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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