基于DEA和SFA的物流企业综合绩效评价研究
|
摘要
全面、正确、及时评价物流企业的绩效，是保证企业高效运行、进而实现目标、价值追求的基础和关键。绩效评价有助于全面加强管控，有助于形成一个快速、科学、规范、高质量运行的模式和格局，有助于分析运行过程中存在的问题、获得的成绩和积累的经验，从而为物流企业持续、健康发展指明方向。本论文对此进行了有益的探索。
本研究首先建立了物流企业绩效评价基本评价指标体系。该指标体系综合考虑了物流企业运输绩效、仓储绩效、物流信息化绩效、财务绩效以及客户服务绩效等五类的绩效指标，全面地反映了物流企业的实际运营信息。采用数据包络分析(DEA)及随机前沿分析(SFA)两种不同的绩效数学分析模型，针对某大型跨国物流企业下属的12家分公司进行综合绩效评估，并对两种方法的评估结果进行了对比分析，作为物流企业提升经营效率的参考。
鉴于物流绩效评价是一种典型的多指标问题，涉及的因素繁多。多指标带来了分析上的复杂性和指标间的多重相关性两大问题。本文在引用DEA和SFA方法前，采用了主成分分析(PCA)方法，将多指标问题转化为较少的综合指标，解决了上述问题，使得物流企业绩效评价的方法更为科学。通过计算分析，DEA与SFA两种方法所得结果相关度较高，两种方法所得公司因素对技术效率的影响方向基本一致，同时进一步明确了影响综合绩效的关键要素，给出了改进的方向。
本研究不仅达到了对物流企业的综合绩效进行评价的目的，而且进一步证明了基于PCA的DEA和SFA方法对物流企业评价的科学性和合理性，为其他类型的企业的绩效评价提供了参考和借鉴。
The comprehensive, correct and timely performance evaluation of the logistics enterprises plays its key role in ensuring the efficient operation and in achieving the goals and values set by companies. Performance evaluation will help us to analyze the problems that exist on our operational process and help us to review the lessons and the experience we obtained from past, thus guiding us appropriately to a continuous and healthy logistics. Based on the above, this paper makes a useful research on logistics performance evaluation
The paper firstly establishes the basic evaluating index system for performance appraisal. The system takes consideration of five key indices that comprehensively reflect the actual operation of the enterprise, including the performance on transportation, warehousing, information, corporate finance as well as customer service. The author made the comprehensive evaluation on 12 subsidiaries subordinated to a large parent multinational logistics enterprise with two different analytic models: Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA). The paper then compares and analyzes the results of the two methods, to provide the reference tfor enhancing the operating efficiency.
Because the logistics performance evaluation is a typical issue involving the multiple indices, many factors must be considered. The multiple indexes bring us tow problems, one is the complexity of analysis and another is the multilateral correlation among the multiple indices. Before using DEA and SFA method, the author adopts Principal Component Analysis (PCA) to reduce the number of indices so as to solve the above problems. This makes the approach of comprehensive performance evaluation being more scientific. Through computation and analyzing, we find out that the correlation of the results from two methods is quite high. The influences of technical efficiency on the company factors from two methods are basically kept with the same direction. This also further clarifies the key factors affecting the overall performance and gives the direction to improve.
This research not only reaches the goal of appraising the comprehensive efficiency of the logistics enterprises, but also proves that it is scientific and rational to evaluate the enterprises with SFA and DEA on the basis of PCA. This paper offers a reference for other enterprises.
本研究首先建立了物流企业绩效评价基本评价指标体系。该指标体系综合考虑了物流企业运输绩效、仓储绩效、物流信息化绩效、财务绩效以及客户服务绩效等五类的绩效指标，全面地反映了物流企业的实际运营信息。采用数据包络分析(DEA)及随机前沿分析(SFA)两种不同的绩效数学分析模型，针对某大型跨国物流企业下属的12家分公司进行综合绩效评估，并对两种方法的评估结果进行了对比分析，作为物流企业提升经营效率的参考。
鉴于物流绩效评价是一种典型的多指标问题，涉及的因素繁多。多指标带来了分析上的复杂性和指标间的多重相关性两大问题。本文在引用DEA和SFA方法前，采用了主成分分析(PCA)方法，将多指标问题转化为较少的综合指标，解决了上述问题，使得物流企业绩效评价的方法更为科学。通过计算分析，DEA与SFA两种方法所得结果相关度较高，两种方法所得公司因素对技术效率的影响方向基本一致，同时进一步明确了影响综合绩效的关键要素，给出了改进的方向。
本研究不仅达到了对物流企业的综合绩效进行评价的目的，而且进一步证明了基于PCA的DEA和SFA方法对物流企业评价的科学性和合理性，为其他类型的企业的绩效评价提供了参考和借鉴。
The comprehensive, correct and timely performance evaluation of the logistics enterprises plays its key role in ensuring the efficient operation and in achieving the goals and values set by companies. Performance evaluation will help us to analyze the problems that exist on our operational process and help us to review the lessons and the experience we obtained from past, thus guiding us appropriately to a continuous and healthy logistics. Based on the above, this paper makes a useful research on logistics performance evaluation
The paper firstly establishes the basic evaluating index system for performance appraisal. The system takes consideration of five key indices that comprehensively reflect the actual operation of the enterprise, including the performance on transportation, warehousing, information, corporate finance as well as customer service. The author made the comprehensive evaluation on 12 subsidiaries subordinated to a large parent multinational logistics enterprise with two different analytic models: Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA). The paper then compares and analyzes the results of the two methods, to provide the reference tfor enhancing the operating efficiency.
Because the logistics performance evaluation is a typical issue involving the multiple indices, many factors must be considered. The multiple indexes bring us tow problems, one is the complexity of analysis and another is the multilateral correlation among the multiple indices. Before using DEA and SFA method, the author adopts Principal Component Analysis (PCA) to reduce the number of indices so as to solve the above problems. This makes the approach of comprehensive performance evaluation being more scientific. Through computation and analyzing, we find out that the correlation of the results from two methods is quite high. The influences of technical efficiency on the company factors from two methods are basically kept with the same direction. This also further clarifies the key factors affecting the overall performance and gives the direction to improve.
This research not only reaches the goal of appraising the comprehensive efficiency of the logistics enterprises, but also proves that it is scientific and rational to evaluate the enterprises with SFA and DEA on the basis of PCA. This paper offers a reference for other enterprises.
引文
[1] Thomas A. Foster. Logistics Benchmarking. New York Wiley, March 1992, p: 30-36.
[2] K. Shetty. Aiming High: Competitive Benchmarking For Superior Performance. Long Range Planning, Februrary 1993, p: 39-44.
[3] nelen. L. Richardson. Improve Quality Through Benchmarking. Transportation&Distribution, Octorber 1992, p: 32-37.
[4] Handbook of logistics and supply chain management. 2002.
[5] [美]鲍尔-索克斯．物流管理．机械工业出版社，1999．
[6] 丁立言，张铎．物流基础．清华大学出版社，2000，4．
[7] 刘源．基于DEA方法的企业物流系统效益评价．经济经纬，2001(5)：54-55．
[8] 丁立言，张铎．物流基础．清华大学出版社，2000，4．
[9] 左元斌．现代物流企业绩效评价的方法研究．管理纵横．
[10] 物流企业分类与评估指标．中华人民共和国国家标准，GB／T19680-2005．
[11] 孙宏岭，戚世钧．现代物流活动绩效分析．中国物资出版社，2001，12．
[12] 王伟．供应链企业物流绩效评测体系．业界资讯，2002(6)．
[13] 范春梅．物流绩效评价研究现状综述．物流技术，2004年第9期
[14] 吴俊卿．绩效评价的理论与方法—在科研机构的实践．科学技术文献出版社，1992．
[15] 托马斯．G．罗斯基．经济效益与经济效率．经济研究，1993，6：38-40．
[16] 魏煌，王丽．中国商业银行效率研究：一种非参数的分析．金融研究，2000，3：88-96．
[17] 孙建．DEA在企业效率评价中的应用研究．地质技术经济管理，2003，25(5)：51-53．
[18] 李亮，崔晋川．DEA方法中输入输出项目的选择和数据处理．系统工程学报，2003，(6)p：487-490．
[19] Banker, R.D.A. Charnes and W.W. Cooper. Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis. Management Science, 1984,30(9) p: 1078-1092.
[20] 彭涛．金融发展与经济增长：基于银行效率的实证研究．湖南大学，2004．
[21] 吴忠岳．台铁车站生产效率分析．台湾：成功大学，2003．
[22] 李冠，何明祥．现代物流管理系统及其DEA绩效评价．商业研究，2003(14)：146-148．
[23] Aigner, D.J.,C.A.K. Lovell and P. Schmidt. Formulation and estimation of stochastic frontier production function models[J]. Journal of Econometrics, 1977,6 p: 21-37.
[24] Meeusen. W, &J. van den Broeck. Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error[J]. International Economic Review, 1977, 18(2) p: 435-444.
[25] Battese, G. E.,& C.S. Corra. Estimation of a Production Frontier Model: With Application to the Pastoral Zone of Eastern Australia[J]. Australian Journal of Agricultural Economics, 1997, 21(3) p: 169-179.
[26] Battese, G.E. and T.J. Coelli. Frontier Production Functions, Technical Efficiency and Panel Data: With Application to Paddy Farmers in India[J]. Journal of Productivity Analysis, 1992,3 p: 153-169.
[27] Battese, C.E. and T.J. Coelli. A Model for Technical Inefficiency Effects in a Stochastic Production Frontier for Panel Data[J]. Empirical Economics, 1995, 20 p: 325-332.
[28] Kumbhakar. S.C.,S. Ghosh, and J.T. McGuckin. A Generalized Production Frontier Approach for Estimating Determinants of Inefficiency in US Dairy Farms[J]. Journal of Business and Economic Statistics, 1991, 9(3) p: 279-286.
[29] Reifschneider, D., and R. Stevenson. Systematic Departures from the Frontier:A Framework for the Analysis of Firm Inefficiency[J].International Economic Review, 1991, 32(3) p: 715-723.
[30] 何枫．中国经济发展中的效率评价：理论与实证研究．西北大学，2004．
[31] Duncan Mortimer. Competing Methods for Efficiency Measurement-A Systematic Review of Direct DEA vs. SFA/DFA Comparisons, The Center for Health Program Evaluation (CHPE),University of East Anglia, working paper no. 136, September 2002.
[32] Jacobs R(2000). Alternative methods to examine hospital efficiency: dataenvelopment analysis and stochastic frontier analysis University of York: Centre for Health Economics(CHE), Discussion Paper p: 177.
[33] Bates J.M., Baines D., Whynes D.K. (1996). Measuring the efficiency of prescribing by general practitioners. Journal of the Operational Research Society ,47, p: 1443-1451.
[34] Lovell C.A.K. (1993). Production frontiers and productive efficiency. Fried H. 0., Lovell C.A.K., Schmid S.S. (Eds), The Measurement of Productive Efficiency: Techniques and Applications, Oxford University Press, p: 3-67.
[35] Tser-yieth Chen. A comparison of chance-constrained DEA and stochastic frontier analysis: bank efficiency in Taiwan. Journal of the Operational Research Society, May2002, Vol. 53, Issue5, p: 492-500.
[36] Tsionas Efthymios G.. Combining DEA and stochastic frontier models: an Empirical Bayes approach. European Journal of Operational Research, Jun. 2003, Vol.147, Issue3, p: 499-510
[37] Wadud Abdul, White Ben. Farm household efficiency in Bangladesh: a comparison of stochastic frontier and DEA methods. Applied Economics, Oct. 2000, Vol. 32, Issuel 3, p: 1665-1673.
[38] Cooper W.W., Kumbhakar, Subal. DEA and Stochastic Frontier Analyses of the 1978 Chinese economic reforms. Socio-Economic Planning Sciences, Jun95, Vol. 29, Issue2, p: 85-112.
[39] 全介．现代企业物流管理理论与实证．中国矿业大学出版社，2001，5．
[40] 胡列格，何其．物流运筹学。北京：电子工业出版社，2005．
[41] 吴清一．物流基础．北京：清华大学出版社，2000，4．
[42] 骆温平．物流与供应链管理．北京：电子工业出版社，2002，10．
[43] 潘文荣．企业物流绩效评价指标体系的构建．统计与决策，2005(22)．
[44] 甘红云，杨家其，蒋惠园．物流绩效评价研究综述．现代物流，2002(10)：24-26．
[45] 陈守东，赵振全，赵立云．证券投资理论与分析．长春：吉林大学出版社，2001，9．
[46] 刘明菲．谈物流企业顾客服务绩效评价体系的建立．商业时代，2005(18)．
[47] 郁俊莉．主成分分析法在建立工业企业经济效益综合评价指标体系中的应用．地质技术经济管理，1998，20(3)：31-35．
[48] 于秀林，任雪松．多元统计分析．北京：中国统计出版社，1999．
[49] 郝黎仁，樊元．SPSS实用统计分析．北京：中国水利水电出版社，2002．
[50] 熊国强，党兴华．具有非正值评价指标的DEA方法及其应用．系统工程理论方法应用，2003(12)：153-156．
[51] 曹阳龙，史本山．基于主成分分析的DEA复合评价模式及其应用研究．现代管理科学，2006(9)．
[52] Coelli, T.. A Guide to DEAP Version 2.1: A Data Envelopment Analysis (Computer). CEPA Working Paper 96/08, Unisversity of New England, Australia.
[53] 郝海，顾培亮，卢奇．带有评价准则的数据包络分析模型．天津大学学报，2002(9)．
[54] 马占新．样本数据包络面的研究与应用．系统工程理论与实践，2003，12．
[55] Coelli. T J. A guide to FRONTIER Version 4.1: A computer program for stochastic frontier production and cost function estimation[EB/OU], http://www.une.edu.au/econometrics/cepa.htm.96/07.
[2] K. Shetty. Aiming High: Competitive Benchmarking For Superior Performance. Long Range Planning, Februrary 1993, p: 39-44.
[3] nelen. L. Richardson. Improve Quality Through Benchmarking. Transportation&Distribution, Octorber 1992, p: 32-37.
[4] Handbook of logistics and supply chain management. 2002.
[5] [美]鲍尔-索克斯．物流管理．机械工业出版社，1999．
[6] 丁立言，张铎．物流基础．清华大学出版社，2000，4．
[7] 刘源．基于DEA方法的企业物流系统效益评价．经济经纬，2001(5)：54-55．
[8] 丁立言，张铎．物流基础．清华大学出版社，2000，4．
[9] 左元斌．现代物流企业绩效评价的方法研究．管理纵横．
[10] 物流企业分类与评估指标．中华人民共和国国家标准，GB／T19680-2005．
[11] 孙宏岭，戚世钧．现代物流活动绩效分析．中国物资出版社，2001，12．
[12] 王伟．供应链企业物流绩效评测体系．业界资讯，2002(6)．
[13] 范春梅．物流绩效评价研究现状综述．物流技术，2004年第9期
[14] 吴俊卿．绩效评价的理论与方法—在科研机构的实践．科学技术文献出版社，1992．
[15] 托马斯．G．罗斯基．经济效益与经济效率．经济研究，1993，6：38-40．
[16] 魏煌，王丽．中国商业银行效率研究：一种非参数的分析．金融研究，2000，3：88-96．
[17] 孙建．DEA在企业效率评价中的应用研究．地质技术经济管理，2003，25(5)：51-53．
[18] 李亮，崔晋川．DEA方法中输入输出项目的选择和数据处理．系统工程学报，2003，(6)p：487-490．
[19] Banker, R.D.A. Charnes and W.W. Cooper. Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis. Management Science, 1984,30(9) p: 1078-1092.
[20] 彭涛．金融发展与经济增长：基于银行效率的实证研究．湖南大学，2004．
[21] 吴忠岳．台铁车站生产效率分析．台湾：成功大学，2003．
[22] 李冠，何明祥．现代物流管理系统及其DEA绩效评价．商业研究，2003(14)：146-148．
[23] Aigner, D.J.,C.A.K. Lovell and P. Schmidt. Formulation and estimation of stochastic frontier production function models[J]. Journal of Econometrics, 1977,6 p: 21-37.
[24] Meeusen. W, &J. van den Broeck. Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error[J]. International Economic Review, 1977, 18(2) p: 435-444.
[25] Battese, G. E.,& C.S. Corra. Estimation of a Production Frontier Model: With Application to the Pastoral Zone of Eastern Australia[J]. Australian Journal of Agricultural Economics, 1997, 21(3) p: 169-179.
[26] Battese, G.E. and T.J. Coelli. Frontier Production Functions, Technical Efficiency and Panel Data: With Application to Paddy Farmers in India[J]. Journal of Productivity Analysis, 1992,3 p: 153-169.
[27] Battese, C.E. and T.J. Coelli. A Model for Technical Inefficiency Effects in a Stochastic Production Frontier for Panel Data[J]. Empirical Economics, 1995, 20 p: 325-332.
[28] Kumbhakar. S.C.,S. Ghosh, and J.T. McGuckin. A Generalized Production Frontier Approach for Estimating Determinants of Inefficiency in US Dairy Farms[J]. Journal of Business and Economic Statistics, 1991, 9(3) p: 279-286.
[29] Reifschneider, D., and R. Stevenson. Systematic Departures from the Frontier:A Framework for the Analysis of Firm Inefficiency[J].International Economic Review, 1991, 32(3) p: 715-723.
[30] 何枫．中国经济发展中的效率评价：理论与实证研究．西北大学，2004．
[31] Duncan Mortimer. Competing Methods for Efficiency Measurement-A Systematic Review of Direct DEA vs. SFA/DFA Comparisons, The Center for Health Program Evaluation (CHPE),University of East Anglia, working paper no. 136, September 2002.
[32] Jacobs R(2000). Alternative methods to examine hospital efficiency: dataenvelopment analysis and stochastic frontier analysis University of York: Centre for Health Economics(CHE), Discussion Paper p: 177.
[33] Bates J.M., Baines D., Whynes D.K. (1996). Measuring the efficiency of prescribing by general practitioners. Journal of the Operational Research Society ,47, p: 1443-1451.
[34] Lovell C.A.K. (1993). Production frontiers and productive efficiency. Fried H. 0., Lovell C.A.K., Schmid S.S. (Eds), The Measurement of Productive Efficiency: Techniques and Applications, Oxford University Press, p: 3-67.
[35] Tser-yieth Chen. A comparison of chance-constrained DEA and stochastic frontier analysis: bank efficiency in Taiwan. Journal of the Operational Research Society, May2002, Vol. 53, Issue5, p: 492-500.
[36] Tsionas Efthymios G.. Combining DEA and stochastic frontier models: an Empirical Bayes approach. European Journal of Operational Research, Jun. 2003, Vol.147, Issue3, p: 499-510
[37] Wadud Abdul, White Ben. Farm household efficiency in Bangladesh: a comparison of stochastic frontier and DEA methods. Applied Economics, Oct. 2000, Vol. 32, Issuel 3, p: 1665-1673.
[38] Cooper W.W., Kumbhakar, Subal. DEA and Stochastic Frontier Analyses of the 1978 Chinese economic reforms. Socio-Economic Planning Sciences, Jun95, Vol. 29, Issue2, p: 85-112.
[39] 全介．现代企业物流管理理论与实证．中国矿业大学出版社，2001，5．
[40] 胡列格，何其．物流运筹学。北京：电子工业出版社，2005．
[41] 吴清一．物流基础．北京：清华大学出版社，2000，4．
[42] 骆温平．物流与供应链管理．北京：电子工业出版社，2002，10．
[43] 潘文荣．企业物流绩效评价指标体系的构建．统计与决策，2005(22)．
[44] 甘红云，杨家其，蒋惠园．物流绩效评价研究综述．现代物流，2002(10)：24-26．
[45] 陈守东，赵振全，赵立云．证券投资理论与分析．长春：吉林大学出版社，2001，9．
[46] 刘明菲．谈物流企业顾客服务绩效评价体系的建立．商业时代，2005(18)．
[47] 郁俊莉．主成分分析法在建立工业企业经济效益综合评价指标体系中的应用．地质技术经济管理，1998，20(3)：31-35．
[48] 于秀林，任雪松．多元统计分析．北京：中国统计出版社，1999．
[49] 郝黎仁，樊元．SPSS实用统计分析．北京：中国水利水电出版社，2002．
[50] 熊国强，党兴华．具有非正值评价指标的DEA方法及其应用．系统工程理论方法应用，2003(12)：153-156．
[51] 曹阳龙，史本山．基于主成分分析的DEA复合评价模式及其应用研究．现代管理科学，2006(9)．
[52] Coelli, T.. A Guide to DEAP Version 2.1: A Data Envelopment Analysis (Computer). CEPA Working Paper 96/08, Unisversity of New England, Australia.
[53] 郝海，顾培亮，卢奇．带有评价准则的数据包络分析模型．天津大学学报，2002(9)．
[54] 马占新．样本数据包络面的研究与应用．系统工程理论与实践，2003，12．
[55] Coelli. T J. A guide to FRONTIER Version 4.1: A computer program for stochastic frontier production and cost function estimation[EB/OU], http://www.une.edu.au/econometrics/cepa.htm.96/07.