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篇名 供應鏈生產及存貨政策之長鞭效應分析
卷期 4:1
並列篇名 The Study of Bullwhip Effect of Supply Chain Production and Inventory Strategy Under Declined Trend Demand
作者 陳銘崑鄭穎聰
頁次 049-080
關鍵字 供應鏈管理長鞭效應系統動態學灰色關聯分析Supply Chain ManagementBullwhip EffectSystem DynamicsGray Relationship
出刊日期 200207

中文摘要

由於企業環境急速變動,產學界提出供應鏈管理之觀念與作法以因應 之。供應鏈常受到生產策略及存貨政策之影響,以致常造成鏈上資訊扭曲, 即稱爲長鞭效應現象。本文旨針對三種生產策略如MTS(Make-to-stock)、 BTO(Build-to-order)、及 CTO(Configuration-to-order)和五種存貨政策如 (s,S), (s,Q), (s,R), (s,Q,R)及 VMI(Vendor Managed Inventory),探討供應 鏈管理中庫存量曲線之波動情形。根據上述波動現象從波動幅度、波動時間 及階層傳遞放大率來觀察長鞭效應現象。長鞭效應衡量指標包括:波動最高 點、波動最低點、波動回穩時間、上游需求放大率、總波動回穩時間及總放 大率等。本硏究採用桌上型個人電腦產業爲對象,需求型態爲下降型。文中 首先以系統動態學建置三種生產策略之模型,五種存貨政策則以各政策意義 以模型參數公式表示之。系統動態模型效度的檢定計有四項:穩態測試、 階梯測試、季節波動測試、及實際資料測試。之後將模擬實驗結果以灰色關 聯求出最佳政策組合。此最佳政策組合可作爲管理者在控制供應鏈長鞭效應 政策之參考依據。

英文摘要

Since the rapid change in industry environment, the academic and industry proposed the concept and procedure to solve the problems of supply chain management (SCM). SCM is often affected by the production and inventory strategy and it has the information distortion. This is called bullwhip effect. The purpose of this paper focuses on three production strategies, make-to-stock(MTS), Build-to-order(BTO), Configuration-to-order (CTO), and five inventory strategies, (s,S), (s,O), (s, R), (s,Q,R), and Vendor Management Inventory (VMI) and studies the SCM inventory fluctuation influenced by the above strategies. Based on the above scenarios, the bullwhip effect can be observed by inventory fluctuation, time and level transfer ratio. The bullwhip effect performance indexes include fluctuation uppermost point, fluctuation lowest point, fluctuation stable time, upstream demand enlarge ratio, total fluctuation stable time and total enlarge ratio. The power supply component of the computer industry is selected and the demand type is declined trend. Three system dynamic models of the production strategy are constructed, and the five inventory strategies are expressed by the model parameters. The system dynamics models are tested by steady state test, step test, seasonal fluctuational test, and real data test. Then, the simulation results are computed based on gray relationship in order to find optimal policy combination. This optimal policy combination can be act as reference for SCM decision-maker.

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