Data Driven Adaptive Forecasting and Inventory Control

1. Data Driven Adaptive Forecasting and Inventory Control Dr. Kevin Taaffe, Clemson University Dr. Aurélie Thiele, Lehigh University Wennian Li (Clemson) Narges Hosseini (Clemson) Rockey Myall (Lehigh) Spring 2010 CELDi Meeting

2. Outline • Objectives • Work Products • VBA / Excel Tool • White Paper • Forecast and Inventory Analysis Tool (Database and Interactive Tool) • Benefits of FIAT Spring 2010 CELDi Meeting

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) • Deliver computer-based decision support tools to: • help managers test and implement the approach • visualize the benefits of considering forecast error and total inventory costs simultaneously Spring 2010 CELDi Meeting

4. Work Products • VBA / Excel application • Helps the manager visualize the impact of risk aversion and parameter ambiguity on its optimal ordering quantities for spare parts inventory control • White paper on data-driven inventory control under risk aversion • SQL Server database using VB.NET that addresses both forecasting and inventory management • FIAT – Forecast and Inventory Analysis Tool • Ability to compare existing and new forecast methods based on inventory cost as well as forecast error • Includes a detailed user’s guide Spring 2010 CELDi Meeting

5. Spare Parts Inventory Control • Lehigh investigated inventory policies under risk aversion for spare parts inventory control (sporadic demand, possible random lead times) • Should the parts be made-to-stock or made-to-order? • VBA / Excel application to address this • The tool works on a product-by-product basis. • Computes the marginal value of adding one more unit based on: • uncertainty on the next arrival time of an order, • production and holding costs, • uncertain production lead times. • discounted value of time. • risk of losing customers if part is not available. Spring 2010 CELDi Meeting

6. White Paper • White paper with the following contributions: • Highlights the challenges related to traditional inventory management (non-adaptive forecasting) • Describes high-level techniques that managers can implement to protect their operations from those challenges • Comments on some more specific techniques that will be interesting to members with a technical background • Provides an overview of current research in the field and state-of-the-art knowledge • Goal is to help all CELDi industry partners understand how they can incorporate data-driven inventory control in their own company Spring 2010 CELDi Meeting

7. Inventory Control / Forecasting • Should we reduce forecast error? Should we reduce inventory control costs? Consider these choices: • Cost minimal approach combines forecasting and inventory control. Useful when backorder and holding costs are very different. • Error minimal approach ignores cost parameters of system, focuses only on best forecasting. • How are lead times addressed? • What is an acceptable part availability service level? • Can these be tested separately from a company’s live system (that may not include the necessary flexibility)? Spring 2010 CELDi Meeting

8. Inventory Control / Forecasting • The FIAT database application provides the following functionality: • Allows the user to set forecast and safety stock policies • Ranks forecast methods based on inventory cost or forecast error • Provides the flexibility to analyze any subset of parts • Allows the user to set all part-specific information • Provides “what if” cost analysis by comparing specific forecast / inventory policies using past historical data • Provides end-of-useful-life forecast and inventory management for any part Spring 2010 CELDi Meeting