Frequency Project BMW – Georgia Tech

1. Frequency ProjectBMW – Georgia Tech April 13th 2006

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

3. European Part Supplies Outline Geographics 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 European Parts Supplies

4. Geographics European Part Supplies Current Case ~1-2 days ~1-2 days ~10 days ~1-2 days The Geographics Wackersdorf / Steyr Spartanburg

5. Current Case Geographics Project Description Frequency: Three times per week Arrival Days: Thursday Friday Saturday Current Case • European Departure Ports: • - Bremerhaven • US Entry Port: • -Charleston • Sailing Time: • -10 days • -12 days • -11 days

6. Project Description Current Case Variable Elements Project description Previous study suggests increasing the shipment frequency would decrease inventory costs Objective for this Project: • Suggest an optimal shipping schedule which • reduces costs related to European parts shipments • - Using real data and constraints • - Considering safety stock • - Considering Split • - Transportation Costs

7. Variable Elements Project Description Approach Used 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 Major variable elements

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 Variable Elements Data Collection Approach used

9. Data Collection Approach Used Constraints on 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 Collection of Sailings

10. Constraints on Sailings Data Collection General Assumptions 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 Constraints on Sailings Ports preferred by BMW

11. General Assumptions Constraints on Sailings Port Selection Tool 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. Port Selection Tool General Assumptions Assignment 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 Port Selection Tool(Excel Model)

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

14. Simulation Input Assignment Model Simulation in Arena (1) 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 Simulation Input Simulated were 5 different engines, three high and two low runners Other parts like transmissions to be simulated at a later time

15. Simulation in Arena (1) Simulation Input Simulation in Arena (2) Simulation in Arena (1) European Transit Assign Departure Port and Departure Date Travel to Exit Port Ship Arrival Travel to North America via Ship Exit Port North America Transit Travel to Spartanburg Entry Port

16. Simulation in Arena (2) Simulation in Arena (1) Simulation in Arena (3) Simulation in Arena (2) North America Total Costs Determine Shipment Size Update Pipeline Inventory Level Spartanburg Plant Demand Update Pipeline Costs Update on-hand inventory level and costs Calculate Total Inventory + Expediting Costs Are inventory levels positive? Update on-hand inventory costs Update Expedited Costs Update on-hand inventory level Define Daily Demand

17. Simulation in Arena (3) Simulation in Arena (2) Output Processing Simulation in Arena (3)

18. Output Processing Simulation in Arena (3) Graphs 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 Processing of Outputs

19. Graphs Output Processing Split *Note: Total cost does not include the transportation cost

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

21. Split Graphs(1) Split Split Graphs(2) Split Graphs Optimizing the split implies important savings in the Total Cost

22. Split Graphs(2) Split Graphs(1) Split Graphs(3) Split Graphs • Models were run using OptQuest Analyzer for Arena. • We defined variables (split variables) to identify the amount of engines (on a day basis) for each shipment route. • The variables implemented in the simulation then were evaluated minimizing total cost under changes in the split variables. • 500 replications of 1000 day runs were done for 100 scenarios to find the behavior of near optimal solutions. • Convergence can be seen from the results. • Best policy is inclined toward sending more through the fastest vessel, but trying to keep a homogeneous distribution among routes.

23. Split Graphs(3) Split Graphs(2) Recommendations Split Graphs • Vessel 2 refers to the fastest vessel in each case. • Different cases for two engines, related by equal and unequal interarrival times. • Split validates that the most should be allocated to the fastest vessel and the split is also trying to be more equally divided among shipments. • First case has variable delay upon arrival at Charleston for the second vessel, volume movement is not enough to make this vessel significantly more prone to more shipping.

24. Recommendations Split Graphs(3) Transportation Recommendations • Use frequency of shipments of four times a • week. • Move towards evenly spaced shipments. • Move towards more shipping routes • Unrestricted case, approximately 7% savings per engine based on Inventory cost+pipeline cost+expedited cost. • Transportation Costs

25. Transportation Recommendations Future Work Total Cost incl. Transportation *Note: Case 1 identifies the current case

26. Future Work Transportation 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 Future Work