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Frequency Project BMW Georgia Tech

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Frequency Project BMW Georgia Tech

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    1. 1 Frequency Project BMW � Georgia Tech April 13th 2006

    2. 2 Outline Project background Problem description and objective Approach Results Recommendation for BMW Future work

    3. 3 European Parts Supplies 40% of parts needed by (Plant 10) Spartanburg are sourced in Europe and shipped across the Atlantic via: regular ocean shipments Airfreight expediting in case of stock-out at Spartanburg plant

    4. 4

    5. 5 Current Case Frequency: Three times per week Arrival Days: Thursday Friday Saturday

    6. 6 Project description

    7. 7 Major variable elements Changing the European (American) ports affects land lead times Frequency of shipments affects needs for inventory at plant Parts proportions (Split) to be shipped in each scheduled shipment may reduce stock-out? Shipping lines have different rates per container, shipping lead times and reliability

    8. 8 Approach used Collection of sailings data Creation of a tool to search among the large number of Sailings Selection of multiple optimal ports and lines combination for various frequencies (based on ocean and land lead time) Simulation to find costs incurred with the different scenarios Processing Simulation Outputs in Excel

    9. 9 Collection of Sailings Ports were selected based on: Ranking in terms of TEU of European ports Ports preferred by BMW Duration of Sailings offered Geography Data obtained from www.joc.com1 (current Tender) and material �Trans Atlantic Workshop� provided by BMW (new Tender) 1sometimes data not accurate

    10. 10 Constraints on Sailings Cutoff for sailing time: 18 days Entry Ports considered : Savannah, Norfolk, Charleston, New York, Montreal, Newark, Baltimore, Philadelphia, Miami, Houston, Halifax European Ports considered: Hamburg, Antwerp, Bremenhaven, Le Havre, Rotterdam, Copenhagen, Fos, Genao, Gioia Tauro, La Spezia, Le Verdon, Livorno, Montoir, Valencia, Algeciras, Barcelona

    11. 11 General Assumptions If it arrives in port on Saturday or Sunday it cannot be shipped until Monday (high extra charge if pulling out of port on weekends) We are not considering multiple arrivals at Spartanburg on the same day High and Low runners can not be mixed on a container Capital Charge: 12 % Non-Capital Holding Charge applied in Spartanburg: 5% (High Runners) 10% (Low Runners)

    12. 12 Port Selection Tool (Excel Model) Assignment Model to minimize lead times under each scenario. Each scenario is a combination of: Various ports in Europe Various ports in the US Various shipping lines used Different weekdays of arrival

    13. 13 Port Selection Tool (Assignment Model in Excel)

    14. 14 Simulation Input Parameters used Holding/carrying cost: Values of Engines Detailed Expediting costs Major points included in the simulation Demand uncertainty (difference between forecast and actual usage) Ocean lead time variability Lead time variability between US-port and Spartanburg

    15. 15 Simulation in Arena (1)

    16. 16 Simulation in Arena (2)

    17. 17 Simulation in Arena (3)

    18. 18 A Simulation with 500 runs 1000 days each was performed for each engine for each scenario The average values for key variables were obtained by aggregating data in Excel Transportation costs were calculated in Excel using the simulation output Costs were summed to total figure

    19. 19

    20. 20 Split Split policies were simulated for: One high and one low runner engine For a frequency of three

    21. 21

    22. 22

    23. 23

    24. 24 Recommendations

    25. 25 Total Cost incl. Transportation

    26. 26 Future Work Improve model to consider only integer number of container loads Come up with a policy concerning mix of parts on a container Simulate other plant 10 parts Improve modeling of safety stock More sensitivity analysis Risk reduction by having multiple arrivals on one day

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