A New Technology for Forecasting Intermittent Demand in Manufacturing

间歇性需求的问题干扰了有效的 美国制造业的生产和库存控制活动 事务所 而准确及时的需求的关键作用 预测计划生产、库存和劳动力， 和经济批量已经得到了相当大的关注， 绝大多数的预测研究，以及几乎所有的商业研究， 预测软件，假设需求是“平稳”的，而不是 断断续续的 第一阶段小企业创新成果 研究（SBIR）研究为新的 可用于预测间歇性需求的软件工具 并且其适合于在台式计算机上使用。 的 第二阶段研究的目标集中在（1）收集和 分析更多真实世界的间歇性时间序列数据;（2） Croston强迫单级数法实现 预测间歇性需求，包括其扩展到 趋势和季节性数据;（3）开发新的多 基于随机过程模型的序列预测方法 在产品线中的各个项目之间“借用力量”;（4） 确定最有效地使用销售人员的判断 间歇性需求，包括其与 统计预报;（5）预报的综合 在第二阶段开发的方法，最终在他们的 在桌面上运行的软件原型中的实现 电脑 该项目将提供计算机化的最先进的解决方案 间歇性需求预测问题。 大 美国制造业公司的百分比将受益于更多 通过这种需求预测可以实现高效生产。

The problem of intermittent demand interferes with efficient production and inventory control activities in US manufacturing firms. While the key role of accurate and timely demand forecasting in planning production, inventories and work force, and economic lot sizing has received considerable attention, the vast majority of forecasting research, and nearly all commercial forecasting software, assume that demand is "smooth" rather than intermittent. Results of the Phase I Small Business Innovation Research (SBIR) study establish the technical basis for a new software tool that can be used to forecast intermittent demand and that is suitable for use on a desktop computer. The objectives of the Phase II research focus on (1) gathering and analysis of more real-world intermittent time-series data; (2) implementation of a single-series method of Croston's for forecasting intermittent demand, including its extension to trending and seasonal data; (3) development of a new multi- series forecasting approach based on stochastic process models that "borrow strength" across the items in a product line; (4) determination of the most effective use of sales-force judgments of intermittent demand, including their combination with statistical forecasts' and (5) integration of the forecasting methods developed in Phase II, culminating in their implementation in a software prototype running on desktop computers. The project will provide a computerized state-of-the-art solution to the intermittent demand forecasting problem. A large percentage of US manufacturing firms stand to benefit from more efficient production made possible by such demand forecasting.