XYZ Company Supply Chain Optimization Project Network Optimization

1. ISyE 6203: Transportation and Supply Chain Management XYZ CompanySupply Chain Optimization ProjectNetwork Optimization Prepared By: Jayson Choy Christie Williams Andy Ang Thomas Ou Naragain Phumchusri Raghav Himatsingka Date: 04/25/2006

2. Agenda • Introduction • Key Deliverables • Data Analysis • Mathematical Model • Recommendation • Sensitivity Analysis • Conclusion

3. Introduction • Locations in Florida and California • Each location has Multiple Operations • Suppliers across USA • Supplier shipments may be parcel, less-than-truckload or full truckload, some must be frozen or chilled Project goal: Reduce inbound transportation costs across the business while meeting customer service requirements.

4. Timeline Phase Jan Feb Mar Apr Project kick-off & Deliverables Rationalization Data Cleansing & Validation Preliminary Modeling Validation of Model Generation of results & sensitivity Analyses

5. Agenda • Introduction • Key Deliverables • Data Analysis • Mathematical Model • Recommendation • Sensitivity Analysis • Conclusion

6. Key Deliverables • Create a graphic illustration of Current North American Supply Chain network • Document Current Volumes and Freight spend by mode to each location and in total • Identify and recommend North American Consolidation Points for most efficient route and capacity utilization • Create a graphic illustration of the Recommended New Supply Chain Network with all consolidation facility representations and conceptual lanes to each location

7. California and Florida Supplier Locations

8. Problem Definition Florida

9. Problem Definition 13% LTL: Greatest Opportunity for Savings 8% 36% 11% 6% 26%

10. Problem Definition • Focus on Consolidation of LTL shipments to Florida • Eliminated Frozen and Chilled shipments from the Optimization model • Included most FTL shipments by breaking them down

11. Agenda • Introduction • Key Deliverables • Data Analysis • Mathematical Model • Recommendation • Sensitivity Analysis • Conclusion

12. Data Analysis

13. Data Analysis

14. Data Analysis

15. Agenda • Introduction • Key Deliverables • Data Analysis • Mathematical Model • Recommendation • Sensitivity Analysis • Conclusion

16. Supplier Locations (LTL Florida)(Data Aggregation by 3-Digit Zip Code)

17. LTL LTL LTL FTL LTL LTL LTL LTL LTL LTL LTL To Consolidate LTL Shipments into FTL Present Situation Shipper Shipper CP Shipper Shipper Proposed Solution XYZ Company

18. Proposed 2-Step Model To Generate Potential Consolidation Point Candidates Step 1: Set Covering Model (SCM) Step 2: Network Design Model (NDM) To Determine which Consolidation Points to Open/ Close

19. Step 1: Set Covering Model (SCM) • Integer Programming Model • Model will decide 30 Potential Consolidation Points within 300 mile Radius • from Suppliers. • Maximize sum(i in Suppliers) y[i] • s.t {sum(i in Suppliers) x[i] <= 30; forall(i in Suppliers) • y[i] <= (sum(j in Suppliers) Matrix[i,j]*x[j]) To Maximize the Number of Suppliers which can be Covered by the CPs To Generate at most 30 Potential CP locations To Ensure that CPs are within 300 mile radius from Suppliers

20. SCM Results: 30 Consolidation Points Next Step: CP Candidates will be fed into the Network Design Model (NDM)

21. Step 2: Network Design Model (NDM) • Model Objectives : • To Decide which Consolidation Points to open or close • To Determine whether Suppliers should Ship Direct to the company • To Assign Suppliers to Consolidation Points To Open or Close? To Open or Close? To Open or Close? To Open or Close? To Open or Close? To Open or Close? To Open or Close? To Open or Close? To Open or Close

22. Objective : To Minimize Total Transportation Costs (Direct shipments + Shipments via opened CP) • Constraint I: If Supplier is not a Candidate CP • We either serve this 3 Digit zip via LTL shipments to the • destination or via a consolidation point LTL Shipment CP Shipper Direct LTL Shipment XYZ Company

23. FTL CP LTL Step 2: Network Design Model (NDM) Constraint II: If Supplier is a Candidate CP • Case 1: If NOT OPEN We send LTL direct or via a designated CP Case 1 XYZ Company CP

24. FTL Step 2: Network Design Model (NDM) Constraint II: If Supplier is a Candidate CP • Case 2: If OPEN We consolidate at CP and send FTL direct Case 2 XYZ Company CP

25. 0.8 * Step 2: Network Design Model (NDM) Constraint III: Load Factor of 0.8 • Total Inflow into CP < = [ 0.8 * Total Truckloads ] LTL LTL CP LTL ie at least 1 truckload per week Constraint IV: Frequency • Minimum Truckloads going through an Open CP per year > = 52

26. Agenda • Introduction • Key Deliverables • Data Analysis • Mathematical Model • Recommendation • Sensitivity Analysis • Conclusion

27. 5 CP Locations

28. Assignment of Suppliers

29. CP Location I: Charlotte, NC

30. CP Location II: Atlanta, GA

31. CP Location III: Los Angeles, CA

32. CP Location IV: Gulfport, MS

33. CP Location V: Jackson, KY

34. LTL Direct Shipments

35. Cost Savings 8% Reduction Less than 8% Reduction

36. Summary

37. Agenda • Introduction • Key Deliverables • Data Analysis • Mathematical Model • Recommendation • Sensitivity Analysis • Conclusion

38. Sensitivity Analysis

39. Sensitivity Analysis

40. Agenda • Introduction • Key Deliverables • Data Analysis • Mathematical Model • Recommendation • Sensitivity Analysis • Conclusion

41. Conclusion • Key Learning • Counter intuitive peculiarities of LTL cost structure (small volumes, backhauling …etc) • Moving Forward • “Milk run” study on remaining LTL direct volumes • Optimization of other shipment modes (e.g. parcel, frozen, chilled …etc) • Optimization of Florida bound shipments

42. Q & A