Data-Driven Adaptive Forecasting and Inventory Control

1. Center-Designated Project Proposals CELDi Conference April 17, 2008 Dr. Kevin Taaffe, Clemson University Dr. Aurélie Thiele, Lehigh University Data-Driven Adaptive Forecasting and Inventory Control

2. Background • Prior work with Lockheed Martin • Safety stock settings re-evaluated based on customer purchase information as well as supplier reliability data • Demonstrated cost savings (via simulation) to reduce part stock-outs and potential work stoppages • New material forecasting model to incorporate into internal monthly reports as well as documents communicated to Lockheed’s suppliers • This center-directed proposal’s focus is now to combine the material forecasting and inventory management components, using data-driven and adaptive control techniques

3. Objectives • Investigate approaches to forecasting and inventory control that are: • data-driven (dynamically integrating the experimental measurements in the decision-making framework) • adaptive (exploiting information revealed over time, to reduce part stock-outs and provide CELDi partners with a framework well-suited to real-life logistics problems) • Industry sponsors: • Lockheed Martin • FLSmidth

4. Intellectual Merits • Integrated framework tightly connects to the data, adaptingseamlessly to changing demand conditions • Positions CELDi members at the forefront of inventory management research • Combines analytical modeling and numerical simulation to demonstrate the validity of the approach • Collaborative effort between Clemson and Lehigh

5. Broader Impacts • Help CELDi members • Recognize the impact of forecastingassumptions on replenishment strategies • Provide them with a decision tool integrating forecasting and inventory control • Graduate students will better understand the operational problems faced by industry • Broad dissemination via: • Final report, presentations, and journal submissions

6. Statement of Work • Analysis of forecasting methods • Part classification, forecasting models to consider • Testing of data-driven approach • Compare with traditional methods (clustering techniques for cyclical data, number of past data points considered) • Development of guidelines • Identify “rules of thumb” that provide insights into the optimal strategy • Large-scale numerical study • Participating industries have unique service levels that can drive unique decisions…

7. Expected Results: Understand… • What is the most relevant information, given the sizable amounts of data available? • What are the tradeoffs between “one-size-fits-all” forecasting (simple but intuitive) and product-specific forecasting (accurate but complex)? • How should seasonal and non-stationary effects be taken into account? • What are the cost benefits of the integrated methodology with respect to traditional approaches?

8. Deliverables • Computer-based decision support tool to: • help managers test and implement the approach • visualize the benefits • Research paper detailing methodology and applying it on test cases • Budget: $99,981 • Clemson: $55,465 (Students:1 Ph.D., 1 M.S.) • Lehigh: $44,516 (Students: 1 Ph.D.)

9. Timeline for Task Completion