應用灰色理論預測半導體設備消耗性零件需求量; Applying Grey Theory to Spare Parts Demand Forecast of Semiconductor Equipment

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题名:	應用灰色理論預測半導體設備消耗性零件需求量;Applying Grey Theory to Spare Parts Demand Forecast of Semiconductor Equipment
作者:	黃錫鴻;Hsi-hung Huang
贡献者:	工業管理研究所碩士在職專班
关键词:	灰色理論;灰預測;半導體設備;零配件需求預測;Grey Prediction;Spare parts;Semiconductor Equipment;Grey Theory;Demand Forecast
日期:	2009-07-08
上传时间:	2009-09-22 14:12:43 (UTC+8)
出版者:	國立中央大學圖書館
摘要:	需求預測是企業經營運作的重要工作之一，因為需求的變化與波動，使得採購作業、庫存管理、生產排程等企業內活動均受影響。透過精確的預測來掌握需求，以達到降低庫存成本、減少人力需求、提高顧客服務品質與競爭力的目的。半導體產業是一個極具競爭的產業，生產中使用的直接材料、間接材料以及生產機台零件的庫存管理更是影響生產排程順暢與否的主因之一。生產用的直接材料、間接材料的需求可以依據訂單、產能透過物料清單來推算需求；但是生產機台的零件需求只能依靠物料企劃人員和機台維修設備人員的經驗來推算需求。本研究應用灰色預測方法中以其容易處理非線性問題、少數據、小樣本的預測特性，針對半導體生產機台零件中的消耗性零件，建構一個合適的零件需求量的預測模式，以期改善物料企劃人員的管理效率，並做為管理者決策的參考，提高管理上的競爭力。從實例驗證中發現，灰色預測對於半導體設備零件的預測有很好的準確度，並且只使用少組數的歷史資料即可得到高準確度的預測值。因此，此一方法可適用於半導體設備零件的需求量預測。 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.
显示于类别:	[工業管理研究所碩士在職專班 ] 博碩士論文

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