This Small Business Innovation Research (SBIR) Phase I project will advance the state of the art in forecasting the demand for spare and service parts, which is typically "intermittent" and difficult to forecast. Intermittent demand is characterized by a high percentage of zero values, interspersed with random nonzero integer demands that often have high variance. This project moves beyond the conventional one-item-at-a-time approach toward a new methodology based on identifying and exploiting clusters of parts which respond to common drivers.
The broader impact/commercial potential of this project will be improved demand planning and inventory management for companies with large inventories of spare or service parts. These are the second largest item on the balance sheets of many companies, and spending on them amounts to roughly 8% of the U.S. Gross Domestic Product. With so much capital invested, minimizing inventory becomes a priority. On the other hand, with shorter cycle times and greater competition across all sectors, maintaining sufficient inventory to meet service level commitments is also essential. These twin pressures require accurate demand forecasts in order to determine the inventory "sweet spot".
