mckinsey -从成本中心到竞争优势：利用人工智能升级逆向物流 From cost center to competitive advantage Modernizing reverse logistics with AI

February2026

Mcsey

&company

LogisticsPractice

Fromcostcentertocompetitiveadvantage:ModernizingreverselogisticswithAI

ByusingAIandautomationtoredesignreverselogistics,retailerscanconvert$200billioninannualcostsintobusinessvalue.

ThisarticleisacollaborativeeffortbyColleenBaum,JohnMurnane,RyanGavin,andTylerRose,withEliseOdellandSamCowan,representingviewsfromMcKinsey’sLogisticsandRetailPractices.

Consumersreturnednearly$1trillioninmerchandiseintheUnitedStatesin2024—

morethan

doublethetotal

fromjustfouryearsprior.Thatsurgehasforcedretailerstospendanestimated$200billionannuallytorecovervaluefromreturnedgoods,makingreverselogisticsamajorcostcenterforconsumercompanies.Formanyretailers,themostcrucialreturns-relatedquestion

iswhetherareturneditemisstillsellableand,ifso,whereitshouldmovenextinthenetworktomaximizevaluerecovery.

Addressingthischallengeiscomplex.Thesamepracticesthatfuelede-commercegrowth—freereturns,instantrefunds,andminimalfriction—havecementedconsumerexpectationsforhow

returnsshouldwork.Atthesametime,supplychainleaderstellustheyarestillmanagingreverselogisticsthesamewaytheydidduringtheCOVID-19pandemic,withone-size-fits-allreturn

policiesanddecisionsforwheretosendreturnedgoods(seesidebar“Abouttheresearch”).

Thosemeasuresworkedwhenthegoalwassimplytokeepgoodsmoving,buttheycan’tkeeppacewithtoday’sconstantflowofreturnsortheneedforfaster,smarterdecisionsaboutwhereeachitemshouldgonext.

Retailersmustapplythesamerigor,investment,andcross-functionalcoordinationtoreverse

logisticsastheydotoforwardlogistics,treatingreturnsnotasaback-endprocessbutasacorestageoftheproductlifecycle.Inthisarticle,weexplainhowretailerscanuseAIandautomationtomodernizereturn-policydesignanddispositiondecisions,twopersistentpainpointsalongthereturnsjourney.Wethendiscusshowretailerscandesignproductsandprocesseswithresale,

retention,andrecoveryinmindfromthestartandoutlinesixleversthatcanshapethereverse-logisticsmodelofthefuture.Doneright,reverselogisticscanshiftfromagrowingcostcentertoasourceofresilienceandcompetitiveadvantage.

Returnpoliciesdon’thavetobeconsistentacrossconsumergroups

Whendesigningreturnpolicies,retailersfacea“returnsparadox”:Howcantheyoffera

customer-friendlyreturnpolicythatdrivessalesandimprovescustomersatisfactionwithoutincreasingcosts?Consumers,meanwhile,areclearaboutwhattheyexpect:Theywantfree,unlimitedreturnsthatrequirenolabelsorboxesandtheabilitytodropitemsoffanywhere,

anytimeafterpurchase.

Abouttheresearch

The2025McKinseyConsumerReturnsPreferencesSurveywasfieldedinJuly

2025andgarnered850responsesfrom

consumersaged18orolderacrossthe

UnitedStates.Theaimofthesurveywas

tounderstandconsumers’experiences

withandpreferencesforreturninggoodsacrosscategories(includingfashion,

beautyandpersonalcare,housewares,

smallappliances,toys,andofficesupplies).

The2025McKinseyMerchantReturns

SurveywasfieldedinJuly2025and

garnered30responsesfromsupply

chainexecutiveswithvisibilityinto

theircompanies’end-to-endreturns

processes.Theaimofthissurveywastounderstandthechallengessellersfaceinmanagingreturnsandtheopportunitiestheyseeforoptimization.

Last,wedrewonproprietaryshipper

intelligencefromtheColographyGroup,aproviderofdetailedinsightsonthe

transportationandlogisticsindustry.

Colographyconducts30,000to60,000interviewsofshippersacrosstheUnitedStateseachyearthroughitsNational

Surveytogatherinsightsontheoutboundandreverse-parcellogisticsmarkets.

Fromcostcentertocompetitiveadvantage:ModernizingreverselogisticswithAI2

Fromcostcentertocompetitiveadvantage:ModernizingreverselogisticswithAI3

Butconsumersdon’tweigheachofthesefeaturesequally.Whenaskedtoranktheirpreferences,consumerssaytheycaremostaboutreceivingaguaranteedrefund,thecostofthereturn,and

theformofrepayment;theycarelessaboutthenumberofdaystheyhavetoreturnanitem,

visibilityintotheprocess,orconsistentprocessesorplatforms(Exhibit1).Theyalsovalueeaseandconvenienceandaccesstoconvenientdrop-offorpickupoptions,oftenenabledthroughpartnershipswiththirdparties(seesidebar“Improvingretailers’collaborationwithparcel

carriersandthird-partylogisticsproviders”).

Exhibit1

Whenitcomestoreturns,consumerscaremostaboutgettingaguaranteedrefund.

Initiate

return

Preparereturn

Dropo仟/mailreturn

Track

return

Consumerprioritieswhenmakingareturn,1%ofrespondentsselectingasmostinfiuentialfactor

Guaranteedrefund18.0

Returnfee9.6

Refundinoriginalformofpayment6.6

Easeofreturnsprocess6.0

Timetoreceiverefund2.6

Familiar/consistentpolicy2.1

Numberofdaystoreturn1.9

Abilitytochoosereturnmethod

3.9

Abilitytochoosereturnshippingprovider

3.6

Noextrachargeforpacking

materials

3.4

Nothavingtorepackage

item

2.9

Nothavingtoprint

returnlabel2.4

Package/labelincludedwithitem2.4

Familiarprocess/platform1.1

Abilitytoreturnatretailpartnerofshipper4.5

Abilitytohavereturnpickedupathome4.4

Abilitytoreturniteminstore3.2

Abilitytoreturnitematshipper3.2

Proximity/drivetimetodrop-o仟location2.4

24/7accesstodrop-o仟location2.2

Self-serviceoptionsatdrop-o仟1.8

Reliable

estimateforrefund2.2

Onlinetrackingplatform

2.1

Notificationrefund

issued1.9

Notificationreturn

received1.7

Notificationreturnaccepted1.6

Notificationreturnshipped1.2

Securityofpackage1.2

1Question:Fromthechoicesbelow,chooseonefactorthatisthemostinfiuentialandonefactorthatistheleastinfiuentialonyourreturnsexperience.Factorsselectedbyfewerthan2%ofrespondentsnotshown.

Source:McKinseyConsumerReturnsSurvey,Aug2–5,2025(n=844)

McKinsey&Company

Fromcostcentertocompetitiveadvantage:ModernizingreverselogisticswithAI4

Improvingretailers’collaborationwithparcelcarriersandthird-partylogisticsproviders

Weestimatethereverse-logisticsservicesmarkettobeworthupto

$14billion(about$8billioninshipping

and$6billioninprocessing),representingamajor“whitespace”opportunity

forcarriersandthird-partylogisticscompanies(3PLs)tointegratemoredeeplywithretailers.

Still,collaborationbetweensellersand

logisticspartnersremainslimited.Ina

surveyof30supplychainexecutives,

abouthalfsaidtheyview3PLsandparcelcarriersasstrategicpartners,while

theotherhalfseesthemprimarilyas

transactionalserviceprovidersordonotcollaboratewiththematall.Thisdivide

underscorestheopportunityforlogisticsplayerstomoveupthevaluechainand

positionthemselvesasstrategicenablers.

Onewaytodothisisbyofferingpremiumservicesforselectcustomersegments.

Forinstance,carrierscanhelpsellers

offerhigh-valuecustomersupgraded

returnoptions—suchasat-homepickup,package-lessdrop-off,orpriorityin-storelanes—whileimprovingefficiencywith

self-servekiosksandotherinfrastructure.Theycanalsoinvestintechnologyand

flexibledispositionmethodstoenhance

routinganddecision-making,buildingreal-timedatafeedsthattransmitdisposition

instructionsfromsellersandintegrating

thoseinputsintoroutingflows.Finally,

carrierscantrackperformanceandsharekeyreturnsdata—suchascost-per-returnandservice-level-agreementadherence—withretailers.Establishingthese

feedbackloopsenablesbothpartiesto

actoninsightsquickly,drivingmeasurablereductionsincostandcycletime.

Often,whatcustomersvalueinareturnpolicyvariesbasedontheproductcategory.For

example,inoursurvey,fashionshoppersaremostlikelytogothroughwithareturnifitisfree,

butpetsupplyshoppersaremostlikelytogothroughwithareturnifofferedaguaranteedrefund(Exhibit2).Thistypeofdifferentiationopensthedoorforretailerstodesignsmarterreturn

policieswhileselectivelyintroducingfrictionintotheprocess(whichcanhelpdiscouragereturnsfraudand“exploitativebehavior”thatretailersfrequentlyencounter1).

Thegoodnewsisthatmostconsumers(71percent)sayadynamic,product-andcustomer-

specificreturnpolicywouldnotmakethemlesslikelytoshopwitharetaileragain.Thisgives

retailersroomtocreatereturnspoliciesthatsegmentcustomersbasedonprojectedcustomerlifetimevalue(CLTV)andpreviousreturnsbehavior.

Considerahandfuloftypicalcustomersegments:AcustomerwithanaverageCLTVandreturn

frequencymightbeofferedfreereturnswithinstantrefundsintheformofstorecreditwithina

14-dayreturnwindow.Loyalcustomerswithhistoricallyhighordervaluesandlowreturnrates

couldberewardedwithmoregenerousterms,suchasfreereturnsandinstantrefundstoacreditcard.Customerswhoreturnitemstheydidn’tbuyorexploit“keep-the-item”policiescouldbe

placedunderstricterconditions:$10returnfees,storecreditonly,andaseven-dayreturnlimit.

Byestablishingthedataarchitectureneededtointegratecustomer,product,andtransaction

information—whicharobustgovernancestructurewithintheretailorganizationsupports—

companiescanruncontrolledexperimentstomeasurehowdifferentpolicylevers,suchasfees,refundmethods,orreturnwindows,affectCLTVandreturnfrequency.

1ArecentNationalRetailFederationandHappyReturnssurveyfoundthat93percentofretailersviewreturnsfraudor

exploitativebehaviortobeasignificantproblem.Onaverage,oneinfourtransactionsnowincludesatleastone“bracketed”item—customersbuyingmultipleversionsofaproductwiththeintentionofdecidinglaterwhichonestoreturn—andnearlythree-quartersofsellerssaythisbehaviorisincreasing.

Fromcostcentertocompetitiveadvantage:ModernizingreverselogisticswithAI5

Exhibit2

Consumershavedi仟erentreasonsformakingareturnbasedonproductcategory.

Topreasonssurveyedconsumersmakereturnsafterdeterminingtheydonotwanttheproduct,1%ofrespondentsselectingreasonasatop3reason

FreereturnsGuaranteedrefundEasyinitiationConvenientdrop-o仟

Fashion

DIYsupplies/equipmentConsumerelectronics

Toys/games

Homedecor

SmallhomeappliancesOfice/craftsupplies

Homewares

Beauty/personalcareHealth/wellness

Petsupplies

Food/beverage

Householdsupplies

58

52

53

59

48

52

51

50

56

27

65

17

52

70

63

59

57

57

54

51

50

47

39

38

30

29

39

31

35

24

38

39

28

41

42

9

25

22

31

49

44

51

38

48

39

37

48

37

25

35

28

50

Average49494131

1Question:Whatarethetop3mostcommonreasonswhyyoudecidedtogoaheadwitha[PRODUCTTYPE]returnafterinitiatingit?Pleaseselectupto3.

Source:McKinseyConsumerReturnsSurvey,Aug2–5,2025(n=844)

McKinsey&Company

Asevidenceaccumulates,AImodelscanbetrainedontheseoutcomestopredicttheoptimal

policymixforeachcustomersegment—andeventually,individualcustomers—balancing

retention,profitability,andfairness.Thesemodelsshouldincludeclearguardrails,suchascapsonrefunds,fees,orreturnwindows,topreventinadvertentmarginerosionormisalignmentwitharetailer’sobjectives.Itisalsoimportantthatallmodeltestingcomplieswithapplicableconsumerprotection,payments,anddataprivacyregulations,whichcanvarysignificantlybyregion.Takentogether,thesecapabilitiesallowretailerstomovefromstaticrulestodynamicsystemsthat

continuouslylearnandadapt.

Fromcostcentertocompetitiveadvantage:ModernizingreverselogisticswithAI6

AIcanhelpreturnedproductsreachtheirnextdestinationfaster

Onceacustomerinitiatesareturn,thesellermustdecidewhattodowiththeproduct,aprocessknownasdispositioning.Among30supplychainexecutivesinoursurvey,morethanhalfsay

dispositioningistheirgreatestchallengeinmanagingreturns,asmostofthetotalcostofareturnisconcentratedatthisstage.

Inthatsamesurvey,allbutfivesupplychainleaderssaytheircompaniesstillrelyonbasicdata,ornodata,tomakedispositioningdecisions.Historically,dispositioninghasbeenmanualand

tedious,requiringcostlyinspectionbeforedecidingwhethertorecycle,repair,orrestock—oftenthroughaslow,linearprocess.

Companiescaninsteadadoptadynamicdispositionmodelthatintegrateswhattheyalready

knowaboutthecustomer,product,supplychain,andoperationstooptimizethereturnpathwayfromthemomentthereturnisinitiated.Manyretailersalreadyhavethedatatomakesmarter

dispositioningdecisions.Besidesknowingagreatdealabouttheircustomers,theyalsoknow

aboutaproduct’smarginprofile,seasonality,shelflife,andresalepotential;thesupplychain

costsandtimelinesassociatedwithshipping,inspection,andredistribution;andtheoperationalsetupoftheirretailnetwork,includingstorelocationsandinventorycapacity.

Thebestresultscomewhencompaniescombinethesediversedatasourcesintoasingle,

AI-drivendecisionenginethatrouteseachreturneditemtoitshighest-valueoutcomeinreal

time.Byintegratingproductdatawithdemandforecasts,retailerscanensurerestockingoccurswhendemandstillexists,whileanalyzinghistoricaldefectpatternsagainstcustomer-reportedissuescaninformconditionchecksthatfast-trackrefurbishingorrecycling.Together,these

insightsenablerisk-adjusted,situation-specificdecisionsthatavoidunnecessaryshippingto

defaultintermediarylocationsandreturnproductstomarketfaster,maximizingvaluerecapture.

Forexample,whenanunknowncustomerreturnsa$100holidaysweaterinearlyDecember,theitemfollowsadefaultpath:Itisroutedtoacentralfacility,queuedforstandardinspectionand

repackaging,andeventuallyshippedtoadiscountpartnerinJanuary.Thatslow,staticprocesserodesvalue,asretailersrecoveronlyabout50percentoftheproduct’sworth.

Incontrast,whenatrustedcustomer—abuyerwithareasonablereturnhistoryandaccurate

claims—returnsthesamesweater,aretailer’sdigitalreturnsportalpredictsthatnorefurbishingorredistributionstepswillbeneeded.And,asin-seasonmerchandise,thereturnshouldbe

routeddirectlytoanearbystoreforresalewithindays,notamonthorlonger.Thisallowstheitemtoreenterthemarketwhiledemandisstillhigh,boostingrecoverytoaround75percent.

Agrowingsetoftechnologyprovidersalreadyhelpsretailersmakesmarterdispositioning

decisions,andsomeretailersareusingthesetoolseffectivelytoday.Thebiggerissueisnot

theabsenceofsolutionsbutlimitedandunevenadoption.Manyretailersstillrelyonmanual

approachestodispositioningorusedispositioningsoftwareinisolation,ratherthanaspartofanend-to-endreturnsprocess.Capturingtheupsiderequiresmorethanadoptingatool.Retailersmustfirstconnectcustomer,product,andsupplychaindata;defineclearrulesthatbalance

speed,margin,andcapacity;andredesignreturnsworkflowssodispositioningdecisionsare

madeautomaticallyandearly—ideallyatthemomentareturnisinitiated,notafteritemsenterthenetwork.(Tobesure,retailersdonothavetouseoff-the-shelfdispositioningplatforms;theymaychoosetoacquireandcustomizetheseplatformsorbuildtheirownentirely,dependingonscalerequirements,internaltechcapabilities,anddatamaturity.)

Fromcostcentertocompetitiveadvantage:ModernizingreverselogisticswithAI7

Retainingvaluefromreturneditemsbeginsbeforetheproductissold

Duringdispositioning,companiesconsiderthevaluetheycanrecoverfromeachitem.For

itemsthataren’trecycledordonated,companiestypicallyhavethreere-commerce(orreversecommerce)optionstorecovervalue:resellingtheproductinstores,liquidatingitviaathird-

partymarketplace,orsellingforparts.Unfortunately,mostsellersrecoveronlyabouthalfofaproduct’svaluewiththesestrategies(Exhibit3).

Factoringexpectedvaluerecoveryintotheearlierstagesoftheproductlifecycle,including

designandpricing,canhelp.Wheninsightsfromreturnsfeedbackintoproductandoperationsdecisions,retailerscanreducereturns,recovermorevaluefromeachitem,andimprovemargins.

Considerproductdesign:Productsareoftenoptimizedfortheirfirstsale,withlessthought

giventowhathappensifthey’rereturned.Byfactoringreturnandresaleinsightsintodesign,

companiescanreducereturnriskandincreaseresalepotential.Somefootwearbrandsarenowdesigningshoeswithinterchangeableparts—likereplaceablesolesordetachableuppers—so

theycanbeeasilycleaned,refurbished,orresoldthroughcertifiedsecond-lifechannels,buildingcircularityintotheproductitself.AnelectronicsmakercouldembedsmartsensorsordigitalIDsthattrackusageandconditiondata,allowingautomaticassessmentandrelistingofreturned

devices.Inbothcases,upstreamdesignchoicesunlockdownstreamvaluerecovery,turningreturnsfromacostcenterintoaplannedstageoftheproductlifecycle.

Exhibit3

Evenwhenretailersattempttoclawbackvalue,mostofthevalueinproductsislostinthereturnsprocess.

Medianshareofvaluerecovered,byrecoverymethod,1%

Resellasnew

65–75

Resellatdiscount

45–55

Refurbishandresell

25–35

Liquidateviathird-partymarketplaces

20–30

Sellparts/components

10–20

Donateitem

5–15

Useparts/componentsinotheritems

5–15

Recyclematerials

0–10

1Question:Forthevaluerecoverymethodsyouselectedabove,approximatelywhatpercentageofareturneditem’soriginalmarketvaluedoesyourcompanytypicallyrecoverthrougheachofthefollowingvaluerecoverystrategies?

Source:McKinseyConsumerReturnsSurvey,Aug2–5,2025(n=844)

McKinsey&Company

Fromcostcentertocompetitiveadvantage:ModernizingreverselogisticswithAI8

Orconsiderpricing:Ahomefurnishingsretailermightusepredictiveanalyticstoidentifyhigh-returnproductsegmentsandadjustpricing,bundling,orincentivesaccordinglytobetteralignwithcustomerpreferences—suchasofferingtailoredbundlesor“keep-it”credits—helping

reduceunnecessaryreturns.Itcouldalsotimepromotionsaroundrefurbishedoropen-box

restocks,protectingmarginsandminimizingmarkdowns.Together,thesestrategiesshiftfocusfromfirst-salerevenuetofulllifecyclevalue.

High-qualitycustomerreviewsarealsovaluabletoretailers.Mostonlineshoppersconsult

reviewsbeforemakingapurchase,anddetailedfeedbackonfit,fabrication,andqualityhelps

customersmakebetterpurchasechoicesupfront.Ashopperchoosingbetweensizesmay

selectthecorrectfitafterreadingthatanitemrunslarge,ortheymaychooseadifferentproductthrough“othersalsobought”recommendations.Retailerscanincreasehigh-qualityreviewsbypromptingcustomersshortlyafterdelivery—onceitemshavebeenwornorused—andbymakingtheprocesseffortlessthroughone-clickratingsfromemailorapp,mobile-firstflows,andshort,structuredpromptsfocusedonfit,quality,anduse.Whenretailersencouragetheircustomerstoleavereviews,theyreduceavoidablereturnswhilestillsupportingconversion.

Tocapturethisvalue,retailersneedanend-to-endre-commercestrategythatclearlydefinesownershipandaccountabilityandtreatsreturnsasacorebusinesscapabilityratherthana

secondaryprocess.Third-partypartnerscansupportretailers’returnsexecution,butprimaryresponsibilityshouldsitwithadesignatedinternalleaderwhoconnectsreturnsinsightsacrossdesign,merchandising,andoperations.

Tobuildafuture-readyreverselogisticsmodel,addresssixcorelevers

Asreturnsgrow,companiescanusethefollowingsixleverstoaddresskeyreverse-logisticspainpoints:demand,dataandinsights,decisioning,operations,re-commerce,andfeedback(table).

Below,we’vehighlightedthreeofthese—dataandinsights,decisioning,andfeedback—sincetheyunderpintheend-to-endcyclefrominsightgenerationtodecision-makingtosustainedexecution.

Dataandinsights

Combinedatafromacrossfunctionstounlockricherinsightsaboutcustomers,products,andmarketdynamics:

—Designscalablecloudorhybridarchitectureforreturnsdata,andestablishreal-timeingestionpipelinesthatconnectstructuredandunstructuredsources(suchasreturnsportals,pointofsale,resaleplatforms,andshippingrecords).Thisenablesasingle,

continuouslyupdatedviewofreturnactivity.

—Createaunifiedreturnsdataproduct,orasinglesourceoftruthwithclearownership

andgovernance.EnsureitincludeskeyvariablessuchasCLTVandreturnshistory,SKU

information,timeandmodeofreturn,seasonality,expecteddemand,reasoncode,productvalue,expectedrecovery,andprocessingcost.

Fromcostcentertocompetitiveadvantage:ModernizingreverselogisticswithAI9

Table

Byoptimizingsixleversacrossreverselogistics,retailerscanrepositionreturnsasasourceofgrowthandcompetitiveadvantage.

KeyreturnsleversDescription

Minimizingreturnratesbyenhancingpurchaseprecisionandoptimizingfrictionalongthereturnsexperience

Leveragingdeepdataintegrationtounlockricherinsightsintocustomers,products,andmarketdynamics

Harnessingdata-drivenintelligencetooptimizereturnsstrategies,operationalefficiency,andvaluerecovery

Optimizingreturnslogisticstominimizeresourceuseandstreamlinereversesupplychainefficiency

Maximizingvaluerecoveryfromreturnedproductsthroughsmartresale,refurbishment,andreintegrationstrategies

Dataandinsights

Decisioning

Re-commerce

Operations

Demand

Feedback

Buildingintegratedfeedbackloopstodrivealignmentacrosstheend-to-endreturnsvaluechain

—Linkproductmetadata(likemarginprofile,defecthistory,andlifecyclestatus)toreturns

information.Usemachinelearningmodelstopredictrecoveryvalueandautomaterouting

decisions,suchaswhethertorefurbish,liquidate,orrestockanitem.Accountforthefull

costandvaluepotentialofreturnsateverystage—fromshippingandinspectiontoresaleorrecycling—tosurfacethetru