1. 1 Supply Chain & Logistics Institute Don Ratliff Executive Director - Supply Chain & Logistics Institute Regents and UPS Professor of Logistics don.ratliff@gatech.edu Developing Data Repositories “Logistics Analyst Perspective”

2. Warehousing & Distribution Global Transportation Manufacturing Logistics Perishables & Cold Chains Resource Scheduling Supply Chain Strategy Health & Humanitarian Logistics Logistics Research & Innovation Centers Supply Chain & Logistics Centers

3. Need for Repositories Understanding and educating Benchmarking and performance assessment Designing networks, infrastructure and processes Operations planning Testing analysis tools Optimizing trade flows 3 Project motivated –One time data collection –One time data collection with periodic updates –Continuous data collection

4. Types of Data Tabular data –Transportation rates –Transportation times Structured data –Ship schedules –Process maps Searchable text –Government rules –Process descriptions Graphics –Facility layouts –Pictures Data associated with graphics –Pier length 4

5. Data Sources Existing automated transaction capture –Customs transactions –Purchasing transactions –Operational transactions Commercial data enterprises –GIS (maps, roads, landmarks, etc.) –Ship schedules –Transportation rates Custom data collection –Processes (e.g., food process maps) –Networks (e.g., Panama logistics infrastructure) –Flows (e.g., wine import to the U.S.) 5

6. Wine Shipping routes

7. Wine Supply Chain Data Capture Purpose –Benchmark time in transit –Assess value of improved temperature control Data –Times –Temperature –Location 7

8. iButton data-logger 8

9. Instrumented carton 9

10. Tagged cartons in container Temperature captured automatically Container tracking requires manual effort

11. Temperature Data

12. 12 Cycle time to Baltimore

13. Difficulties Encountered Getting data loggers returned Requires multiple company participation Time and effort to get companies to participate Expansion to all global container shipments 13

14. Logistics Benchmarking In collaboration with the Association of Costa Rican Logistics Benchmark the logistics departments of 19 of the main companies in Costa Rica Issues –Confidentiality –Common activities

15. Costa Rica Customs Automation In collaboration with Dirección General de Aduanas (Costa Rica Customs) TIC@: An electronic system through which import and export paperwork has been reduced or eliminated. Impact on the system of the implementation in both total values and effect of staggered implementation Issues –Continued collaboration –Repurposing to analyze trade

16. Data Challenges Existing automated transaction capture –Difficult to repurpose –Proprietary elements –Integration and update Commercial data enterprises –International availability (e.g., GIS) –Expensive for smaller projects Custom data collection –Design and execution of collection processes –Cleansing –Cost –Sustainability 16

17. Typical Project Problem definition with client Methodology specified Data needs determined Data collected/assembled/cleaned Analysis performed Recommendations to client 17

18. 18 Example: U.S. New Car Delivery Network “Client Problems” Can the development of consolidated rail facilities for distribution of new cars be justified by a reduction in the inventory of new cars at plants waiting for shipment? Can the purchase of additional rail cars to haul new cars be justified by the decrease in inventory of new cars at plants waiting for shipment? What is the value of technology to optimize transportation mode?

19. 19 Logistics Services Data Plant Rail Ramp (VDC) Switch Yard Dealer Manufacturer controls what gets loaded on rail TTX controls rail car allocation Hauler controls truck allocation truck schedule what gets loaded Railroad controls train schedule Rail Truck

20. 20 Infrastructure Data Map Data Border Points Demand Areas Plants Ports Rail Ramps

21. 21 Flow Data

22. 22 Assemble Car Car Available Wait at Plant Dispatch Transport by Truck Transport by Rail Arrive at Dealer Dispatch Arrive at VDC Wait at VDC Dispatch Transport by Truck Arrive at Dealer Wait at Plant D D D D 5.98 days 1.36 days 3.10 days 8.76 days 4.15 days.44 daysTotal = 7.3Total = 16.4 Process Data

23. 23 Time Data Waiting Times for Shipment from Plants

24. 24 Inventory Data (Aggregated) Consolidated rail facilities not justified

25. 25 Inventory Data (Time Based) Lag in Shipments When Plant Restarts No Value to Adding Additional Rail Cars

26. 26 Cost Data $200-$300 Cost Per Car $300-$400 $400-$500 $500-$750 < $100 $100-$200 $750-$1000 $1000+

27. Repository Recommendations Understand in advanced the purposes for the data Determine who will get what value from the data Determine how the effort will be continuously funded Document ownership and proprietary issues Develop a rigorous set of data requirements Establish sustainable processes for collection/assembly of data Design scalable databases Determine who can access the data and how it will be accessed Anticipate unauthorized access Provide tools for analysis Provide documentation and training 27