SBIR Phase I: Accurate Demand Forecasts of Spare Parts Inventories

该创新是一种新的方法，预测需求的备件和服务零件的需求是间歇性的或缓慢移动。 间歇性需求主要由零值组成，在随机时间混合随机大小的非零需求，使得预测非常困难。这项创新将使零件供应商能够在“甜蜜点”运营，平衡将未使用的零件放在货架上的成本与在需要时没有零件所产生的成本。该研究将开发新的预测算法，将其嵌入原型软件产品中，并记录其与传统方法相比更高的准确性。准确性的评估将基于大量的计算实验，使用合成数据（以发现哪些数据特征是关键的）和现有客户提供的超过100，000个真实世界需求历史的库。更广泛的/商业影响将是减少对美国经济的数十亿美元拖累：零件库存管理不善。零部件库存是许多公司资产负债表上的第二大项目，其支出约占美国国内生产总值（GDP）的8%。改进的零件预测可以使零件可用性提高10%以上，同时库存成本降低15%以上。这些改进不仅有利于零部件供应商，也有利于他们的客户，他们的供应链将变得更加可靠，他们的运营将减少停机时间。

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.