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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/69335


    Title: 考量需求預測下存貨訂購量之決定-以W公司為例
    Authors: 陳佑哲;Chen,Yu-che
    Contributors: 工業管理研究所在職專班
    Keywords: 預測
    Date: 2016-01-21
    Issue Date: 2016-03-17 18:10:56 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 存貨管理一直是各產業所關注的議題, 也是用來評判一間工廠績
    效好壞的重要指標, 良好的存貨管理可降低資材成本, 而不好的管理
    時就會造成大量存貨的囤積與資金的積壓。因此如何有效管理存貨,
    發揮最大投資效益, 實為企業在經營與管理上之重要課題。
    本研究透過歷史訂購量與庫存量資訊來做為需求預測的輔助, 並
    應用於存貨管理理論, 建立適合個案公司之存貨管理模式, 以提升個
    案公司在存貨管理上的效益。以考慮預測需求的概念, 並使用預測方
    法預測需求量, 找出適合個案公司使用的預測方法, 作為存貨預測參
    考, 再與原個案公司的存貨管理模式(依據經驗法則)和依造連續補貨
    政策中的 ( s、S)訂購模式, 相互比較不同的存貨管理模式, 並在總成本、缺貨數與平均存貨上的差異, 以找出較佳的訂購政策, 達到存貨
    最小化與利益最大化。
    藉由預測需求與存貨訂購量政策的改善後, 本研究不但將案例公
    司的年存貨成本大幅減少、平均存貨水準下降, 並降低了因為預測需
    求錯誤將有可能造成的斷貨與缺料問題。同時也減少了個案公司積壓
    在存貨上的資金與可能經由斷料而造成的損失。;Inventory management has been a subject of concern to all industries, also one of the important targets used to judge a factory’s performance. A good inventory management may reduce the materials cost, while poor management will create massive stock and pressure on cash flow. Therefore managing the goods in stock effectively will reveal greatest investment benefit, and one of the key topics in management.
    This research is based on historic order quantity and storage information as aid to the demand forecast. With appliance in the inventory management theory to establish suitable for case study to a company’s inventory management efficiency. In this study, forecast demand is considered, and prediction method for demand trend or next to actual figures as reference. The difference between the company′s inventory management model (based on a rule of thumb) is compared with its’ policy on continuous replenishment order quantity method (s, S) model. The differences in total cost, average output and inventory are analyzed to identify for best model to minimize inventory and maximize benefits.
    By considering the demand forecast and inventory control rule. It significantly reduced cost of inventory, but also dropped average storage level. This model can address the actual demand forecast that sometimes may cause lack of material during sudden upside. As well as significant reduction for possibility of loss from high backlog that puts company cash flow stagnant.
    Appears in Collections:[工業管理研究所碩士在職專班 ] 博碩士論文

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