future freight flows: poten1al trends – near and...
TRANSCRIPT
MIT Center for Transportation & Logistics ctl.mit.edu
FutureFreightFlows:Poten1alTrends–NearandFar
Chris Caplice ([email protected]) Director, MIT FreightLab
20 January 2017
MIT Center for Transportation & Logistics
FreightTransporta1onPlanningisHard.• Hardforshippers,• Harderforcarriers,• Hardestforgovernmentplanners!
n Infrastructureplanning1meframeisdecadesn Diverseandvocalcons1tuents(NIMBY,BANANA)n Palletsdon’tvoten Bothmodalandjurisdic1onalsilosn Revenuesourcesaredecreasingdrama1callyn Removedfromthesystemusers
These challenges were recognized by AASHTO and USDOT – resulting in the Future Freight Flows project.
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TheFutureFreightFlowsProject
MIT Center for Transportation & Logistics
FFFProjectObjec1ves&Deliverables• TwoObjec1ves:
n “Providedecisionmakers[stateDOTs]withacri1caldrivingforcesbehindhigh-impacteconomicchangesandbusinesssourcingpaTernsthatmayeffecttheUSfreighttransporta1onsystem[intheyear2030&beyond].”
n “BeTerenableinformeddiscussionsofna1onal,mul1-state,state,andregionalfreightpolicyandsysteminvestmentpriori1es.
• ThreeDeliverables:n AnalysisofDrivingForcesn FutureScenariosn ToolkitforrunningaFutureFreightFlowWorkshops
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So many potential futures, so little time . . .
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FFF Thought Leaders
Candidate Forces& Uncertainties
12 Snapshot Scenarios
Brainstorming Session
Prioritization Workshop
Expert Practitioners
Analyze, Harmonize and Merge
Future Freight Flows Symposium
Stress maps Flow Impacts
Influence Matrices
Analyze and Merge
Freight Stakeholders
20 Candidate Forces
Distribute Survey
264 complete and usable responses
Stakeholders Survey
Scenario Generation
Identify key driving forces
Two structuring axes Develop
storylines Potential storylines
Test and Refine storylines
4 scenario skeletons Finalize scenarios
4 scenarios
MIT CTL Team
Supply chain professionals
Phase 1
Phase 2
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Strategyvs.FactorsvsForces
7
• Strategyn Thingsyoucontroln Solu1ons&approaches
• Factors(“Inside-out”)n Youcannotcontroln Youmaybeabletoinfluencen Directandobviouseffects
• Forces(“Outside-in”)n Youcannotcontroln Youcannotinfluencen Indirect,ambiguous&unknowneffects
A scenario is a set of driving forces
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Key Drivers 1. Global Trade 2. Resource Availability
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FourFutureFreightFlowScenarios
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• DigitalFreightMatching• Transporta1onManagementSystems• MobileCommunica1on• AutonomousTrucks
now +20years+5years+1year +10years
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DigitalFreightMatching
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UberforX
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WhynotUberforFreight?
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Over $500M invested in these 67 start ups
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MIT Center for Transportation & Logistics
Thelast1meVCsthoughtfreightwassexy... >200 Transportation Electronic Marketplaces existed in 1999,
but essentially none survived in their original form.
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Thelast1meVCsthoughtfreightwassexy...
Source:Boyle,Marc(2000)Business-to-BusinessMarketplacesforFreightTransporta7on
MIT Center for Transportation & Logistics
Most Recent Real Disruption?
Source: AAR and ATA
50.0$
60.0$
70.0$
80.0$
90.0$
100.0$
110.0$
1980$1982$1984$1986$1988$1990$1992$1994$1996$1998$2000$2002$2004$2006$2008$2010$
IndexofRevenueperMileforUS.TruckinginReal$
Deregulation
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Case of Rapid Change: Deregulation
Bifurcation of US Trucking Market
Source: Parming 2013
Predominant LTL
Predominant TL
Hybrid
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DoestheUbermodelfit?
• Whatdowedowhenweuber?1. ContactasinglesourcethroughanApp2. “Real1me”visibilityofnearbyvehicles3. Matchedtooneofmul1pleunderlyingproviders4. Paymenthandledoffline,es1matedinadvance5. Pricingvariesbasedonsurging
IsUberjustFreightBrokerageforPassengers?
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HowdotheMarketsCompare?PAX FRGT
CompeDDveMarket LocalMonopolies(taxis)
HighlyCompe11ve
NewCapacity Untapped/Part-Time NoneBusinessType C2C B2BServiceTypes Limited UnlimitedFrequencyofUse Occasional Repe11vePlanningLeadTime 0min 1-3DaysLengthofHaul VeryShort
(~6miles)MuchLonger(500miles+)
LoadingTime ~30seconds >1hourAsset/DriverType PersonalVehicle CommercialVehicle
Par1allyadaptedfromSa1shJindal(2016)
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Transporta1onPoroolioCon1nuum• Differentnetworksegmentsrequiredifferentrela1onships• Segmenta1onofnetworkandcarriersbyneeds• Con1nuumfromone-offtransac1onstoownership
n OwnershipofAssetsversusControlofAssetsn Responsibilityforu1liza1onn On-goingcommitment/responsibili1esn SharedRisk/Reward–Flexiblecontracts
Private Fleet
Spot Market
Dedicated Fleet
Core Carriers
Alternate Carriers
Use for most reliable and steady flows
Use for random & distressed traffic
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ProposedvaluetobeTermatching
• Improvedvehicleu1liza1onn Es1matesinUS10%-30%emptymilesn Differsbylengthofhaul&carriersize
• Reducedtransac1onalinefficiencies(fric1on)n Streamlinematching,payment,no1fica1on,visibility,etc.
n Doesvisibilityofnearbytrucksaddvaluetoashipper?
MIT Center for Transportation & Logistics
MyTake-Awayson“UberforFreight”• Moststartupsinthisspacehatethename!• Somestartupsdohavehaveimprovedfunc1onality...
n Evolu1onarymorethanrevolu1onary,n Servingtoincreasecustomerexpecta1ons,butn Worthwhilefunc1onalityisbeingincorporatedwithinTMSorbrokers.
• Demiseofbrokershasbeengreatlyexaggerated(again)n Middleman’sroleisgrowing,notbeingdiminishedn Promised“twoparty”transac1onsarereally“threeparty”n Poten1alconsolida1oninbrokeragespace–strongeconomiesofscale
• Areaforfit:Localreal-1me,on-demanddelivery
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Begsabiggerques1on...
ContractRate
SpotRate
Transporta1o
nRa
te($
/mile)
1me
Ifspotmarketwastotallyliquidandreliable,woulditleadtotheendofannualcontracts?
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TMSTrends
MIT Center for Transportation & Logistics
Gartner’sMagicQuadrantforTMS
Excel, Phone & Fax!
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LatestTMSTrends• ConvergenceofSystems
n BridgingFunc1onsw Connec1ngtoWMS,OMS,IMS,etc.w Fixnginend-to-endsolu1onsw GrowthofSupplyChainPlaoorms
n Connec1nggapbetweenplanning&execu1onw Integra1ngreal-1mestatusintoexecu1onw Feedingexecu1onresultsbackintoplanningw Procurementtriggering(marketvs.schedulebased)
• Evolu1onofDeploymentn Finallyflippedfromself-hostedtoremotehostedn Longevolu1on:ASPtoSaaStoCloudn Differentflavorsofremotehos1ngn Fasterupgradesandrolloutofimprovements
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MyTakeAwaysforTMSs• Thedecisionfortheshipperhasnotchanged,
n StandardprocessesversusCompe11veadvantagen ERPoff-the-shelfversusBestofBreed
• Thespeedofimplementa1oniss1llaproblem,n Gexngfaster(forvanillainstall)n Connec1ngcarriersiss1llthe1mesinkn Nostandardiza1onofformatordata
• MosthaveDigitalFreightMatchinganyway!n Privatemarketplacesn Dynamicandadap1vecarrierselec1on
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Example:CarrierSelec1onwith AutomatedEscala1on
Carrier
Accept?
OrderManagement
System
Load
No tLT>tMIN?
SelectAppropriate:(1)CarrierGroup&
(2)ClearingMechanism
No
Yes
CarrierCarrierCarrier
Offer
Response(s) OK?
Yes
Done
SelectCarrierfromRou1ngGuide
Tender
Tender
Yes
No
Transporta1onManagementSystem
MIT Center for Transportation & Logistics
Num
ber o
f Car
riers
R
ange of Pricing
Primary
Step 1
Lane Backup
Step 2 Step 4
All Relevant Company Carriers
(Dynamic Prices)
All Relevant Company Carriers (Quoted Rates)
Step 3 Steps Step 5
Public Market
AutomatedEscala1onProcess
MIT Center for Transportation & Logistics
MobileCommunica1ons
MIT Center for Transportation & Logistics
MobileCommunica1ons
• Providingreal-1meaccesstodriversn Forshippers,carriers,brokers...n GPSbasedposi1oning-trackingn Visibilityversusexcep1onmanagement
• Connec1vitytothedriver......doshippersreallywantthisinforma1on?...docarriersreallywanttogivethisinforma1on?
MIT Center for Transportation & Logistics
ChallengesforMobileTracking• Howeasilycanreal-1meassettracking...
n GPSdatabemergedwithmilestoneEDIdata?n Betranslatedandmappedintoac1onableontheunderlyingordersandgoods?
n BeconvertedintobeTerpredic1ons?• ImpactofwidespreaduseofElectronicLogBooks?• Whathappenswithcompletetransparencytodrivers?
n Dissolu1onofcarriers?n Growthofalliances?n Growthoffreightbrokerage(UberFreight)?
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AutonomousTrucks
MIT Center for Transportation & Logistics
Shizfrom“If”to“What,When,&Where”• TheWhat...likeboilingafrog!
n Notabinarydecision...w NoAutoma1on(Level0)w Func1on-SpecificAutoma1on(Level1)w Combined-Func1onAutoma1on(Level2)w LimitedSelf-DrivingAutoma1on(Level3)w FullSelf-DrivingAutoma1on(Level4)
n SystemsinPlacew CollisionMi1ga1onSystemsw IntegratedSafetySystemsw LaneDepartureWarningw BlindSpotDetec1on
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Shizfrom“If”to“What,When,&Where”• TheWhen...fasterthanoriginallythought!
n FirstpaidautonomousdeliveryoccurredinColoradoinOctober2016.
n UberFreightOn-goingExperiments&Trialsw Ini1alwindowwas15yearstocommercialnon-pilotusew Releasingsozwareupdates2-3xweeklyandhardwareweeklyw Windowfornon-pilotcommercialuseshrinkingtosingleyears
MIT Center for Transportation & Logistics
MIT Center for Transportation & Logistics
From“If”to“What,When,Where,&How”• TheWhere...threeenvironmentsforfreight
n Longhaulcorridorsn Shorterhaullocalmoves/shuTlerunsn IntraFacility(Yard)moves
LongHaulShorterHaulIntra-Yard
MIT Center for Transportation & Logistics
LongerTerm...• DirectChanges
n Increasedsingledayrange(~1000miles)n UbiquitousnessofTLcombinedwithlowcostofIMn Lowerfuelcosts
• IndirectImpactsn Reduc1oninNa1onalDCs,increaseinlocalsn Concentratedcorridortrafficn Dissolu1onofTLcarrierstoindependentdrivingen11es
MIT Center for Transportation & Logistics
• DigitalFreightMatching• Transporta1onManagementSystems• MobileCommunica1on• AutonomousTrucks
now +20years+5years+1year +10years
MIT Center for Transportation & Logistics
Questions, Comments, Suggestions?
“Wilson&Dexter–disrup1ngthedominantdesigndaily”YankeeGoldenRetrieverRescuedDogs(www.ygrr.org)