Demand Planning Hierarchy System for Hierarchical Products

碩士 === 國立臺灣大學 === 工業工程學研究所 === 91 === Demand planning results serve as the basis of every planning activity in supply chain planning. An important feature of demand planning systems is to provide an easily accessible demand database, which support multidimensional views of demand information for dec...

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Bibliographic Details
Main Authors: Hsieh-Peng Huang, 黃協鵬
Other Authors: Argon Chen
Format: Others
Language:en_US
Published: 2003
Online Access:http://ndltd.ncl.edu.tw/handle/31692595615923388214
Description
Summary:碩士 === 國立臺灣大學 === 工業工程學研究所 === 91 === Demand planning results serve as the basis of every planning activity in supply chain planning. An important feature of demand planning systems is to provide an easily accessible demand database, which support multidimensional views of demand information for decision making. Such a database should be able to support demand aggregation, disaggregation and forecasting. Based on demand planning results, companies can proceed with other supply chain planning activities, such as capacity planning, inventory planning and production planning. Current demand planning systems are designed to support requirements mentioned above. However, they don''t provide any suggestion on what is the most proper view for making demand plans. Demand planning path provides a sequential view from the highest aggregated demand view to the lowest detailed disaggregation demand view for the planner to perform bottom-up, top-down or middle-out planning. In this research, we propose the demand planning hierarchy which extends the demand planning path for hierarchical products. First, we introduce a symbolic representation system supporting demand planning hierarchy with hierarchical products. With product hierarchy, different evolving rules are introduced for the demand planning hierarchy. Second, a software system prototype for such a demand planning hierarchy is developed. As a decision support system, the developed system is not only figures out the optimal demand views but also provides interactive interfaces for decision making. Finally, a demand data set from a semiconductor manufacturing company is used to test the proposed algorithms and the prototype developed in this research.