Next Wave Agency Ocean Carrier Bid Optimization Final Presentation

1. Next Wave AgencyOcean Carrier Bid OptimizationFinal Presentation April 16, 2008 Senior Design Team: Juan Araya Steven Butts Owen Carroll Emily Sarver Justin Stowe Jan Zhang Sponsor: John Trestrail, CEO & Principal Consultant jtrestrail@nextwaveagency.com Advisor: Dr. Ozlem Ergun ozlem.ergun@isye.gatech.edu *This presentation was created in the framework of a student design project. The Georgia Institute of Technology does not sanction its content in any way.

2. Outline • Company and Problem Background • Project Description • Solution Approach • Deliverables • Value and Benefits

3. Food for Peace Program • USDA & USAID • Invitation published monthly to procure: • Food commodities • Transportation • USDA awards contracts to minimize cost

4. Food for Peace Program

5. Food for Peace Program

6. Next Wave Agency Background • Specializes in Food for Peace Program • Ocean carrier consulting agency • Recommends bid prices to carriers

7. Project Introduction • Bids based on market knowledge • Wanted to develop an analytical method • Created a tool to determine bid prices

8. Project Flowchart User Input Supplier Bid Forecasting Optimization Model Analysis Carrier Bid Forecasting Bid Recommendations

9. Project Flowchart User Input Supplier Bid Forecasting Optimization Model Analysis Carrier Bid Forecasting Bid Recommendations

10. Solution Approach: Forecasting • Parameters for optimization model • Supplier bids • Origin ports • Commodities • Carrier bids

11. Solution Approach: Forecasting Supplier Bids • Data available • Winning and losing bids for past year • Bids are for a commodity at an origin • 3,750 pairs • 10 suppliers per pair • Supplier interaction

12. Solution Approach: Forecasting Supplier Bids Minimum bids for origin-commodity pairs Upward trend Non-seasonal

13. Solution Approach: Forecasting Supplier Bids • Double exponential smoothing • Non-seasonal data • Trend in minimum supplier bids over time • Low mean absolute percent error (5.62%)

14. Solution Approach: Forecasting Carrier Bids Data available Past winning carrier bids Past market indices for ocean freight costs Voyage lengths Past voyage factors highly variable Analyze past voyage data 3 carriers 10 vessels

15. Solution Approach: Forecasting Carrier Bids Estimate costs per ton Fuel Daily leasing Port call costs Method considered: Regression High R-squared (87-99.7%) Not adaptable for unusual voyages

16. Solution Approach: Forecasting Carrier Bids Use market costs to estimate profit per ton Past profit per ton fits normal distribution

17. Solution Approach: Forecasting Carrier Bids Method developed Calculate voyage costs using current indices Add random variable for profit per ton Calculate confidence intervals for bid prices

18. Solution Approach: Forecasting Carrier Bids Houston to Djibouti 20,000 tons Maersk vessel

19. Solution Approach: Forecasting Carrier Bids Confidence intervals for expected bid price

20. Project Flowchart User Input Supplier Bid Forecasting Optimization Model Analysis Carrier Bid Forecasting Bid Recommendations

21. Project Flowchart User Input Supplier Bid Forecasting Optimization Model Analysis Carrier Bid Forecasting Bid Recommendations

22. Solution Approach: Optimization Simulates USDA’s process of awarding contracts Objective: Minimize total cost Constraints: Carrier quantity Commodity demand Carrier-supplier pairing US vessel priority

23. Solution Approach: Optimization • Validated using actual data from 3 past invitations • Checked tonnage distribution among carriers • Average accuracy for each carrier: 91.7%

24. Solution Approach: Optimization Tested with forecasted supplier bids Change in error: <1%

25. Solution Approach: Optimization • Run model multiple times • Increment bid prices for Next Wave • Analyze results • Determine bid prices

26. Project Flowchart User Input Supplier Bid Forecasting Optimization Model Analysis Carrier Bid Forecasting Bid Recommendations

27. Project Flowchart User Input Supplier Bid Forecasting Optimization Model Analysis Carrier Bid Forecasting Bid Recommendations

28. Deliverables

29. Deliverables Expected Price

30. Deliverables

31. Deliverables

32. Deliverables

33. Deliverables

34. Deliverables

35. Deliverables

36. Value and Benefits • Financial value from increase in bids won • If 10% increase: • $550,000 for clients • $5,500 for Next Wave • Unique advantage over competitors • Potential to attract new clients

37. Summary Created analytical bidding tool Forecasting bids Optimization model Software User Interface Database Reports

38. Questions?