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Integrated Analytical Methods, Systems , Process in IMC’s. Michael F. Gorman, Ph.D. University of Dayton, MFG Consulting INFORMS General Meeting –Phoenix October, 2012. Integration of Methods Systems , Processes at IMCs. Background/Introduction: The Industry and Situation
Integrated Analytical Methods, Systems, Process in IMC’s Michael F. Gorman, Ph.D. University of Dayton, MFG Consulting INFORMS General Meeting –Phoenix October, 2012
Integration of Methods Systems, Processes at IMCs • Background/Introduction: The Industry and Situation • Strategic Operations Research Modeling: The Justification • Real time Decision Support: The ChallengeIntegrated suite of five modules using a variety of methods: • Supply and Demand Forecasts • Capacity Valuation Model • Load Accept Optimization • Fleet Inventory Target Setting • Load Routing Optimization • Extensions, Lessons learned, other applications
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
2) Strategic Fleet Allocation Model (FAM) Conceptualization Inputs Model Outputs Historical Container Traffic Flows Recommended Fleet Assignments & Utilization Fleet Contract Provisions Network Profitability Fleet Allocation Model (FAM) Repositioning Costs and Times Ramp Imbalances and Repositioning Cost Transit and Street Times Strategic modeling shows the benefit, justifies the investment in real-time systems.
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)
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
3) Real Time Optimization: Technological Architecture Overview and Information Flow Production Transaction System User Interface Business Process LoadAccept Optimization Load Routing Optimization Network Optimization Program (NOP) Demand And Supply Forecasts Capacity Valuation Model Fleet Inventory Targets Fleet Allocation Model
3) Real Time Optimization Modeling Component Overview LRO LAO Forecasts CVM FIT Network Optimization Program (NOP) suite of integrated tools Real-time yield management tool determines load acceptance thresholds Assigns accepted loads to equipment and routes in a least-cost way CSR Dispatcher LoadAccept Optimization (LAO) Load Routing Optimization (LRO) Daily forecasts with realtime updates Demand And Supply Forecasts Capacity Valuation (CVM) Fleet Inventory Target (FIT) Seasonally, set daily inventory targets for container fleets Daily estimates of the value of capacity at a location on a date; updated with changing supply LAO and LRO interact directly with CSR and dispatcher.Novel methods are detailed in the CVM and FIT support modules.
3.1) Daily Supply and Demand Forecasts LRO LAO Forecasts CVM FIT • 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?
3.2) Capacity Valuation Model – CVM Overview LRO LAO Forecasts CVM FIT • 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
3.2) CVC Illustration LRO LAO Forecasts CVM FIT The CVC captures the diminishing marginal value of container capacity as capacity on a date increases. In this example, supply of 6 has CV=$72.30
3.3) Load Acceptance Optimization (LAO) Overview of Methodology LRO LAO Forecasts CVM FIT • Load Acceptance Optimization maximizes the anticipated value of a tendered load against other future potential loads at that origin • Accept a tendered load if: Load Value >= Acceptance Threshold: Tendered Load Profitability + Tendered Load Destination CV >= Origin Load CV + Average Destination CV • The simple decision rule captures the opportunity costs of accepting a customer order as determined by the CVC • It also includes the future potential created by the accepted load at the load’s destination, as compared to other loads’ future potential
3.4) Target Inventory Setting Conceptualization LRO LAO Forecasts CVM FIT • Target Fleet inventory balances the cost of a Fleet box against the increased profit potential of a Fleet over other equipment types • Target Inventory is greater than zero because: • Fleet cost advantages • Mismatch in timing of inbound vs. outbound pattern of fleet flows • Variation in IB (supply) and OB (demand) patterns • A Unique Two-step Algorithm was developed: • 1) Target inventory setting - ideal inventory of fleet containers to maximize the incremental profit potential over other capacity sources • 2) Cost of Inventory Target Deviation - establish shortage and surplus costs • Targets are set monthly as demand patterns change; they are used daily in LRO for making routing decisions
3.4) Target Inventory Setting Cost of Deviation from Target Inventory LRO LAO Forecasts CVM FIT Fleet Repositioned Due to high Surplus Cost ($) Failed Service to Customer Fleet Assigned to Suboptimal Lanes Due to excess Supply Higher-Cost Rail-Owned Container Assignments Step 1: Establish Fleet Target Inventory (units) We establish a fleet inventory target and estimate a cost of deviation from that target.
3.5) Load Routing Optimization Formulation: Integration of all the pieces LRO LAO Forecasts CVM FIT FIT Orig/Dest Target inventory One-way cost of shipment Inventory and Service failure costs CVM Estimated Capacity Value at Destination
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
Thank you! Michael F. Gorman, Ph.D. University of Dayton Phoenix INFORMS October, 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
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 Dray Zone City/St/Zip Customer CHI Dray Zone City/St/Zip Customer
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’ outputs Can create ‘brittle’ models that fall apart when data is bad or assumptions are violated.
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 Dray Zone City/St/Zip Dray Zone City/St/Zip Consignee Consignee CHI LAX Dray Zone City/St/Zip Dray Zone City/St/Zip Shipper Shipper
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