future freight flows: today & tomorrow - mit...

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MIT Center for Transportation & Logistics ctl.mit.edu

FutureFreightFlows:Today&Tomorrow

[email protected]

Director,MITFreightLab15June2017

MIT Center for Transportation & Logistics

MITFreightLabFinding better ways to design, procure, manage, and assessfreight transportation networks across all modes and regions.

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FreightLab – PartialResearchProjectList• OptimalTransportationProcurement• GlobalOceanTransportReliability• TransportationPortfolioDesign&Management• FreightTransportationProductivity• CarrierFuelBurden• SameDayFleetLoadAcceptance• SmallCarrierProfitabilityStrategies• DirecttoStoreDeliveryStrategies• TheLivingPlan– Robustnessvs.Flexibility• SupplyChainComplexity• DisruptionsofDominantDistributionDesign

UnderlyingQuestion...Howtodealwithvariability&uncertainty?


Planning(Prevention)versusResponding• MITCTLGlobalRiskSurvey

n 1,500responsesfrom70countriesn Ratingfrom0(100%Prevention)to4(100%Response)

• Averageoverallwas1.41n 54%Plannersn 16%Respondersn 30%OntheFence

• DemographicDriversn Gendern Geographyn JobFunction

4

WomenareRespondersMenarePlanners(1.60vs.1.37)

0 1 2 3 4

Planner Responder

3


Geography– Ofbirth,notwork!

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JobRoleorFunction

4


TheLivingPlan:Robustvs.FlexiblePlanning

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IsBenFranklinstillcorrect?“An ounce of

prevention is worth a pound of cure”

Poor Richard’s Almanac

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MIT Center for Transportation & Logistics 9

Whatistheproblem?OverallResearchQuestions

1. Howshouldafirmbestselect,onastrategicbasis,whattypesofcontractualrelationshiptouseforwhichsegmentsofitsfreighttransportationnetwork?

2. Howshouldthisdecisionfitintothegeneraltransportationprocurementprocess?

3. Howcanwebalancerobustnesswithflexibility?

ResearchApproachn PartneredwithWal-Martn Extendedresearchteam(Dr.FranciscoJauffred co-PI)n Developedandimplementedstochasticoptimizationmodeln IntegratedinWMprocurementprocess


CurrentTransportationPractice• ThereisacontinuumofrelationshipsforTLbasedon:

n OwnershipofAssetsversusControlofAssetsn Responsibilityforutilizationn On-goingcommitment/responsibilitiesn SharedRisk/Reward– Flexiblecontracts

PrivateFleet

SpotMarket

DedicatedFleet

CoreCarriers

AlternateCarriers

Use for most reliable and steady flows

Use for random & distressed traffic

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0%10%20%30%40%50%60%70%80%90%100%

0 1 2 3 4 5 6 7 8 9 10

Loads Per Week

Pro

bab

ility

Prob Cumul

0%10%20%30%40%50%60%70%80%90%100%

5 7 9 11 13 15 17 19 21 23Loads Per Week

Pro

babi

lity

Prob Cumul

Howmuchvolumetoassigntothefleet?

Lane: DCP -> S93304 ~N(14.6, 2)

The“BufferVolume” capturesourconfidencelevel.Itcanbepositiveornegative:>0indicateswepreferexcessfleetcapacityoverbeingshort(ShortSensitive)<0indicatesweprefershortfleetcapacityoverhavingexcess(ExcessSensitive)=0indicatesweareindifferenttohavingexcessorshortfleetcapacity(RiskNeutral)

AssignedFleetVolume(AFV)perWeek=AvgVol +(BufferVolume)

Continuous Lane Intermittent Lane

ExcessSensitive

ShortSensitive

Risk Neutral Lane: V93252 -> DCP

~P(3.6)

ExcessSensitive

ShortSensitive

Risk Neutral

How do we determine the acceptable risk to calculate the optimal “Buffer”?


AllToursGenerated“Optimally” EachWeek

0%

20%

40%

60%

80%

100%

0

50

100

150

200

250

0 1 2 3 4 5 10 15 20 25 30 35 40 45 50 52

Cumulative%ageofOccurrence

Freq

uency

No.ofweeks

Histogram&CDFofAllToursinCompleteFlexibilityOperationalPlan

Frequency Cumulative%Consistent Tours!

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AnnualPlannedToursthatAppearinWeekly

0%

20%

40%

60%

80%

100%

0

10

20

30

40

50

60

0 5 10 15 20 25 30 35 40 45 50 52

Cumulative%ageofOccurrence

Freq

uency

No.ofWeeks

Histogram&CDFofStochasticPlanTours

Frequency Cumulative%

Greater Percentage of Consistent Tours!!


Comparing“Plan” versus“Operational” Tours

0

50

100

150

200

0 10 20 30 40 50

No.ofTou

rs

OperationalPlanSimulationWeek

Break-downoftheNo.ofweeklygeneratedtoursthatconsistofStochasticPlanTours

No.ofToursinFullFlexibilityOperationalPlan No.ofToursfromStochasticPlan

“Ad-hoc” tours generated, that were not part of the Annual Plan (29%)

No. of tours generated, that were part of the Annual Plan (71%)

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USTRANSCOMProject• Explore,develop,andevaluatealgorithmictechniques,

methodologies,andapproachestobettersolvethetacticalaircraftplanningandschedulingproblemforUSTRANSCOM–especiallyconsideringuncertaintyandvariabilityinmissions.

• Sub-Objectives1. Formulateandcreatecodeprototypesofcandidateresilientoptimization

models2. Examinethetrade-offbetweenrobustplanningandflexibilityinresponse3. Createasimulationtestbedtoenablehands-ontestingandevaluationof

differentplanningmodels,operationalpolicies,andotherfactors


Simulation ModuleNetwork

Resources

Requirements

‘Optimal’PlanParameters

ActualOutcome

UNCERTAINTY MODULE

PLANNING MODULE Options:• Heuristic Rules • Deterministic Optimization• Stochastic Optimization• Robust Optimization

UNCERTAINTY MODULE Options:

• Arrival of new requirements• Change to old requirements• Aircraft delays

OPERATIONAL LOGIC Options:

• Buy 3rd party capacity• Cargo re-routing allowed• Aircraft re-routing allowed• Cargo & Aircraft re-routing

allowed

PLANNING MODULE

OPERATIONAL LOGIC MODULE

SCENARIO ANALYSIS SCENARIO ANALYSIS • Lane capacity comparisons

• Total system cost• Comparative statistics . . .

etc.

1

2

SimulationTestBedOverview

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ResilienceTradeoff

0

5

10

15

20

25

30

TotalSystemCost

LevelofResilience(robustness&flexibility)

CostofthePlan CostofDisruption Plan+Disruption

Deterministic-Optimal Solution – potentially fragile in face of any change

Overly Redundant Solution – excess robustness or padding

Sweet Spot

Resilient Solution – proper balance of robustness & flexibility

No Resilience

HighResilience


Synchromodal Flow

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SynchronizingTheSupplyChain• OverallResearchQuestions

1. HowcanweidentifySKUsthatcanbemanufactured,transported,anddistributedinstablequantitiesoveranextendedperiodoftime?

2. Wheredothesavingscomefromforthissynchronizedflow?3. Howcanwemaintain/updatethissynchromodal plangoingforward?

• ResearchApproach1. Workedwithlargeconsumerpackagedgood(CPG)manufacturer2. ThesisbyPriya Andleigh andJeffreyBullock,supervisedbyDr.AhmadHemmati3. DevelopedmodelforidentifyingcandidateSKUsandquantifyingthebenefits.


ProjectObjectivePlant

Warehouse

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ProjectObjective

Howmuch?

WhichSKUs?

On-Demand

Fixed Volume

$ per load

PlantWarehouse


SteadyFlowTrade-offs

5 7 9 11 13 15

Costs/Savings($)

SteadyFlow(#ofpallets/week)

TransportationSavings

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5 7 9 11 13 15

Costs/Savings($)



Cross-DockSavings



5 7 9 11 13 15

Costs/Savings($)



Cross-DockSavings

ExcessInventoryCost

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5 7 9 11 13 15

Costs/Savings($)


TransportationSavingsCross-DockSavingsExcessInventoryCostTotalSavings


Maximum Savings


Yes

Hweeks

Methodology

HistoricalData

ForecastData

ForecastCheck

DescriptiveStatistics

SteadyFlow

Eligible?

STOP

No

%Forecast>0>X%%weeksshipped>Y%COV<Z%ALL

weeks

Fweeks

1

2 3

4

5

6

CreateOptimizedSteadyFlow

LevelSKUs• List• Quantity

SteadyFlow

7 9

Maximize{Transportationsavings+Cross-docksavings–

ExcessInvcost}

FixedTruck/IMCapacity

8

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SKUExample1 DemandCharacteristicsMinimum(pallets/week) 28 %weeksshipped 100%Mean(pallets/week) 37.7 COV 0.13StdDev(pallets/week) 4.80

ModerateVolume,StableSKU


DemandCharacteristicsMinimum(pallets/week) 28 %weeksshipped 100%Mean(pallets/week) 37.7 COV 0.13StdDev(pallets/week) 4.80

ModerateVolume,StableSKU

SKUExample1

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SKUExample2 DemandCharacteristicsMinimum(pallets/week) 9 %weeksshipped 100%Mean(pallets/week) 27.9 COV 0.60Std Dev(pallets/week) 16.7

HighCOVSKU


SKUExample2 DemandCharacteristicsMinimum(pallets/week) 9 %weeksshipped 100%Mean(pallets/week) 27.9 COV 0.60Std Dev(pallets/week) 16.7

HighCOVSKU

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LowVolume,NotShippedEveryWeek

SKUExample3



LowVolume,NotShippedEveryWeek

SKUExample3

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Implications

SteadyFlow


ReplenishmentCycleTimeReduction

BullwhipDampening

Cross-dockProductivity

SafetyStock

Reduction

ContractInnovation

• Identifyingthe“steadymovers”hasbenefitsbeyondjusttransportationstability.

• Allowsformanufacturingandcross-dockingimprovements.


GlobalOceanTransportationReliability

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Lead/TransitTimeReliability• KeyQuestions:

nWhatisthedefinitionofreliabilitywithinafirm?nWhatarethesourcesofunreliability/variability?nHowcanthecurrentsituationbeimproved?

• TwoDimensionsofReliabilityn Credibilityn ScheduleConsistency

Material adapted from Arntzen, B. (2011) “Global Ocean Transportation Project,” Internal MIT Center for Transportation & Logistics (CTL) Report.


Meanactualtransittime

Contractedtransittime

Estimatedtransittime

45°line

ObservationsfromPractice

Betterthancontract

Worsethancontract

Contract reliability in procurement and operations do not always match

Material adapted from Caplice, C and Kalkanci, B. (2011) “Managing Global Supply Chains: Building end-to-end Reliability,” Internal MIT Center for Transportation & Logistics (CTL) Report.

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Whileaccurateestimatesoftheport-to-porttransittimesexist,thereisonlylimitedinformationonportdwelltimes.

Origin port dwell Destination port dwellPort-to-port transit time

Contract reliability differs dramatically across different route segments

ObservationsfromPractice


Distributions are not always symmetric or uni-modal.ObservationsfromPractice

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WhenshouldyourERPconsidervariability?

IncorporateWorseoffbyignoringlead-timevariability

IgnoreBetteroffbyignoringlead-timevariabilityCo

efficientofV

ariatio

n(std

dev/med

ianlead-tim

e)

Criticalratio

~15%

~80%

~5%


HandlingToday’sVariability• Useportfoliooftransportationsolutions

n IdentifybestuseofRobust&FlexibleoptionsnDetermineoptimalallocationacrossthenetwork

• Segmenttheproductbyvariabilityn IdentifycandidateSKUsforsynchromodal flown Benefitsextendbeyondjusttransportationsavings

• Ignoreitn Identifywhenitisworthwhiletoinvestornot

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ShiftingtoTomorrowVariabilitybecomesUncertainty


FreightTransportationPlanningishard.• Hardforshippers,• Harderforcarriers,• Hardestforgovernmentplanners!

n Infrastructureplanningtimeframeisdecadesn Diverseandvocalconstituents(NIMBY,BANANA)n Palletsdon’tvoten Bothmodal andjurisdictional silosn Revenuesourcesaredecreasingdramaticallyn Removedfromthesystemusers

42Recognized by FHWA & NCHRP – thus the NCHRP 20-83 Projects

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NCHRP20-83(1)ProjectObjectives• TwoObjectives:

n “Providedecisionmakers[stateDOTs]withacriticalanalysisofthedrivingforces behindhigh-impacteconomicchangesandbusinesssourcingpatternsthatmayeffecttheUSfreighttransportationsystem [intheyear2030&beyond].”

n “Betterenableinformeddiscussionsofnational,multi-state,state,andregionalfreightpolicyandsysteminvestmentpriorities.

• Details:n NationalAcademies- TransportationResearchBoard(TRB)&National

CooperativeHighwayResearchPrograms(NCHRP)n Firstof7projectsfocusedon“long-rangestrategicissues”n Developanddelivermethodologythrough6workshops

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DifferentMethodsforPlanning

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Time

Plan

ning

Hor

izon

But what about very long term (10 to 30+ years) planning?

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Longertermplanningisimpactedbyevents

Source:Scenarios:AnExplorer’sGuide,ShellInternational2003.


PoorForecastingisnot athingofthepast...

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2"

2.25"

2.5"

2.75"

3"

3.25"

3.5"

3.75"

4"

4.25"

4.5"

4.75"

Jan+06" Jul+06" Jan+07" Jul+07" Jan+08" Jul+08" Jan+09" Jul+09" Jan+10" Jul+10" Jan+11" Jul+11" Jan+12" Jul+12" Jan+13"

On#Highway#Diesel#Fue

l##($/gallon)##

Forecast_Jan2008" Forecast_Jan2009" Forecast_Jan2010" Forecast_Jan2011" Forecast_Apr2011" Actual"

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Stillnotathingofthepast...


DifferentMethodsforPlanning

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Time

Plan

ning

Hor

izon

Shift focus from prediction to preparation

But what about very long term (10 to 30+ years) planning?

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FutureFreightFlowScenariosWecreated4FFFscenariosforNovember2,2037

• Running6workshopsacrosstheUS• Over300shippers,carriers,andgovernmentplanners• Developmentof“ScenarioPlanningToolkit”forstateDOTs


TechnologyOpinions

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TechnologyTrends&ImpactHotTopicsHitList

nUberforFreightnDeliveryDronesnAutonomousTrucksnMobileComputingnAdditiveMfg

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Soon≤5years

Later>5years

SmallImpact

LargeImpact

1large-soon

2large-later

3small-soon

4small-later

5none- ever


DigitalFreightMatching

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Uber forX

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WhynotUber forFreight?

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Over$500Minvestedinthese67startups

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56

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ThelasttimeVCsthoughtfreightwassexy...>200 Transportation Electronic Marketplaces existed in 1999,

but essentially none survived in their original form.

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ThelasttimeVCsthoughtfreightwassexy...

• Source:Boyle,Marc(2000)58

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Most Recent Real Disruption?

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$2010$

IndexofRevenueperMile forUS.TruckinginReal$

Deregulation

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DoestheUber modelfit?• Whatdowedowhenweuber?

1. ContactasinglesourcethroughanApp2. “Realtime”visibilityofnearbyvehicles3. Matchedtooneofmultipleunderlyingproviders4. Paymenthandledoffline,estimatedinadvance5. Pricingvariesbasedonsurging

IsUber justFreightBrokerageforPassengers?

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HowdotheMarketsCompare?PAX FRGT

CompetitiveMarket LocalMonopolies(taxis) HighlyCompetitiveNewCapacity Untapped/Part-Time NoneBusinessType C2C B2BServiceTypes Limited UnlimitedFrequencyofUse Occasional RepetitivePlanningLeadTime 0min 1-3DaysLengthofHaul VeryShort

(~6miles)MuchLonger(500miles+)

LoadingTime ~ 30seconds >1hourAsset/DriverType PersonalVehicle CommercialVehicle


TransportationPortfolioContinuum• Differentnetworksegmentsrequiredifferentrelationships• Segmentationofnetworkandcarriersbyneeds• Continuumfromone-offtransactionstoownership

n OwnershipofAssetsversusControlofAssetsn Responsibilityforutilizationn On-goingcommitment/responsibilitiesn SharedRisk/Reward– Flexiblecontracts

PrivateFleet

SpotMarket

DedicatedFleet

CoreCarriers

AlternateCarriers

Useformostreliableandsteadyflows

Useforrandom&distressedtraffic

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Proposedvaluetobettermatching• Improvedvehicleutilization

n EstimatesinUS10%-30%emptymilesnDiffersbylengthofhaul&carriersize

• Reducedtransactionalinefficiencies(friction)n Streamlinematching,payment,notification,visibility,etc.nDoesvisibilityofnearbytrucksaddvaluetoashipper?

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MyTake-Aways on“Uber forFreight”• Moststartupsinthisspacehatethename!• Somestartupsdohavehaveimprovedfunctionality...

n Evolutionarymorethanrevolutionary,n Servingtoincreasecustomerexpectations,butn WorthwhilefunctionalityisbeingincorporatedwithinTMSorbrokers.

• Demiseofbrokershasbeengreatlyexaggerated(again)n Middleman’sroleisgrowing,notbeingdiminishedn Promised“twoparty”transactionsarereally“threeparty”n Potentialconsolidationinbrokeragespace– strongeconomiesofscale

• Areaforfit:Localreal-time,on-demanddelivery64

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Begsabiggerquestion...

ContractRate

SpotRate

Transportatio

nRa

te($

/mile)

timeIfspotmarketwastotallyliquidandreliable,woulditleadtotheendofannualcontracts?

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DeliveryDrones

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Trend:DeliveryDrones§ Whatisit?

§ Anunmannedaircraftthatcannavigateautonomously,withoutdirecthumancontrolorisguidedremotely.

§ Initiallyusedinmilitaryoperationsin2000§ Commercialusenowcommoninfilming,disastermanagement,search

&rescue,geographicmapping,precisionagriculture,wildlifemonitoring,etc.

§ Whatisstatustoday?§ DroneDeliveriesHaveAlreadyHappened

§ OnDec.72016,AmazonPrimedeliveredanAmazonFireTVandabagofpopcornbydronetoamannearCambridge,UK.

§ Flirtey and7-Elevendeliveredachickensandwich,donuts,candy,Slurpees andhotcoffeeviadroneinJuly2016inRenoNV.

§ Countrieshavevariedrestrictions

67PhotobyByEduardofamendes (Ownwork)[CCBY-SA4.0(http://creativecommons.org/licenses/by-sa/4.0)],viaWikimediaCommons

§ CommercialDrones§ Marketsize~$550Min

2014andforecastto$1Bin5years

§ Salesgrew>200%last2yearswith>2.5MinuseinUS

December2016ConnectRoboticsdeliveredfoodforanoldmeninthemountainsofPortugal.


Trend:DeliveryDrones§ DirectImpact:

§ Abletosendsmallloadstoremotelocationsquickly§ Expandsdeliverycapabilitiesusing“openair”withoutusingexistinginfrastructure

§ Essentiallyanewtransportationmodeforveryfastreplenishmentofverysmallshipmentsizeoveraclosedistance.

§ PotentialLongerTermIndirectImpacts§ Amazonwasawardedapatentforan"airbornefulfillmentcenter"whichessentiallyisblimpwithafleetofdronesfordelivery.

§ Willcomplement(butnotreplace)existinglastmiledeliverytechniques– willfindanichewithmostdeliverycompanies

§ Provideultra-fastreplenishmentforhighenditems

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Soon≤5years

Later>5years

SmallImpact

LargeImpact

1large-soon

2large-later

3small-soon

4small-later

5none- ever

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AutonomousTrucks

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Trend:AutonomousTrucks&Vehicles§ Whatisit?

§ Trucksandothervehiclesthatcanoperatewithminimal(orno)humaninteraction.

§ EstablishedLevelsofAutomation§ NoAutomation(Level0)§ Function-SpecificAutomation(Level1)§ Combined-FunctionAutomation(Level2)§ LimitedSelf-DrivingAutomation(Level3)§ FullSelf-DrivingAutomation(Level4)

§ Whatisstatustoday?§ AutonomousDeliveryAlreadyHappened

§ FirstpaidautonomousdeliveryoccurredinColoradoinOctober2016.

§ OttodeliveredfullTLofbeer

§ Majorinvestmentsintechnology§ UberacquiredOttoin2016for$680M§ IntelacquiredMobileyein2017for$15B

70PhotobySteveJurvetson [CCBY2.0(http://creativecommons.org/licenses/by/2.0)],viaWikimediaCommons

§ Wherecanitbeused?§ Linehaul Corridors§ Localdelivery§ Intra-Yard

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Trend:AutonomousTrucks§ DirectImpact:

§ Singledayrangeoftruckscoulddouble(~1000miles)§ Lowerfuelcostsduetolowerspeeds§ Ubiquityoftruckloadiscombinedwithlowcostofintermodal(truck-rail)

§ PotentialLongerTermIndirectImpacts§ Reductioninnumberofdistributioncentersandthusloweroverallinventorylevels

§ Concentratedcorridortrafficwithterminalsforlocaldrivingforlastmile

§ DissolutionofTLcarrierstoindependentdrivingentities§ Largejoblossinlong-haultrucking

72

Soon≤5years

Later>5years

SmallImpact

LargeImpact

1large-soon

2large-later

3small-soon

4small-later

5none- ever

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MobileComputing

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Trend:MobileComputing§ Whatisit?

§ Technologythatallowstransmissionofdata,voiceandvideoviaacomputeroranyotherwirelessenableddevicewithouthavingtobeconnectedtoafixedphysicallink.

§ Whatisstatustoday(2016)?§ Mobiledatatraffichasgrown18-foldoverthepast5years§ 8billionmobiledevices(325millionwearabledevices)currentlyinuse§ Averagesmartphoneusagegrew38%in2016withtheMiddleEastandAfricashowing

65%CAGRinmobiledatatrafficgrowth(mostanywhere!)§ Smartphonesareusedasphoneslessthan3%ofthetime§ Onlineshoppingbehaviordiffersbyplatform:laptops&tabletsvs.smartphones

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Trend:MobileComputing

§ DirectImpact:§ Abletoaccessdataandsystemsfromanywhere§ Enablesnewparadigmsofshoppingandthusretailsupplychains(omnichannel)

§ PotentialLongerTermIndirectImpacts§ Transformationofretailshoppingindustryandexperience§ CompleteshifttoCloudcomputingforenterpriseandotherapplications

§ Decentralizeoperationsformanyprocesses

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Soon≤5years

Later>5years

SmallImpact

LargeImpact

1large-soon

2large-later

3small-soon

4small-later

5none- ever


Whatothertrendsortechnologies???

76

Soon≤5years

Later>5years

SmallImpact

LargeImpact

1large-soon

2large-later

3small-soon

4small-later

5none- ever

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KeyTake-Aways• HandlingVariabilityToday

nManageacompletetransportationportfolionSegmentoutstableflowthroughsupplychainnUnderstandwhennottobother

• PreparingforUncertaintyforTomorrownFocusonpreparationratherthanpredictionnInvolvelargergrouponscenarioplanning

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Questions, Comments, Suggestions?

“Wilson&Dexter– disruptingthedominantdesigndaily”YankeeGoldenRetrieverRescuedDogs(www.ygrr.org) 78

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