Forecasting Fluctuating Demand in Manufacturing

这个项目的目的是研究方法 用于预测对产品和服务的“luxury”需求。 块状需求通常为零，但间歇性地不为零。 标准的天气预报系统无法科普高亮度的需求， 这是生产和库存中常见的问题 控制 公司对这个问题的兴趣是对 客户在制造业。 现有的LUB需求预测方法 将亮度数据转换为常规数据， 处理或制作亮度需求的参数模型 (e.g.,泊松模型）。 没有一个系统的 这些替代品的比较。 研究人员将根据经验评估 这些替代品的适当性和准确性。 他们 还将研究两种创新方法。 一是 探索在真实的数据中是否存在跨系列的关联 以及它们如何被利用。 二是分析 实践中最常用的方法：“销售人员综合” 预测，它汇总了 为每一位潜在客户提供服务。 组合 使用时间序列技术的销售人员估计可能 当统计数据稀疏时，如亮度， 需求 这项研究将导致发展 以电脑为基础的“制冷”需求预测电脑软件 在制造业。

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.