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Outbound Logistics Optimization May 2009

Outbound Logistics Optimization May 2009. Miguel Juraidini Francis Wong. Agenda. Project Team. Sponsor: Mr. R. Sakaran Mentor: Mr. Veerabaskar Rohit Sarma. Project Overview. Outbound Logistics Optimization Understanding the distribution network Issues with outbound logistics

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Outbound Logistics Optimization May 2009

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  1. Outbound Logistics OptimizationMay 2009 Miguel Juraidini Francis Wong

  2. Agenda

  3. Project Team • Sponsor: • Mr. R. Sakaran • Mentor: • Mr. Veerabaskar • Rohit Sarma

  4. Project Overview • Outbound Logistics Optimization • Understanding the distribution network • Issues with outbound logistics • Modeling and simulation of processes

  5. DistributionNetwork 19 17 • 3 Plants • Hosur • Mysore • HP • 4 Zones • 20 Distribution • Centers • 600+ Dealers 16 18 20 14 13 11 15 12 10 4 5 9 8 7 6 3 Plant 2 Area Warehouse 6 1

  6. Many SKU’s available • 3 Product families • Mopeds • Motorcycles • Apache, Flame, Star • Scooters • Scooty • Over 70 different SKU’s

  7. ORDER DEALER PLANTS ALLOCATION D.C 1st 1st 20th 25th 28th DEALER

  8. ALLOCATION BILLED

  9. 83% Service Level (SKU) DEALER DEALER PLANTS ALLOCATION BILLED D.C 1st 1st 20th 25th 28th

  10. Project Focus • Understand existing distribution process • Create numerical models for: • SKU level allocation forecast • Simulation of vehicle distribution process • Help answer the questions: • Will there be enough vehicles (at SKU level) to meet allocation goals? • Will there be enough shipping capacity to deliver vehicles to dealers?

  11. Allocation Simulation -Historical allocation -Fast Vs. Slow moving SKU’s -Seasonality Effect -Percentage of dealers ordering -Production schedule and variability -Expected SKU allocation -Expected shortages -Expected ending inventory -Sensitivity Analysis Parameters Performance Spreadsheet Decision Model Decision Visibility

  12. Inputs

  13. Expected Allocation

  14. Shortages

  15. Ending Inventory

  16. Value • Increased Visibility • Coordination between allocation and production • Flexibility and agility • Improve SKU Service Level 1st Simulation Orders Billing Allocation

  17. Modeling Distribution Process

  18. Basic Structure

  19. Shipment Decision Bases On • Availability of vehicles • Availability of trucks for delivery • Availability of payment from dealer

  20. Uncertainties • Payment Availability • Which dealer will pay and when will they pay? • Truck Availability • Will a truck be available for delivery? • Transit time variability • Distance from Plant to Dealer/Warehouse varies. • Distance from Warehouse to Dealer varies. • Road and traffic condition varies.

  21. Model • Monte Carlo Simulation Model in Excel • Random shuffle of dealers to simulate the order of dealer payment • Use queuing model as the basis • Time between payment receive = interarrival time • Number of trucks available = no of process station available • Transit time = process time

  22. Creating the model • Entire system with 3 factories, 200+ dealers in the South Zone, 20 Area Warehouses and 400 dealers in East, North and West Zones too large. • Goal – a frame work of modeling the system • Start with modeling a small area warehouse • Continue with a larger area with multiple trucks

  23. Result • 2 models were built to demonstrate how to simulate the distribution process • First model – Uttarachal (North Zone) • One of the smallest area • 1 truck (21 vehicle capacity) • 5 dealers • Second model – Chattisgarh (West Zone) • 3 trucks (25 vehicles capacity) • 11 dealers

  24. Screenshot of UTT Model

  25. Screenshot of CHT Model

  26. Learnings from India • Hospitality Culture • Diverse but still one • Amazing driving skills • Must stop at Kamat on the way to Mysore • IPL Cricket

  27. Learnings from TVS • CSR • Serving emerging market • World class manufacturing operation

  28. Q & A

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