台灣物流中心經營效率之評估－資料包絡分析法之應用

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题名:	台灣物流中心經營效率之評估－資料包絡分析法之應用
其它题名:	Efficiency Evaluation of Distribution Centers in Taiwan-An Application of DEA Approach
作者:	陳衍霖 Chen, Yen-Lin
贡献者:	陳景榮 Chen, Jing-Jung 東海大學食品科學系
关键词:	物流中心;效率;資料包絡分析法;DEAP;差額變數分析 DEA;DEAP;distribution center;efficiency;slack variable analysis
日期:	2001
上传时间:	2011-05-25T09:42:19Z (UTC)
摘要:	在市場趨於飽和及競爭日益激烈的環境下，物流中心在達成組織目標之餘，更應該致力於提昇資源的使用效率。效率評估之主要目的是藉由評估結果來診斷受評估單位的經營管理狀況，進而改善現狀並提出建議。由於資料包絡分析法(DEA)具有可同時處理多項投入及產出之能力，並且無須預設生產函數即可自行賦予權重，因此，衡量結果較其他效率評估方法客觀。本研究利用DEA方法探討國內26家物流中心在2000年的相對經營效率，並提供無效率物流中心改善的方向及幅度。在投入及產出項的選擇上，選擇四個投入項－總倉儲面積、機器設備成本、運輸成本、工作時數以及二個產出項－出貨量與營業額成長率，繼而使用DEAP軟體來計算各物流中心之相對效率。由分析結果得知，在評估的樣本中由製造商及零售商所成立之物流中心較具有整體效率，而有超過半數的業者在資源的配置上使用得宜，但是由獨資或其他背景所成立之業者表現相對較差，此外，無效率業者在生產規模上皆處於不適狀態，必須加以擴大或縮小，最後則利用差額變數分析提供給無效率業者在資源配置上之具體建議。 Under current conditions of a saturated market and fierce competition, Distribution Centers should use more of their resources to promote efficiency in order to achieve their goals. The major purpose of efficiency evaluation is to diagnose the managed situation and provide further suggestions. Data Envelopment Analysis(DEA) is a nonparametric approach that incorporates multiple inputs and outputs, and thus the results are more objective than other methods of efficiency evaluation. This study used the DEA method to evaluate the relative efficiencies of the 26 Distribution Centers in Taiwan using data from the year 2000, and gives advice to inefficient Distribution Centers on how to improve their performance. Efficiency scores were determined using four inputs－area of storehouse, cost of equipment, cost of transport, hours of labor－and two outputs: amount of good, and rate of turnover growth. The computer program used was DEAP. The research showed that among the 26 Distribution Centers evaluated, M.D.C and Re.D.C were considered relatively aggregate efficient. On the aspect of pure technical efficiency, more than half of those observed were considered efficient, but the performance of those Distribution Centers with exclusive ventures etc. were not as good. Additionally, inefficient Distribution Centers were found to lie on the two extreme ends of the spectrum, in that they should either expand or reduce their company size. Finally, we provide specific suggestions to inefficient Distribution Centers via slack variable analysis.
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