ForecastingInternationalFlowsofReturnableTransportItems

By:PatrickJacobsandRajdeepSingh

Advisor:Dr.EvaPonce

Agenda

2

IndustryOverview

Background

ProjectScope

Methodology

Forecasting

KeyTakeaways

FutureResearchAreas

WhatisaReturnableTransportItem?

3

RTILeasingOverview

4

LogisticsServicesCompany

Customer

TierIICustomer

TierNCustomer

RTILeasingOverview

5

ServiceCenter

Customer1st Tier

Customer2ndTier

ServiceCenter

Customer1st Tier

Customer2ndTier

ServiceCenter

ServiceCenter

FlowServiceCentertoCustomer

CustomertoCustomer

CustomertoServiceCenter

Background

5

Background

6

Background

7

ProjectScope

KeyQuestion• DeterminehowForeignExchangeRateswouldalterdirectionof

ProductFlowsbetweenCanadatotheUS

ProjectObjectives• Develop1-monthaheadforecasttopredictInternationalFlows

betweenCanadaandUSA

• Utilizingmacroeconomicfactorsaspredictivevariables

9

ForecastingwithMacroVariables

VariablesSelectionCriteria

• Relevant• ReadilyAvailable

10

Country Variable Aggregation Level

USA&CA #2 DieselPrices Monthly

USA&CA ForeignExchangeRate Monthly

USA ExportstoCA Monthly

USA ImportsfromCA Monthly

USA GoldPrices Monthly

USA&CA GDP Quarterly

Methodology

• Monthlyvariablelagsrangefrom1to12months• Quarterlyvariablelagsrangefrom3to12months

Results

11 *Correlations where X>.6 or X<-.6

Movement VariableLag

(Months) Correlation

CAtoUSAUSQuarterlyGDP 12 0.7197CanadaQuarterlyGDP 12 0.6868AverageCAtoUSDFEx 5 -0.6995

USAtoCA -- NetInternational --

ForecastingwithMacroVariables

12

ForecastingFramework

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Identify Accuracy Metrics (MAPE, MAD, MASE)

Identify Relevant Endogenous and Exogenous Independent Variables & Time Lags

Evaluate Stationarity & Level, Trend Seasonality pattern in

Data Set

Subtract the Forecasted US-> CA and CA->US to get ∆ Flows

Choose Top Forecasting Models for ∆ Flows

Develop Univariate & Multivariate Forecasting Methods for US->CA

Approach 2: Best of Breeds

Develop Univariate & Multivariate Forecasting Methods for CA->US

Approach 1: Same Forecasting Method

DataAnalysisLevel,Trend,Seasonality

• Level– meanvalueofY

• Trend– Localmean,periodtoperioddifference

• Seasonality– Repeatingincreaseordecreaseinagiventimeperiod

14

DataAnalysisTrend

Aslightlineartrendexistsintheindividualflows

15

DataAnalysisSeasonality

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ForecastingModels

• StepwiseRegression• Univariate• Multivariate

• Endogenous• Exogenous(MacroEconomic)

• SARIMASeasonalAutoRegressiveIntegratedMovingAverage

• ExponentialSmoothing- Multiplicative• Standard• StateSpace

17

ForecastingMethodologies

Approach1– UsesamemethodologyforUSAtoCanada&CanadatoUSA

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𝒀"𝒅𝒆𝒍𝒕𝒂 = 𝒀"𝑼𝑺𝒕𝒐𝑪𝑨 −𝒀"𝑪𝑨𝒕𝒐𝑼𝑺

Seasonal Exponential

Seasonal Exponential

Predicted value of International flow

Approach 2 – Use top performing individual forecasts for USA to ……….. Canada and Canada to US to select “Best of Breed”

𝒀"𝒅𝒆𝒍𝒕𝒂 = 𝒀"𝑼𝑺𝒕𝒐𝑪𝑨 −𝒀"𝑪𝑨𝒕𝒐𝑼𝑺

Seasonal Exponential

Simple Regression

Predicted value of International flow

PerformanceEvaluationMetrics

PerformanceIsMeasuredbyRelativePerformanceonAllThree

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𝑀𝐴𝑃𝐸 =𝐸𝑡𝐴𝑡

𝑀𝐴𝑆𝐸 =𝐸𝑡

|𝐸𝑡789:;|𝑀𝐴𝐷 = |𝐸𝑡|

Mean Absolute Percent Error

Mean Absolute Scaled Error

Mean Absolute Deviation

Errorinrelationtoactualvalue

ErrorinrelationtoNaiveFt

error

AbsoluteUnitError

PerformanceEvaluationMetrics

Issue:ImperfectMetrics

Solution:CompositeScores• Weighseachmetricevenly&comparesmodels

performancesacrossall3metrics

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MAPERankMultiplicative: MASERank MADRank

✚

**

Mean: MAPERank MASERank MADRank（ )3

✚

ModelSelectionQuantitativeSelection

21

Multiplicative Score RankValidation Rank

Model MAPE MASE MAD MAPE MASE MAD MeanScore Mult ScoreSARIMA(0,1,1)(0,1,0)|SARIMA (0,1,1)(1,1,0) 15.3% 3.84 37340 1 28 1 10.00 28

SeasonalExponential |SimpleRegression 15.8% 3.76 40211 2 26 4 10.67 208

Holt-Winter|SimpleRegression 16.8% 3.63 39365 6 21 2 9.67 252

SeasonalExponential|SeasonalExponential 16.9% 3.27 40314 7 10 5 7.33 350

SeasonalExponential |EndogenousRegression 16.3% 4.09 39895 4 33 3 13.33 396

Validation Rank

Model MAPE MASE MAD MAPE MASE MAD MeanScore Mult ScoreSeasonalExponential|SeasonalExponential 16.9% 3.27 40314 7 10 5 7.33 350

Holt-Winter|SimpleRegression 16.8% 3.63 39365 6 21 2 9.67 252

SARIMA(0,1,1)(0,1,0)|SARIMA (0,1,1)(1,1,0) 15.3% 3.84 37340 1 28 1 10.00 28

SeasonalExponential |SimpleRegression 15.8% 3.76 40211 2 26 4 10.67 208

SimpleRegressionM2Y|SimpleRegression 16.2% 3.69 41462 3 23 6 10.67 414

Mean Score Rank

Differentmodelsselectedwhenusingdifferent

compositescores

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Seasonal Exponential | Seasonal Exponential was selected due to quantitative and qualitative performance

Model MeanScore MultScore

UpdateRequirement

SoftwareDependency

SeasonalExponential|SeasonalExponential 7.33 350 1 1

Holt-Winter|SimpleRegression 9.67 252 2 1

SARIMA(0,1,1)(0,1,0)|SARIMA (0,1,1)(1,1,0) 10.00 28 1 2

SeasonalExponential |SimpleRegression 10.67 208 2 1

SimpleRegressionM2Y|SimpleRegression 10.67 414 2 1

SeasonalExponential |EndogenousRegression 13.33 396 3 1

SeasonalExponential |SimpleRegressionM3Y 13.67 1015 2 1

ModelSelectionQualitativeSelection

KeyTakeaways

1. Macrovariables*arenoteasilytiedtomicroleveldata2. Methodicalforecastingidentification3. Timehorizonsgreatlyeffecttimeforecastevaluationandperformance

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Metric SeasonalExponential SARIMA SE PerformanceDifference

MAPE 16.79% 15.3% -8.87%

MASE 3.62 3.83 5.80%

MAD 40492 37340 -7.78%

Metric SeasonalExponential SARIMA SE PerformanceDifference

MAPE 8.23% 4.68% -43.07%

MASE .67 .39 -42.58%

MAD 40492 37340 -43.19%

Aggregation: Monthly

Aggregation: Yearly

BenefitsforReverseLogisticFirms

• IncorporatingSeasonalityinInventoryPlanning

• StrategicPlanningforDemandUncertaintyinReverseLogistics

• MinimizeRTIRepositioningFlowsTransportationCosts

• ImproveBalancingofRTIFlowsacrossNetwork

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NextStepsUS Origin States - SpreadOrigin US State- Destination Province

DistributionOrigin California – Destination Province

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FutureResearchAreas1. ForecastingofRTIFlowsatmoreGranularLevel2. MinimizeTransportationCostsbyReducingRTIRepositioning

• PlanFlowstoServiceCenters:StatewideMixforDestinationFlows3. TailorPricingandLeasingContractsusingHistoricalCrossBorderRTIflows

ServiceCenter

Customer1st Tier

Customer2ndTier

ServiceCenter

24

FutureResearchAreas1. ForecastingofRTIFlowsatmoreGranularLevel2. MinimizeTransportationCostsbyReducingRTIRepositioning

• PlanFlowstoServiceCenters:StatewideMixforDestinationFlows3. TailorPricingandLeasingContractsusingHistoricalCrossBorderRTIflows

ServiceCenter

Customer1st Tier

Customer2ndTier

ServiceCenter

24

ReferenceSlides

DataAnalysisForecastability - CV

30

Low Variation Within Sets –High When Combined

Low Variation Within Sets –Consistent When Combined

High Variation Within Sets –High Variation When Combined

CA to US is the easiest flow to predict as the variation is consistent over time

Training Validation Total

UStoCA 12.27% 10.77% 16.17%

CAtoUS 13.17% 12.32% 12.21%

Delta 17.13% 24.58% 19.68%

𝐶𝑉 =𝜎𝜇

EndogenousVariables

• MonthlyNetworkPurchasesofPallets• DomesticMonthlyRTIIssued• DemandGrowth• RTIReturnstoServiceCenters

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Movement Variable Monthly LAG CorrelationUSA - Domestic Issues 3 0.791CA - Domestic Issues 3 0.783USA Inflows 3 0.771CA Inflows 3 0.766USA Inflows 6 0.785USA - Domestic Issues 6 0.743USA - Domestic Issues 3 0.672CA Inflows 6 0.659

Net International -- --

CA to USA

USA to CA

CAtoUS