DHL：物流业数字孪生报告

ContentsPage1/39ADHLADHLperspectiveontheimpactofdigitaltwinsonthelogisticsindustryContactusPage2/39UnderstandingDigitalTwinsDigitalTwinsinLogistics211.1TheDigitalTwinComesofAge43.1Packaging&ContainerDigitalTwins221.2WhatMakesaDigitalTwin?63.2DigitalTwinsofShipments231.3UnderlyingTechnologiesEnablingDigitalTwins73.3DigitalTwinsofWarehousesandDistributionCenters231.4HowDigitalTwinsCreateValue83.4DigitalTwinsofLogisticsInfrastructure261.5TheDigitalTwinThroughtheProductLifecycle93.5DigitalTwinsofGlobalLogisticsNetworks271.6ChallengesinApplyingDigitalTwins104LogisticsImplicationsofImplementingDigitalTwins28DigitalTwinsAcrossIndustries124.1InboundtoManufacturing292.1DigitalTwinsinManufacturing134.2In-plantLogistics302.2DigitalTwinsinMaterialsScience144.3AftermarketLogistics322.3DigitalTwinsinIndustrialProducts154.4OrchestratingtheSupplyChain322.4DigitalTwinsinLifeSciencesandHealthcare16Conclusion&Outlook342.5DigitalTwinsinInfrastructureandUrbanPlanning17Sources362.6DigitalTwinsin2.6DigitalTwinsintheEnergySector192.7DigitalTwinsinConsumer,RetailandE-commerce20RecommendedReading39ContactusContentsPage3/39Forcenturies,peoplehaveusedpicturesandmodelstohelpthemtacklecomplexproblems.Greatbuildingsfirsttookshapeonthearchitect’sdrawingboard.Classiccarswereshapedinwoodandclay.Overtime,ourmodelingcapabilitieshavebecomemoresophisticated.Computershavereplacedpencils.3Dcomputermodelshavereplaced2Ddrawings.Advancedmodelingsystemscansimulatetheoperationandbehaviorofaproductaswellasitsgeometry.Untilrecently,however,thereremainedanunbridgeddividebetweenmodelandreality.Notwomanufacturedobjectsareevertrulyidentical,eveniftheyhavebeenbuiltfromthesamesetofdrawings.Computermodelsofmachinesdon’tevolveaspartswearoutandarereplaced,asfatigueaccumulatesinstructures,orasownersmakemodificationstosuittheirchangingneeds.Thatgapisnowstartingtoclose.Fueledbydevelopmentsintheinternetofthings(IoT),bigdata,artificialintelligence,cloudcomputing,anddigitalrealitytechnologies,therecentarrivalofdigitaltwinsheraldsatippingpointwherethephysicalanddigitalworldscanbemanagedasone,andwecaninteractwiththedigitalcounterpartofphysicalthingsmuchlikewewouldthethingsthemselves,evenin3Dspacearoundus.Ledbytheengineering,manufacturing,automotive,andenergyindustriesinparticular,digitaltwinsarealreadycreatingnewvalue.Theyarehelpingcompaniestodesign,visualize,monitor,manage,andmaintaintheirassetsmoreeffectively.Andtheyareunlockingnewbusinessopportunitiesliketheprovisionofadvancedservicesandthegenerationofvaluableinsightfromoperationaldata.Aslogisticsprofessionals,wehavebeenthinkingabouthowdigitaltwinswillchangetraditionalsupplychains,andhowthelogisticssectormightembracedigitaltwinstoimproveitsownprocesses.Ourobjectiveinwritingthisreportistoshareourfindingsandtohelpyouanswerthefollowingkeyquestions:■Whatisadigitaltwinandwhatdoesitmeanformyorganization?■Whatbest-practiceexamplesfromotherindustriescanbeappliedtologistics?■Howwillmysupplychainchangebecauseofdigitaltwins?Lookingahead,webelievethattheadoptionofdigitaltwinsacrossindustrieswilldrivebetterdecisionmakinginthephysicalworld.That,inturn,willdrivesignificantchangesintheoperationofsupplychainsandlogisticsprocesses.Inthelogisticsindustryitself,digitaltwinswillextendthebenefitsofIoTalreadybeingappliedtoday.Theywillbringdeeperinsightintotheplanning,design,operation,andoptimizationofsupplychains,fromindividualassetsandshipmentstoentireglobalsupplynetworks.Wethinktherehasneverbeenamoreexcitingtimeforindustriesandlogisticianstoworktogethertoleveragethefullpotentialofdigitaltwins.OnbehalfofusallatDHL,welookforwardtocollaboratingwithyouinthisexcitingandpotentiallytransformativefield.SeniorVicePresidentGlobalHeadofInnovation&CommercialDevelopment,DHLVicePresidentInnovation&TrendResearch,DHLContactusContactusContentsPage4/39Formanyyears,scientistsandengineershavecreatedmathematicalmodelsofreal-worldobjectsandovertimethesemodelshavebecomeincreasinglysophisticated.Todaytheevolutionofsensorsandnetworktechnologiesenablesustolinkpreviouslyofflinephysicalassetstodigitalmodels.Inthisway,changesexperiencedbythephysicalobjectarereflectedinthedigitalmodel,andinsightsderivedfromthemodelallowdecisionstobemadeaboutthephysicalobject,whichcanalsobecontrolledwithunprecedentedprecision.Page5/392015SystemDesignAdvancedsimulationbecomescentraltocomplex,multi-disciplinarysystemdesignandengineering.Anenhancedrangeofsimulationapplicationsenablesmodel-basedsystemsengineering.196019852000DigitalPage5/392015SystemDesignAdvancedsimulationbecomescentraltocomplex,multi-disciplinarysystemdesignandengineering.Anenhancedrangeofsimulationapplicationsenablesmodel-basedsystemsengineering.196019852000DigitalTwinsAvirtualmodel(onceonlyusedinsimulation)isseamlesslyandcontinuallyupdatedacrosstheentirelifecycleofaproduct,wherethevirtualmodelsupportsoperationofthephysicalproductthroughdirectlinkageandrepresentationofitsoperationaldata.ApplicationsComputer-drivenSimulationtoolsdropinprice,broadeningavailabilityandapplicabilitytomanyengineeringSimulationemergesinspecificandhighlyspecializedfieldsforexpertuseonly.anddesignfields.AtfirstthecomplexityandcostinvolvedinbuildingdigitaltwinslimitedtheirusetoAtfirstthecomplexityandcostinvolvedinbuildingdigitaltwinslimitedtheirusetotheaerospaceanddefensesectors(seethetimelineinfigure1)asthephysicalobjectswerehigh-value,mission-criticalassetsoperatinginchallengingenvironmentsthatcouldbenefitfromsimulation.Relativelyfewotherapplicationssharedthesamecombinationofhigh-valueassetsandinaccessibleoperatingconditionstojustifytheinvestment.Thatsituationischangingrapidly.Today,aspartoftheirnormalbusinessprocesses,companiesareusingtheirownproductstogeneratemuchofthedatarequiredtobuildadigitaltwin;computer-aideddesign(CAD)andsimulationtoolsarecommonlyusedinproductdevelopment,forexample.Manyproducts,includingconsumerelectronics,automobiles,andevenhouseholdappliancesnowincludesensorsanddatacommunicationcapabilitiesasstandardfeatures.s2018Digitaltwinsinproductportfoliosofallmajorsoftwareandindustrialcompanies1983-2001AutoCADbecomesadefactotoolinnearlyallengineeringanddesign2015GEdigitalwindfarminitiative2011NASA&USAFpaperson1982AutoCADisborndigitaltwins197720172002GartnerlistsdigitaltwinsasFlightsimulatorswith•Dr.Grieves‘conceptofaatop10techtrendcomputersimulationdigitaltwinemerges•McLarenF1digitaltwintechnologyforproductdevelop-mentandperformanceprediction1970NASApairingtechnologyonApollo13missionsincethestartofthe21stcentury,theapproachisnowreachingatippingpointwherewidespreadadoptionislikelyinthenearfuture.That’sbecauseanumberofkeyenablingtechnologieshavereachedthelevelofmaturitynecessarytosupporttheuseofdigitaltwinsforenterpriseapplications.Thosetechnologiesincludelow-costdatastorageandcomputingpower,theavailabilityofrobust,high-speedwiredandwirelessnetworks,andcheap,reliablesensors.Figure1Figure1:TheevolutionofdigitaltwinsFigure1Figure1:Theevolutionofdigitaltwins.Source:DHLFigure2:GEhascreatedadigitaltwinoftheBoeing777enginespecificallyforengineblademaintenance.Source:GEadirectphysicalreplicatosupporttheoperationandmaintenanceofanassethasalonghistory.NASApioneeredapairingapproachduringtheearlyyearsofspaceexploration.WhentheApollo13spacecraftsufferedsignificantdamageonamissiontothemoonin1970,NASAengineerswereabletotestandrefinepotentialrecoverystrategiesinapairedmoduleonearthbeforeissuinginstructionstothestrickencrew.Tothisday,pairing-nowusingdigitalmodels-remainsacentralpartoftheUSspaceagency’sstrategyformanagingspacemissions.Figure2ContactusPage6/39RepresentsauniquephysicalassetAssociatedwithasingle,specificinstancePage6/39RepresentsauniquephysicalassetAssociatedwithasingle,specificinstanceofaphysicalassetContinuouslycollectsdata(throughsensors)Continuouslyconnectedtothephysicalasset,updatingitselfwithanychangetotheasset‘sstate,condition,orcontextProvidesvaluethroughvisualization,analysis,prediction,oroptimization■Adigitaltwinisunique,■Adigitaltwinisunique,associatedwithasingle,specificinstanceofthething.■Adigitaltwinisconnectedtothething,updatingitselfinresponsetoknownchangestothethingsstate,condition,orcontext.■Adigitaltwinprovidesvaluethroughvisualization,analysis,prediction,oroptimization.Therangeofpotentialdigitaltwinapplicationsmeansthateventhesedefiningattributescanblurinsomesituations.Adigitaltwinmayexistbeforeitsphysicalcounterpartismade,forexample,andpersistlongafterthethinghasreachedtheendofitslife.Asinglethingcanhavemorethanonetwin,withdifferentmodelsbuiltplayerssuchasCityzenith,NavVis,andSWIM.AIdevelopingtheirownofferingstailoredtoparticularnichesandusecases.Inpracticewithsomanydifferentapplicationsandstakeholdersinvolved,thereisnoperfectconsensusonwhatconstitutesadigitaltwin.Asourexamplesshowveryclearlylaterinthisreport,digitaltwinscomeinmanyformswithmanydifferentattributesItcanbetemptingforcompaniestoridethewaveofinterestintheapproachbyattachinga‘digitaltwinlabel’toarangeofpre-existing3Dmodeling,simulation,andasset-trackingtechnologies.Butthisshortsellsthecomplexityofatruedigitaltwin.mmentatorsagreeonkeyracteristicssharedbythemajorityofesthathelptoedigitaltwinsfromothermputermodelorsimulationare■Adigitaltwinisvirtualmodelofareal‘thing’.■Adigitaltwinsimulatesboththephysicalstateandbehaviourofthething.nvirtualrepresentationofgrows,sotoodoesthenumberoftechnologyproviderstosupplythisdemand.Industryresearchersexpectthedigitaltwinsmarkettogrowatanannualrateofmorethan38percentoverthenextfewyears,passingtheUSD$26billionpointby2025.Figure3fordifferentusersandusecases,suchFigure3fordifferentusersandusecases,suchaswhat-ifscenarioplanningorpredictingthebehaviorofthethingunderfutureoperatingFigure3:Characteristicsofadigitaltwin.Source:DHLconditions.Forexample,theownersoffactories,hospitals,andofficesmaycreatemultiplemodelsofanexistingfacilityastheyevaluatetheimpactofchangesinlayoutoroperatingprocesses.onthispotentiallylucrativespace.Thebroadrangeofunderlyingtechnologiesrequiredbydigitaltwinsencouragesmanycompaniestoenterthemarket,includinglargeenterprisetechnologycompaniessuchasSAP,Microsoft,andIBM.Theseorganizationsarewellpositionedtoapplytheircloudcomputing,artificialintelligence,andenterprisesecuritycapabilitiestothecreationofdigitaltwinsolutions.Inaddition,makersofautomationsystemsandindustrialequipmentsuchasGE,Siemens,andHoneywellareusheringinaneweraofindustrialmachineryandservicesbuiltondigitaltwins.Alsocompaniesofferingproductlifecyclemanagement(PLM)suchasPTCandDassaultSystèmesareembracingdigitaltwinsasafundamentalcoretechnologytomanageproductdevelopmentfrominitialconcepttoendoflife.Digitaltwinopportunitiesarealsoattractingtheattentionofstart-ups,withContactusPage7/39Figure4:Technologiesbehinddigitaltwins.Source:DHLContentsPage7/39Figure4:Technologiesbehinddigitaltwins.Source:DHLFivetechnologytrendsaredevelopinginacomplementaryFivetechnologytrendsaredevelopinginacomplementarywaytoenabledigitaltwins,namelytheinternetofthings,cloudcomputing,APIsandopenstandards,artificialintelligence,anddigitalrealitytechnologies.growthofIoTisoneimportantfactordrivingtheadoptionofdigitaltwins.IoTtechnologiesmakedigitaltwinspossiblebecauseitisnowtechnicallyandeconomicallyfeasibletocollectlargevolumesofdatafromawiderrangeofobjectsthanbefore.CompaniesoftenunderestimatethecomplexityandvolumeofdatageneratedbyIoTproductsandplatforms,requiringtoolstohelpthemmanageandmakesenseofallthedatatheyarenowcollecting.Adigitaltwinisoftenanidealwaytostructure,access,andanalyzecomplexproduct-relateddata.Digitaltwinsrelyonahostofunderlyingtechnologiesthatareonlynowreachingthepointwheretheycanbeappliedreliably,costeffectively,andatscale.ofdigitaltwinsRendersthespatialmodelandvisualizationofthedigitaltwin,providingthemediumforcolla-borationandinteractionwithit.Allowsstorageandpro-cessingoflargevolumesofmachinedatafromtheassetanditsdigitaltwininrealtime.Providethenecessarytoolstoextract,share,andharmonizedatafrommultiplesystemsthatcontributetoasingledigitaltwin.High-precisionsensorsenablecontinuouscollectionofmachinedata,state,andcon-ditionfromthephysicalassettoitsdigitaltwininrealtimeviawirelessnetworks.Leverageshistoricalandreal-timedatapairedwithmachinelearningframeworkstomakepredictionsaboutfuturescenariosoreventsthatwilloccurwithinthecontextoftheasset.Figure4CloudComputing.Developing,maintaining,andusingdigitaltwinsisacompute-andstorage-intensiveendeavor.Thankstothecontinuallyfallingcostofprocessingpowerandstorage,largedatacenternetworkswithaccessprovidedviasoftware-as-a-service(SaaS)solutionsnowenablecompaniestoacquireexactlythecomputingresourcestheyneed,whentheyneedthem,prietary-by-designsimulationtoolsandfactoryautomationplatformsareincreasinglybecomingathingofthepast.Technologycompaniescreatedandprotectedtheirowndatamodels,requiringintensive,ground-upsoftwaredevelopmenttobuildinfrastructurefromscratchforeachnewpanieshavebuiltdigitaltwinsateveryscalefromatomstoplanets.Thesmallestdigitaltwincanrepresentthebehaviorofspecificmaterials,chemicalreactions,ordruginteractions.Attheotherextreme,alargedigitaltwincanmodelentiremetropolitancities.Themajorityofdigitaltwinssitsomewhereinthemiddle,withmostcurrentapplicationsaimedatmorehuman-scaleproblems,especiallythemodelingofproductsandtheirmanufacturingprocesses.Onenotabletrendisthedevelopmentoflarger,morecomplexdigitaltwinsasorganizationsevolvefrommodelingsingleproductsormachinestomodelingcompleteproductionlines,factories,andfacilities.Similarly,effortsareunderwaytocreatedigitaltwinsofentirecitiesorevenofnational-scaleenergyinfrastructureandtransportnetworks.TheUKisevenworkingonplanstodevelopadigitaltwinofthewholecountrytoserveasarepositoryformultiplesourcesofdatarelatedtobuildings,infrastructure,andutilities.ContactusContentsNowtheavailabilityofopenstandardsandpublicapplicationprogramminginterfaces(APIs)hasdramaticallystreamlinedsharinganddataexchange,makingitpossibleforuserstocombinedatafrommultiplesystemsandtoolsquicklyandreliably.improvementsinthepowerandusabilityofadvancedanalyticaltoolshavetransformedthewaycompaniesextractusefulinsightsfrombig,complexdatasets.Machinelearningframeworksareenablingthedevelopmentofsystemsthatcanmakedecisionsautonomouslyaswellaspredictionsaboutfutureconditionsbasedonhistoricalandreal-timedata.ordertoleverage,consume,andeffectivelytakeactionontheinsightsgeneratedbyadigitaltwin,itmustberenderedeitheronascreen(2D)orinphysicalspace(3D).Todate,mostdigitaltwinshavebeenrenderedintwo-dimensionalspace,astheconventionalcomputingnormsoftodaylimitustodisplaysonmonitors,laptops,andotherscreens.Butincreasingly,augmentedrealityisenablingustodisplaydigitalcontentin3D.Inaddition,mixedrealityallowsustointeractwithdigitalcontentinourexistingphysicalenvironment.Andvirtualrealityallowsustocreateentirelynewenvironmentstorenderdigitaltwinsinahighlyimmersiveway,creatingtherichestconsumptionofandinteractionwiththeinformation.Whiletheabovetechnologies–IoT,cloudcomputing,APIs,andartificialintelligence–providetheunderlyingsensingandprocessinginfrastructurerequiredtocreateadigitaltwin,augmented,mixed,andvirtualrealityarethetoolsforvisualizingdigitaltwinsandmakingthemrealtotheuser.Digitaltwinscanbeusedindifferentwaystoaddvaluetoaproduct,process,user,ororganization.Thevalueavailable,andtheinvestmentrequiredtocaptureit,arehighlyapplicationdependent.Mostfallintooneormoreofthefollowingbroadcategories.immediatelyvisualizethestatusofanassetviaitsdigitaltwinisvaluablewhenthoseassetsareremoteordangerous–examplesincludespacecraft,offshorewindturbines,powerstations,andmanufacturer-ownedmachinesoperatingincustomerplants.Digitaltwinsmakeinformationmoreaccessibleandeasiertointerpretfromadistance.nsthatincorporatesimulationtechnologiescanprovidedatathatisimpossibletomeasuredirectlyonthephysicalobject–forexampleinformationgeneratedinsideanobject.Thiscanbeusedasatroubleshootingtoolforexistingproductsandcanhelptooptimizetheperformanceofsubsequentproductgenerations.DiagnosticValue.Digitaltwinscanincludediagnosticsystemsthatusemeasuredorderiveddatatosuggestthemostprobablerootcausesofspecificstatesorbehaviors.Thesesystemscanbeimplementedintheformofexplicitrulesbasedoncompanyknow-how,ortheymayleverageanalyticsandmachinelearningapproachestoderiverelationshipsbasedonhistoricaldata.ikelyfuturestateofthephysicalmodelcanbepredictedusingadigitaltwinmodel.OneexampleisGE’suseofdigitaltwinsinwindfarmstopredictpoweroutput,asdepictedinfigure5.Themostsophisticateddigitaltwinsdomorethanmerelypredicttheissuethatmayoccur;theyalsoproposePage8/39Figure5Figure5:GE’sdigitalFigure5:GE’sdigitalwindfarmprojectleveragesdigitaltwintechnologytomakepredictionsonpoweroutput.Source:HarvardBusinessReviewtwinswillplayasignificantroleinthedevelopmentoffuturesmartfactoriescapableofmakingautonomousdecisionsaboutwhattomake,whenandhow,inordertomaximizecustomersatisfaction–andprofitability.Earlyadoptersofdigitaltwinscommonlyreportbenefitsinthreeareas:■Data-drivendecisionmakingandcollaboration■Streamlinedbusinessprocesses■NewbusinessmodelsContactusContentsBecauseeachdigitaltwinpresentsasinglevisualizationtokeydecisionmakers,itprovidesasinglesourceoftruthforanassetthatdrivesstakeholdercollaborationtoresolveproblemsexpediently.Digitaltwinscanbeusedtoautomatetediouserror-proneactivitiessuchasinspections,testing,analysis,andreporting.Thisfreesteamstofocusonhigher-valueactivities.Digitaltwinsareamajordriverofproduct-as-a-servicebusinessmodelsorservitization–thisiswhencompaniesabandontheone-timesaleofaproducttoinsteadselloutcomesbymanagingthefulloperationoftheassetthroughoutitslifecycle.Digitaltwinsallowmanufacturerstomonitor,diagnose,andoptimizetheirassetsremotely,helpingtoimproveavailabilityandreduceservicecosts.Sincetheirinception,digitaltwinshavebeencloselyassociatedwithproductlifecyclemanagement(PLM).Digitaltwinsarenowusedthroughoutthefullproductlifecycle,withaproduct’stwinemergingduringthedevelopmentprocessandevolvingtosupportdifferentbusinessneedsasaproductprogressesFigure6throughdesign,manufacturing,launch,distribution,operation,servicing,anddecommissioning.ProductDevelopment.Datafromthedigitaltwinsofpreviousproductscanbeusedtorefinetherequirementsandspecificationsoffutureones.Virtualprototypingusing3Dmodelingandsimulationallowsfasterdesigniterationsandreducestheneedforphysicaltestsasdepictedinfigure6.Duringthedesignphase,testswithdigitaltwinscandetectclashesbetweencomponents,assessergonomics,andsimulateproductbehaviorinawidevarietyofenvironments.Togetherthesemeasureshelptoreducedevelopmentcosts,acceleratetimetomarket,andimprovethereliabilityofthefinalproduct.Production.Digitaltwinsfacilitatecollaborationbetweencross-functionalteamsinthemanufacturingprocess.Theycanbeusedtoclarifyspecificationswithsuppliersandallowdesignstobeoptimizedformanufacturingandshipping.Iftheorganizationmanufacturesanewdigitaltwinwitheveryproductitmakes,eachmodelwillincorporatedataonthespecificcomponentsandmaterialsusedintheproduct,configurationoptionsselectedbyendcustomers,andprocessconditionsexperiencedduringproduction.Digitaltwinsofproductionlinesasillustratedinfigure7allowlayouts,processes,andmaterialflowstobetestedandoptimizedbeforeanewmanufacturingfacilityiscommissioned.Page9/39bySamGeorge,Director,AzureInternetofThings,Microsoftstateoftheirmodels,aswellassimulatepotentialchanges.AdigitaltwinhelpsbringvaluetothevariousIoTdataintothemodelwhichfitstheirdomain,andwithoutittheIoTdatahaslessvalue.Itiscriticaltoconnecttothedevicesandsensorsinthephysicalworldtoprovidereal-timeandoperationalizeddata–notjusttheidealizedstateofthesystem.Thisenablescustomerstotakeadvantageofreplicasalivewithdata.Byaddingartificialintelligence(AI)tothemix,customerscanidentifytrends,forecastthefuture,optimizeandsimulatechanges.Thisenablescustomerstosavemoneyandimproveplanning,products,andcustomerrelationships.We’velearnedthatmostdigitaltransformationeffortsbenefitfrom