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

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

3 3 Where We’ve Been 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

4 4 Where We’ve Been 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

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

6 6 Reality 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

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

8 8 Where We’ve Been 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

9 9 Where We’ve Been 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

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: 3 days –Trailer Fill 1 day 2 days

11 Where We’re Going 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

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

13 Models 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