Integrated Analytical Methods, Systems, Process in IMC’sMichael F. Gorman, Ph.D.University of Dayton,MFG Consulting

INFORMS General Meeting –Phoenix October, 2012

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Integration of Methods Systems, Processes at IMCs

1. Background/Introduction: The Industry and Situation

2. Strategic Operations Research Modeling: The Justification

3. Real time Decision Support: The ChallengeIntegrated suite of five modules using a variety of methods:

1. Supply and Demand Forecasts2. Capacity Valuation Model3. Load Accept Optimization4. Fleet Inventory Target Setting5. Load Routing Optimization

4. Extensions, Lessons learned, other applications

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The five stages of analytical competition:Where are the IMCs?

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2) Analytics Methods Modeling The growing need for OR at IMCs

• IMCs are becoming more asset based as RRs shed container assets– Shift in industrial organization, cost structures, decision rules

• Previous decision rules:– Load acceptance: Take all profitable moves– Load routing: Take lowest cost (available) route and equipment

• With a fixed fleet, these rules leads to missed profitable loads, and undesirable situations with routing

• Change in philosophy:– “Network view” rather than a “one way” view of each load– Not all profitable moves are good moves:

• Repositioning costs – must balance fleet, unlike with rail-owned• Opportunity costs - allocation of scarce, fixed fleet resources

– Lowest “one way” cost does not lead to lowest network cost• Consider better opportunities for alternative orders at origin• Equipment value from subsequent loads at destination

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2) Strategic Fleet Allocation Model (FAM) Conceptualization

HistoricalContainer Traffic Flows

RecommendedFleet Assignments & UtilizationFleet

Contract Provisions

Repositioning Costs and Times

Transit andStreet Times

Fleet AllocationModel (FAM)

Network Profitability

Ramp Imbalances andRepositioning Cost

Inputs OutputsModel

Strategic modeling shows the benefit, justifies the investment in real-time systems.

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3) Real Time Decision Support Project Scope Considerations and Constraints

• We wanted to take a conservative approach to an aggressive project– Relatively small company– Relatively new to OR-based DSS

• To reduce project disruption, risk and cost, we applied these constraints to our approach:

– Organizational:• Business Process cannot change• Marketing-centric Customer Service Representative (CSR) and

Operations-centric Dispatcher responsibilities cannot change

– Technological: optimization technology must fit within existing production system and data structures

• In particular – “transactional”, one-at-a-time order processing (both acceptance and dispatching)

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3) Real Time Optimization – Organization and Process Load Accept and Routing Decisions are Sequential

Load Accept Optimization

(LAO)Customer

Service Rep

Load Routing Optimization

(LRO)Dispatcher

First, the Load Acceptance decision (Marketing Decision)– Should Customer Service Representative (CSR) accept a tendered load? To maximize contribution of scarce resources

Second, the Load Routing decision (Operating Decision)– Which equipment/railroad should dispatcher choose? To minimize cost, given a set of accepted loads

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3) Real Time Optimization: Technological Architecture Overview and Information Flow

ProductionTransactionSystem

Demand And SupplyForecasts

LoadAcceptOptimization

LoadRoutingOptimization

Capacity ValuationModel

User InterfaceBusiness Process

Fleet InventoryTargets

NetworkOptimizationProgram(NOP)

Fleet AllocationModel

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3) Real Time Optimization Modeling Component Overview

ForecastsLAO LRO

CVM FIT

Demand And SupplyForecasts

LoadAcceptOptimization(LAO)

LoadRoutingOptimization(LRO)

Capacity Valuation(CVM)

Fleet InventoryTarget (FIT)

Network Optimization Program (NOP) suite of integrated tools

Daily forecasts with real time updates

CSR Dispatcher

Seasonally, set daily inventory targets for container fleetsDaily estimates of the value

of capacity at a location on a date; updated with changing supply

Real-time yield management tool determines load acceptance thresholds

Assigns accepted loads to equipment and routes in a least-cost way

LAO and LRO interact directly with CSR and dispatcher.Novel methods are detailed in the CVM and FIT support modules.

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3.1) Daily Supply and Demand Forecasts

• Container Supply forecast is in two parts:– “Controlled” capacity – Timing of current and near-term customer

releases each day – quantity is known, timing is not– “Predicted” capacity - “Street Sourced” – expert opinion on

available rail-owned supply off the “street”

• Demand Forecast:– Two week horizon of daily orders– Time-series based demand forecast – Hundreds of estimated equations– Aggregate forecast of smaller lanes so that all demand is forecasted

• Critical inputs for decision support models; Valuable new source of information for managers

• Challenge: What is the appropriate level of forecast detail to support decision makers/models, but maintain accuracy?

ForecastsLAO LRO

CVM FIT

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3.2) Capacity Valuation Model – CVM Overview

• Capacity Valuation Model – CVM– Establishes the value of an additional unit of capacity – Output is Capacity Valuation Curve (CVC) –

relationship between expected margin and capacity

• Capacity Valuation Curve (CVC) depends on – Demand Forecast and forecast error distribution– Margin distribution of market segments and ODs out of an origin

• Capacity Value (CV) is a point on the CVC at a point in time– Value is measured by the expected profit potential generated by the

unit of capacity

• CV is an input to LAO and LRO– for both establishing the opportunity cost of accepting a load at origin,

and – for estimating the value of capacity at destination

ForecastsLAO LRO

CVM FIT

3.2) CVC Illustration

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$-

$50

$100

$150

$200

$250

$300

$350

$400

$450

$500

1 2 3 4 5 6 7 8 9 10

Expe

cted

Pro

fitab

ility

Container Supply

Capacity Valuation Curve

The CVC captures the diminishing marginal value of container capacity as capacity on a date increases.In this example, supply of 6 has CV=$72.30

ForecastsLAO LRO

CVM FIT

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3.3) Load Acceptance Optimization (LAO) Overview of Methodology

• Load Acceptance Optimization maximizes the anticipated value of a tendered load against other future potential loads at that origin

• Accept a tendered load if: Load Value >= Acceptance Threshold:

Tendered Load Profitability + Tendered Load Destination CV >=

Origin Load CV + Average Destination CV

• The simple decision rule captures the opportunity costs of accepting a customer order as determined by the CVC

• It also includes the future potential created by the accepted load at the load’s destination, as compared to other loads’ future potential

ForecastsLAO LRO

CVM FIT

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3.4) Target Inventory Setting Conceptualization

• Target Fleet inventory balances the cost of a Fleet box against the increased profit potential of a Fleet over other equipment types

• Target Inventory is greater than zero because:– Fleet cost advantages– Mismatch in timing of inbound vs. outbound pattern of fleet flows – Variation in IB (supply) and OB (demand) patterns

• A Unique Two-step Algorithm was developed:– 1) Target inventory setting - ideal inventory of fleet containers to

maximize the incremental profit potential over other capacity sources– 2) Cost of Inventory Target Deviation - establish shortage and surplus

costs

• Targets are set monthly as demand patterns change; they are used daily in LRO for making routing decisions

ForecastsLAO LRO

CVM FIT

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3.4) Target Inventory Setting Cost of Deviation from Target Inventory

Step 1: Establish Fleet Target Inventory (units)

Failed Service toCustomer

Higher-Cost Rail-OwnedContainerAssignments

Fleet Assigned to Suboptimal LanesDue to excess Supply

Fleet RepositionedDue to highSurplus

We establish a fleet inventory target and estimate a cost of deviation from that target.

Cost ($)

ForecastsLAO LRO

CVM FIT

3.5) Load Routing Optimization Formulation: Integration of all the pieces

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Minimize cost

N

i

M

j1 1

K

k

T

t1 1

(Cijk + TIikt, + TIjkt - CVjkt + Invjkt + SVjkt ) xijkt (1)

Subject to:

K

k 1

T

t 1

xijkt = Dijt for all (i,j) (2)

M

j

T

t1 1

xijkt <= Sikt for all k, for all i (3)

FIT Orig/Dest Target inventory

CVM Estimated Capacity Value at Destination

Inventory and Service failure costs

One-way cost of shipment

ForecastsLAO LRO

CVM FIT

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4) Successes and Lessons Learned

• What we did well:– Delivered wins early; earned quick paybacks

• Strategic model “paid for” more aggressive production models;Forecasts were used for planning months before optimization was in place

– Mapped the business process, information flow, decision points and systems integration before modeling

• Stayed on scope, under budget – increased returns– Stayed small for minimal disruption - reduced risk

• No change to the organization, the load tendering process, or the system’s user interface

• Lessons we learned– We could have started with simpler costing, then added

complexity – Simplicity gains confidence– compliance is hard.

(and some sophisticated costing has little impact on decisions)– Incurring near-term costs to obviate future costs or gain future

benefits is challenging in an economic downturn!

4) Extensions

• Assignment of equipment to load also affects origin and destination dray

• Dray carriers/location/box holding affects network economics• Requires

– Very detailed data– Very detailed forecasts– Longer look-ahead– Managing shipper loading behavior– Managing dray company behavior

• Bid response/Pricing– Very challenging to price given dramatic change in cost structure due

to network effects when a change in product mix– Estimation of customer response to pricing initiatives a challenge– Especially true in an all-or-nothing bid style pricing problem

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Thank you!

Michael F. Gorman, Ph.D.University of Dayton

Phoenix INFORMSOctober, 2012

Flows including dray moves increase problem size

• The number of OD pairs will be much larger, and will depend on the level of detail for customer/consignee ‘location’– Ramp area– Dray zone– City/state/zip location– Customer lat/long

• How we estimate dray efficiency changes with ‘location’ specification (more specific gives better estimate of dray efficiency)– Aggregated location:

• Imbalance Based – net flow balance contributes to efficiency – Specific location:

• Triangularization – pairing IB/OB loads; empty move distance

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Customer Specific Locations

• Centroid of Dray zone or zip code might misstate specific cross-region dray distances/costs– Both overstate for proximate locations, and understate for remote ones

• Specific customer locations allow better estimation of empty miles, costs

CHI

Customer

Customer

Dray Zone

Dray Zone

City/St/Zip

City/St/Zip

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Potential Problems with More Specific “Locations”

• Data sets grow – – Increasing data management/data quality issues– Increasing challenge of results interpretation

• Data requirements grow –– E.g., Lat/long of ramp, customer/dray area/city/state/zip– Estimates of distance, dray costs for a large number of pairings

• Assumptions surrounding feasibility grow – – Customer1-Customer2 pairing in ‘plan’ – will it happen?– Seasonality, day of week, delivery/pickup windows, etc.

• Assumptions surrounding constraints grow – – Can we ‘never’ dray two different box types in the same move?

More demanding data and modeling to get more ‘realistic’ outputsCan create ‘brittle’ models that fall apart when data is bad or assumptions are violated.

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More detailed destination location

• For “Major” Ramps, Dray Zones, Customers:

• A Move is Shipper/Ramp - Consignee/Ramp–

• 700 Ramp pairs can become 7000 Shipper-Consignee pairs

CHI

Shipper

Dray Zone

Dray Zone

City/St/Zip

City/St/ZipLAX

Consignee

Shipper

Dray Zone

Dray Zone

City/St/Zip

City/St/Zip

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Consignee

Considerations for level of specificity for location

• Volume– If there is not high volume, then any anticipated triangularization is

suspect – need large numbers to be predictable– Also, with low volume, potential impact on economics is lowered

• Proximity to Ramp– For locations near ramp, empty moves are not as costly, and

potential for dray efficiency gains lower• Geographic size of locations

– If a location encompasses a small geographic region, gains from more specific location definition is diminished

• Proximity/Interaction of locations– The level of proximity and actual or possible interaction between

locations drives the need for more specificity

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