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The Research Of Performance Evaluation And Influence Factors Of Large-scale Logistics Enterprises Based On DEA And AHP Methods

Posted on:2012-07-15Degree:MasterType:Thesis
Country:ChinaCandidate:L G WangFull Text:PDF
GTID:2189330335962290Subject:Management Science and Engineering
Abstract/Summary:PDF Full Text Request
As Chinese government treats the development of logistics industry as one of the top ten industry strategies, the developing extent of modern logistics industry becomes a major indicator to measure socialization, modernization and comprehensive competence of a region. Large-scale logistics enterprise is the pillar of national logistics industry, which reflects the transformation of national logistics industry from traditional pattern into modern. However, under the circumstances of low efficiency of enterprise's management, weakness of logistics operating capability and market expanding capability, imperfectness of management performance evaluating indexes, and high competition in domestic logistics market, the establishment of an effective evaluating mechanism on performance becomes an important issue. This mechanism should evaluate enterprise's management performance, analyze the key factors of enterprise's performance and give supports to the establishment of core competence, existence and sustainable development.This article is composed of three parts. The first part is based on literature review, which summarizes theories of modern logistics and researches on management performance evaluation, and discusses the characteristics of Chinese large-scale logistics enterprises on the basis of Data Envelope Analysis (DEA) and Analytic Hierarchy Process (AHP) methods. The second part establishes management performance evaluating model of large-scale logistics enterprises and analyzing model of enterprises' influence factors. The third part firstly selects input and output indicators of DEA evaluating model under the discussion of literature review and principal component analysis, and establishes management performance evaluating system of large-scale logistics enterprises. Then, this article promotes research from the industial macro-perspective and enterprise micro-perspective. In respect of industial macro-perspective, this article utilizes DEA method to evaluate the situation of management performance of large-scale logistics enterprises and logistics industry. and analyzes the factors of non-effectiveness of management performance. Under the results of DEA analysis, this article chooses indicators of DEA model as explanatory variable, and selects the value of systhesis technology efficiency as dependent variable. Then, analyzes the influence factors of performance on the basis of Panel Data Model. In terms of enterprise micro-perspective, this article discusses the management performance and industrial position ofⅩLogistics Group. In addition. by using the existing operational indicators ofⅩLogistics Group, this article discusses the influence factors of performance ofⅩLogistics Group on the basis of the AHP method and linear regression method in order to helpⅩLogistics Group establish a systematic, scientific and effective performance management index which adapts for its development. The combination of industial macro-perspective and enterprise micro-perspective indicates the generalization and particularity of this study. Finally, this article gives proposals on leveraging management performance and improving strategies of large-scale logistics enterprisesThe results of this research provide significant theoretical value, such as clarifing the circumstance and trend of management performance of China's large-scale logistics enterprises, which is important for their industrial position. Besides, the strategy of leveraging management performance in this article shows pratical value on the establishment of enterprise's competitive strategy.
Keywords/Search Tags:Large-scale Logistics Enterprise, Management Performance, Data Envelope Analysis, Analytic Hierarchy Process, Influence Factors
PDF Full Text Request
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