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Summary of First Section: Deterministic Analysis

Summary of First Section: Deterministic Analysis. John H. Vande Vate Spring, 2007. Introduction to modes and transportation rates There are economies of scale in transportation costs Consolidation helps us capitalize on these economies of scale. Where We’ve Been.

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Summary of First Section: Deterministic Analysis

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  1. Summary of First Section:Deterministic Analysis John H. Vande Vate Spring, 2007 1

  2. Introduction to modes and transportation rates There are economies of scale in transportation costs Consolidation helps us capitalize on these economies of scale Where We’ve Been 2

  3. Introduction to Finance & SCM Economic Profit Focus on Working Capital Days of Inventory Days Sales Outstanding Days Purchases Outstanding Cost of Holding Inventory Capital charge Non-capital charge Where We’ve Been 3

  4. Transportation & “Deterministic” Inventory Pipeline Inventory Cycle Inventory Simple Example to illustrate How to estimate, transportation & inventory costs The “magic” of consolidation The EOQ: Balancing Transport & Inventory costs Network Models Quick review of network flows Adding reality Weight & Cube Concave costs Some aspects of Time Where We’ve Been 4

  5. Consolidation Consolidating LTL shipments Costs Basic model Integrality?: Should assignments of customers to consolidation points be binary? Integrality? In Favor: Simplicity. Against: Reality Where We’ve Been 5

  6. Our assumption: Annual demand is evenly spread across the year (No seasonality, No variability) The Reality: Individual customer demands vary widely from day-to-day, week-to-week, month-to-month The Impact: We plan to run full trucks In reality sometimes they are not full, other times there’s more than they can carry. Our model ignores this we do incorporate a load (fudge) factor Reality 6

  7. Multi-Stop Routes Long LTL shipments to capture enough demand XD Shorter LTL shipments, but poorer utilization of the trucks Where We’ve Been XD Fixed cost: 156 trucks Plant 7

  8. Multi-Stop Routes Use Column Generation to find a small set of good multi-stop routes Two Complications A Route entails several variables RouteVolume: how much volume we carry on this route for a given consolidation point MultiStopTrucks: how many trucks we run on this route What columns do we generate? The constraints in the Master problem that relate MultiStopTrucks to RouteVolumes Normally in Column Generation we don’t add constraints as we add columns. Case 1: Constraint is not relevant Case 2: Constraint is tight Where We’ve Been 8

  9. Load-Driven Consolidation When we are concerned about cost of transportation first, then level of service Low value, thin margins, high volume Consolidate to improve service Full truck load to each store is Impractical (small format stores) Creates too much (cycle) inventory Forces us to forecast demand at the store level far in advance Where We’ve Been 9

  10. Where We’ve Been • Objective is transport costs • Line haul to pools • Delivery from pools to stores • Service as a constraint • Trailer Fill: Max Time to Fill Trailer • Example: OTD < 6 days • Order processing: 1 day • Batching & Picking: 1 day • Line Haul: 3days • Trailer Fill 2 days 1 day 2 days 10

  11. Location: We assumed the choices for potential consolidation were given How do we identify good choices? Stochastic Analysis Introduction to Stochastic Variability Retail Pricing: Markdowns as a % of Sales have risen steadily to over 30% Sport Obermeyer The relationship between forecasting, sourcing, and markdowns Managing Inventory: Replenishment Postponement & Push vs Pull Applications BMW and the Bullwhip Effect Your projects Where We’re Going 11

  12. Laptops not permitted 4-5 questions Did you understand? Can you interpret for the business? Some modeling The Exam 12

  13. Define your variables and parameters clearly, give units. Use clear mnemonics Brief description of what each constraint accomplishes Clear and unambiguous indexing Pseudo AMPL is fine Expect to need to read (but not produce) AMPL models Models 13

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