summary of first section: deterministic analysis
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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. - PowerPoint PPT PresentationTRANSCRIPT
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Summary of First Section:Deterministic Analysis
John H. Vande VateSpring, 2007
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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
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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
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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
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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
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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
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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
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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
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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
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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 day2 days2 days
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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
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The Exam
• Laptops not permitted• 4-5 questions• Did you understand?• Can you interpret for the business?• Some modeling
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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