedCBMprogramovercomesinitialcoststhroughimprovedmaintenanceandsupplystrategiestomaximizeassetoperationaltimeandprofit.EnsuringthattheCBMprogramispartofacomprehensiveapproach–onethatputsCBMatthecenterofadatacapturestrategy–isagoodplacetostart.Followingthatupwiththetoolstomanage,understand,andleveragethedataiscritical.

ClockworkSolutionsisagloballeaderinpredictiveanalyticssolutionsfororganizationswithstrategic,high-valuefleetsandassetswithafocusonimprovingassetavailability,reducingpartsinventory,andcontrollingmaintenancecosts.Withmorethan30yearsofexperiencedeliveringsolutionsforFortune500enterprisesacrossmultipleindustriesandmilitaryorganizationsworldwide,ClockworkdeliversafullrangeofsolutionsrangingfromExtract-Transform-Load(ETL)datacapabilitiesthroughtoConditionBasedMaintenance,PredictiveHealthManagement,andfullLifecycleManagement.

Formoreinformationvisitwww.clockwork-solutions.com