Forecasting Fluctuating Demand in Manufacturing

预测制造业需求波动

• 批准号: 9060759
• 负责人: Charles Smart
• 金额: $ 5万
• 依托单位: Smart Software Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1991
• 资助国家: 美国
• 起止时间: 1991-01-01 至 1991-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9060759&HistoricalAwards=false
• 关键词: Forecasting; Fluctuating; Demand; Manufacturing

The objective of this project is to investigate methods for forecasting "lumpy" demand for products and services. Lumpy demand is usually zero but intermittently non-zero. Standard forecasting systems cannot cope with lumpy demand, which is a common problem in production and inventory control. The firm's interest in the problem is a response to their customers in manufacturing. Existing approaches to lumpy demand forecasting either convert lumpy data into conventional data for conventional treatment or make parametric models of the lumpy demand (e.g., a Poisson Model). There has not been a systematic comparison of these alternatives. The researchers will empirically assess the appropriateness and accuracy of these alternatives. They will also investigate two innovative approaches. One is to explore whether associations across series occur in real data and how they might be exploited. The other is to analyze the method used most often in practice: "sales-force composite" forecasting, which totals the estimates of demand made by the sales force for each potential customer. Combination of sales force estimates with time-series techniques may have special merit when statistical data are sparse, as with lumpy demand. This research will lead to the development of computer software for PC-based forecasting of "lumpy" demand in manufacturing.

这个项目的目的是研究预测产品和服务“块状”需求的方法。块状需求通常为零，但间歇性非零。标准的预测系统无法应对块状需求，这是生产和库存控制中的一个常见问题。该公司对这个问题的兴趣是对其制造业客户的回应。现有的块需求预测方法要么将块数据转换为常规数据进行常规处理，要么建立块需求的参数模型（例如泊松模型）。目前还没有对这些替代方案进行系统的比较。研究人员将对这些替代方案的适当性和准确性进行实证评估。他们还将研究两种创新方法。一个是探索跨系列关联是否发生在真实数据中，以及如何利用它们。另一个是分析在实践中最常用的方法：“销售人员综合”预测，它将销售人员对每个潜在客户的需求估计加起来。当统计数据稀疏时，销售人员估计与时间序列技术的结合可能具有特殊的优点，如块状需求。这项研究将导致基于pc的预测制造业“块状”需求的计算机软件的发展。