SBIR Phase I: Parts Forecasting for Configurable Products

SBIR 第一阶段：可配置产品的零件预测

• 批准号: 0610553
• 负责人: Roy Marsten
• 金额: --
• 依托单位: Emcien, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-07-01 至 2007-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0610553&HistoricalAwards=false
• 关键词: SBIR; Phase; Parts; Forecasting; Configurable

This Small Business Innovation Research (SBIR) Phase I project addresses the impact of product variety on parts inventories. The goal is to help the manufacturers of configurable products; these are products that have many different configurations, or variants, or build combinations. These variants arise because the product has a large number of features/options or the customer is given choices over options. The manufacturers of such products have a difficult time forecasting the requirements for the parts they will need to build the end items that they deliver to their customers. This effort is designed to show that current methods and first order take rates are inadequate for parts planning, and to show that much better parts forecasts can be based upon configuration-level demand forecasts.Manufacturing in the United States is shifting from mass production toward mass customization. Mass production of identical objects can be done more cheaply overseas. But higher value manufacturing that produces customized products can be kept in the United States. One of the major problems associated with high variety manufacturing is parts planning. Poor parts forecasting leads to large inventories of unneeded parts, production-interrupting shortages of needed parts, and hence higher product cost. Better parts forecasting tools could improve the profitability of American manufacturers.

这项小型企业创新研究（SBIR）I阶段项目解决了产品品种对零件库存的影响。目标是帮助可配置产品的制造商；这些产品具有许多不同的配置，变体或构建组合。这些变体之所以出现，是因为该产品具有大量功能/选项，或者为客户提供了选择。此类产品的制造商很难预测他们将需要构建向客户交付的最终物品所需的零件的要求。这项工作旨在表明当前的方法和一阶接收率对于零件计划不足，并且表明更好的零件预测可以基于配置级别的需求预报。可以更便宜地在海外进行大规模生产相同的物体。但是，可以在美国保存更高的生产定制产品的价值制造。零件计划是与大型制造相关的主要问题之一。零件预测会导致不需要的零件的大量库存，生产中断所需零件的短缺，从而更高的产品成本。更好的零件预测工具可以提高美国制造商的盈利能力。