導入企業資源規劃系統準則評選之研究

A Study on Criteria Selection for the Enterprise Resource Planning System Implementation

賀增原(Tzeng Y. Heh) ； 張雅婷(Ya T. Chang)

《國防管理學報》 34卷1期 (2013/05) Pp. 29-45

摘要

為因應經濟體系之全球化與國際化，為能快速回應顧客需求與有效協調資源供給，導入企業資源規劃（Enterprise Resource Planning, ERP）系統已成為企業提升競爭力的關鍵要素。然而ERP系統導入過程複雜且成本昂貴，倘導入失敗，恐致企業產生營運危機，故在導入之前，需要格外慎重評估。本研究採用「國立中央大學ERP卓越計畫研究中心」之問卷資料進行分析。首先，藉由文獻探討歸納出企業導入ERP軟體評選時應考量之評選準則，包含：成本與營運效率、系統支援能力、系統相容程度與彈性、流程適切性及系統功能完整性；其次，應用自我組織特徵映射網路（Self-Organizing Feature Map Network, SOM）與K-means cluster法來分群，最後，藉由貝氏網路，分析兩者分群結果。研究分析獲得系統相容程度與彈性，為最重要評選準則，次為成本與營運效率、系統功能完整性、系統支援能力與流程適切性。另分群準確率而言，K-Means cluster法以99.65%高於SOM法97.88%；不過就資料收集與分析成本效益，SOM法可省84%優於K-Means cluster法57%。本研究的結果，可以提供企業評選ERP系統之參考。

關鍵字

企業資源規劃系統 ； 類神經網路 ； 自我組織特徵映射網路 ； 貝氏網路

並列摘要

During the highly changed environment, it is the critical factors to promote the enterprises competitiveness by importing ERP system. However, it takes a great deal of resources to develop ERP system. It has to carefully evaluate before installing ERP system. Otherwise, it would lead to operational crisis for enterprises.This study references the questionnaire survey data from the ERP Excellence Project Research Center of National Central University. It is subjects of the investigation for those enterprises imported the ERP system. It is going to clustering by using SOM and K-Means in first phase. There are cost and operational efficiency, system support ability, system compatibility and flexibility, process appropriate and system functional complete as input parameters. In second phase, Bayesian Network analyzed the accuracy of clustering for above two methods. System compatibility and flexibility are the most important selection criteria factors from the study result. For the accuracy of clustering, the K-Means 99.65% is higher than the SOM 97.88%. But for information collection and cost effectiveness analysis, the SOM 84% is higher than the K-Means 57%. The result of this study can be the reference for the enterprises installing ERP system.

並列關鍵字

ERP system ； Neural Networks ； SOM ； Bayesian Network