SBIR Phase I: Accurate Demand Forecasts of Spare Parts Inventories

The innovation is a new method for forecasting the demand for spare and service parts whose demand is intermittent or slow moving. Intermittent demand consists mostly of zero values, with nonzero demands of random sizes mixed in at random times, making it extremely difficult to forecast. The innovation will allow parts providers to operate in the "sweet spot" that balances the costs of keeping unused parts on the shelf against the costs created by not having parts available when needed. The research will develop the new forecasting algorithm, embed it in a prototype software product, and document its greater accuracy compared to conventional methods. The evaluation of accuracy will be based on extensive computational experiments using both synthetic data (to discover which data features are critical) and a library of over 100,000 real-world demand histories provided by existing customers. The broader/commercial impact will be reduction of a multi-billion dollar drag on the US economy: mismanagement of parts inventories. Parts inventories are the second largest item on the balance sheets of many companies, and spending on them amounts to roughly 8% of the US Gross Domestic Product (GDP). Improved parts forecasting can lead to increases in parts availability by over 10%, and simultaneous reductions of over 15% in inventory costs. These improvements will benefit not only the vendors of parts but their customers, whose supply chains will become more reliable and whose operations will have reduced down time.