| Summary: | 碩士 === 國立中央大學 === 工業管理研究所碩士在職專班 === 97 === Demand forecasting is one of the most crucial aspects of inventory management of a company. Demand variation affects significantly not only inventory level but also procurement decision and manufacturing scheduling within an enterprise. Adequate estimation is essential for demand management will reduce acquisition cost of inventory and demand of human resource and all together increase customer satisfaction as well as core enterprise competency.
Semiconductor industry is rather completive. Effective inventory managing of direct material, indirect material and spare parts of equipment eases manufacture scheduling. The demand of direct material and indirect material can be calculated based on customer order, capacity utilization and bill of materials (BOM). However demand of spare parts relies solely on the experience and estimation of material planning staff and equipment maintainer.
This research applies the Grey prediction theory, which is ideal for non-linear, limited data and small sample problem in order to build up a feasible model for estimating the consumable spare parts demand of equipment. This aims to improve the productivity of material planner and as decision making referral to top-manager.
Empirical analyses reveal the Grey GM(1,1) model offers better accuracy on spare parts demand forecast of semiconductor equipment. On the other hand, it is capable to provide reliable estimation based on limited historical data. Hence, it is feasible for the demand forecast for the spare parts of semiconductor equipment.
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