Empirical Research on Logistics Enterprises Efficiency Based on DEA

Transcription

1 Empirical Research on Logistics Enterprises Efficiency Based on DEA SHI Lan, WU Guangwei School of Business Administration, Guangxi University of Finance & Economics, China, Abstract: Efficient evaluation system on the basis of previous studies, the use of the data envelopment analysis (DEA) method for the systematic analysis of the level and efficiency of influencing factors listed logistics company's efficiency and build the evaluation of the efficiency of logistics enterprises in China situation. First, the study concludes that the existing research results at home and abroad, and based on the influencing factors of enterprise efficiency, build a logistics business efficiency evaluation index system; Second, the 10 domestic listed logistics enterprises as the study sample, the use of DEA method of sample enterprises technology efficiency, pure technical efficiency and scale efficiency of the evaluation study, the results showed that: the scale of the low efficiency of logistics enterprises main reason for inefficiency, pure technical efficiency is only part of the logistics enterprises impact further proved that the DEA method in the evaluation of logistics of the scientific method and superiority of the enterprise, the more in-depth evaluation of the efficiency of logistics enterprises have a greater practical significance. Keywords: Data envelopment analysis (DEA), Logistics, Technical efficiency The Performance evaluation of logistics enterprises is in fact the study of the economic benefits and efficiency of logistics enterprises. At present, there are mainly four method of quantitative performance evaluation: Activity-based costing, Analysis Hierarchy Process (AHP), index of tree, and DEA. (1) The biggest advantage of DEA is it does not need to determine the explicit expression of the relationship between the input and output. Instead, by using the observed effective sample data and the linear programming techniques to determine the effective frontier, DEA can get the information of the relative efficiency of each decision-making unit. DEA is highly objectivity, because it excludes a lot of subjective factors. (2) Several practices of both domestic and foreign logistics enterprises have proved that the performance evaluation of logistics companies get better results by using the DEA. 1 Introduction of Data Envelopment Analysis 1.1 Origination of Data Envelopment Analysis Data envelopment analysis (short for DEA) is an efficient analysis method. The French quantitative economist Farrell (1957) first used DEA to measure the efficiency. He used the best practice frontier to determine whether the decision-making unit is effective. In 1978, the well-known operations researcher--professor Charles Burns from the University of Texas in the United States, W.W.Cooper and E.Rhodes formally proposed a new field of operations research: Data envelopment analysis, the model of which is referred to the C 2 R model. The model is used to evaluate the interdepartmental relative effectiveness (therefore known as the DEA efficient). Since the establishment of the first DEA model - C 2 R model (also known as CCR model), the related theories are being further studies and increasingly used. From the application of the relative efficiency and effectiveness evaluation, to the application of the economic system modeling and parameter estimation, to the application in the analysis of costs, revenues and profits forecast and early warning system classification and control, DEA method has now become an important and effective analysis tool and means in many scientific fields like management, systems engineering, decision analysis, and evaluation techniques. Thus, the research of DEA has attracted a number of scholars. Meanwhile, researchers, research institutions and scholars from other fields but with close ties to the DEA invested a lot of 301

2 scientific researches in DEA. A lot of scientific achievements have been made. These new developments not only supplement the theoretical and practical application of the DEA, but also combined DEA with other different mathematical methods to get more comprehensive analysis and detailed results in the practical application. 2 Basic Principles and Models of DEA 2.1 Basic principles of DEA DEA is an evaluation means which based on the concept of relative efficiency, uses convex analysis and linear programming method as tool. DEA uses mathematical programming models to calculate and compare the relative efficiency of the decision-making unit, and makes evaluation for the evaluation objects. DEA can fully consider the optimal input-output program for the decision-making unit, thus, could ideally reflect the information and features of the evaluation object. Meanwhile, DEA has its uniqueness when evaluating the multi-input-output in complex system. 2.2 Introduction of C 2 R model The C 2 R model is the most basic model of DEA. C 2 R model contains the cone axiom of axiom system, therefore, decision-making unit could proportional increase investment elements to expand output, which means the size of the decision-making unit does not have an impact on the efficiency. That is to say if a decision-making unit is valid for the DEA (C 2 R); it is also valid for technology and scale. C 2 R evaluation model: There are many problems in the actual situation which may not strictly satisfy the assumptions of the C 2 R model. When the decision-making unit is not in the best scale, technical efficiency and scale efficiency would mixed up and manifested comprehensive efficiency. When the θ value of a decision-making unit is not equal to 1, we cannot directly see whether it is due to the non-effective technology or the non-effective scale. To further identify the reasons that affect the operational efficiency, pure technical efficiency analysis is needed. 3 Case Study of DEA in the Logistics Industry According to the DEA selection requirements about the decision making unit DMU, after careful screening of the two major Stock Exchange Markets in Shanghai and Shenzhen, the author selected 10 logistics enterprises to apply the DEA method. These 10 companies are listed in the following table: 302

3 Table 1 Sample enterprises Company Name Stock Belongs Province / Location code City /District Main business Bohai Logistics Northern part Qinhuangdao Overland logistics TielongLogistics Northern part Dalian Overland logistics Dudley shares Northern part Harbin Overland logistics Wuhua Shares Northern part Jilin Overland logistics Sinotrans Development Northern part Beijing Aviation Logistics China Shipping Development Central Shanghai Sea / Water Logistics Shanghai Airlines Central Shanghai Aviation Logistics Hainan Airlines Southern part Hainan Aviation Logistics Baiyun Airport Southern part Guangzhou Aviation Logistics COSCO Shipping Southern part Guangzhou Sea / Water Logistics First, using SPSS software to analyze the 10 participating logistics enterprises basic situation from 2008 to 2012, the obtained results are shown in the following table. Table 2 Input and output data sample statistical description Input and output values In 2008 In 2009 In 2010 In 2011 In 2012 Profit from principal operations Mean Fixed assets ($) Cost of principal operations ($) Number of employees (person) Executive compensation ($) Profit from principal operations Maximum Fixed assets ($) Cost of principal operations ($) Number of employees(person) Executive compensation ($) Profit from principal operations Minimum Fixed assets ($) Profit from principal operations Number of employees (person) Executive compensation ($) DEA software DeaP2.1 is used to analysis data in this article. According to the previously introduced DEA data model C 2 R, the author input 10 PARTICIPATING logistics enterprises sample data from into the analysis software (The input and output data of each DMU was sorted out from the annual statements released by enterprises), you can get the efficiency value of these 10 logistics enterprises. The software calculation results are shown in Table 3, the data in the table contains the technical efficiency of the enterprises from 2008 to Pure technical efficiency of the sample enterprises and scale efficiency values are shown in Table

4 Table 3 Comprehensive technical efficiency value of the PARTICIPATING logistics enterprises ( ) Year Company name In 2012 In 2011 In 2010 In 2009 In 2008 Bohai logistics Tielong logistic Dudley shares Wuhua shares Sinotrans Development China Shipping Development Shanghia airlines Hainan airlines Baiyun airlines COSCO Shipping Industry average Annual data Company name Table 4 P TE and SE Table of PARTICIPATING logistics enterprise In 2012 In 2011 In 2010 In 2009 In 2008 PTE SE PTE SE PTE SE PTE SE PTE SE Bohai logistics Tielong losgistics Dudley shares Wuhua shares Sinotrans Development China shipping development Shanghai airlines Hainan airlines Baiyun airport Cosco shipping Table 5 statistics various types of evaluation of efficiency values of participating logistics enterprises from We can see from the table that during these five years, the average efficiency of 10 participating logistics enterprises are divided into 0.515, 0.692, 0.707, 0.676, 0.678, not very high, in the middle to upper level, indicating that there is till much room for improvement for the overall efficiency of China's logistics enterprises. Table 5 Various types of evaluation of efficiency values of participating logistics enterprises Year In In In In In Evaluation value Technical efficiency Pure technical efficiency value Scale efficiency During the five years from , the average value of Pure Technical Efficiency (PTE) is the highest among the three average efficiency, higher than the average technical efficiency (TE) and scale efficiency (SE). It can be inferred that low scale efficiency or scale inefficiency is one of the main 304

5 reasons for inefficient logistics enterprises. Scale inefficiency indicated the irrational allocation of resources in China s logistics enterprises, smaller scale of enterprises amounted to less than the level of economies of scale. Another possible cause for China s logistics enterprise inefficiency is the low pure technical efficiency (PTE) or inefficient pure technical efficiency, but compared to the PTE inefficiency, the impact of SE inefficiency has smaller impact on the efficiency of enterprise. Careful observation of Table 3, Technical efficiency TE=1 illustrates that the two companies are technically efficient in these five years. Shanghai Airlines technical efficiency value is 1 from 2009 to 2012, which means it is technically efficient in the four years. In 2008, the Shanghai Airlines technology efficiency value is low, only TE= In order to further analyze of the reason we inquired the other related data in 2008 and compared it with other year s data of the same type. We discovered that Shanghai Airlines pure technical efficiency and scale efficiency is also relatively low in 2008; especially scale efficiency, which indicated the low technical efficiency in 2008 is due to the poor scale efficiency. Next, let s compare the input and output value of Shanghai Airlines in 2008 and The enterprise s fixed assets in 2008 is $1,332,877,204.00, fixed assets in 2009 is $19,852,777,780.00, the fixed assets in 2009 increased more than 14 times in Accordingly, when other input index data changes little, the main business of the enterprise profits grow with the growth of fixed assets. The main business profit in 2008 is $ 23,931,616.00; main business profit in 2009 grows to $ 651,082, The profit growth rate reached more than 26 times. The author can deduced from these data that the overall size of Shanghai Airlines in 2008 failed to achieve the degree of economies of scale. Small scale means enterprise scale inefficient. The direct impact of the low scale efficiency on the enterprise in 2008 is the embodiment of comprehensive technical inefficiency. In 2009, the enterprises increased investments to expand the scale of operation, fixed assets increased more than 14 times in The growth of the scale makes the growth of scale efficiency of enterprise achieve the ideal of economies of scale. Thus the technical efficiency of Shanghai Airlines increased in 2009, the annual comprehensive technical efficiency is effectively. Take Bohai Logistics as another example. The Bohai Logistics technical efficiency (TE) is in 2010; the pure technical efficiency (PTE) is 1, scale efficiency (SE) is However, in 2011, technical efficiency (TE) is 0.432, pure technical efficiency (PTE) is 0.553, and scale efficiency (SE) is Bohai Logistics technical efficiency in 2010 and 2011 are technical inefficiency (TE2010<1, TE2011<1). Reasons can be found after analyzing the two years enterprise technical inefficiency. In 2010 the pure technical efficiency of the enterprises is efficient (PTE2010, =1), enterprise technical inefficiency is mainly affected by the inefficient scale efficiency (SE2010 <1); in 2011, the technical efficiency inefficient is mainly affected by the pure technical efficiency inefficient. (PTE2011<SE2011 <1) Dudley Shares, Wuhua Shares, and Baiyun Airport s five-year pure technical efficiency is efficient (PTE=1). However, due to the inefficient scale efficiency (SE<1), the ultimate enterprise technical efficiency is inefficiency (TE<1). This shows that the major source of enterprise s technical efficiency inefficient is the inefficiency of the scale efficiency. Therefore, enterprises should be targeted in adjusting the scale, the resource allocation, and strive to achieve the scale economy in order to achieve the technical efficiency of enterprises. 4 Suggestions to Improve the Efficiency of the Logistics Enterprises According to the empirical analysis of the logistics enterprises, we can reach to the conclusion that the firm size, the asset-liability ratio, the concentration of the business, corporate human resources level, remuneration level, and other factors haves significant influence on the efficiency of enterprises. Here the author offered several suggestions on how to improve enterprise efficiency in fixed macro environment: (1) Appropriately expand the scale of assets, which enables the company to achieve scale economy. (2) Improve the level of human resources; enhance the core competitiveness of enterprises. (3) Reasonable control the asset-liability ratio, strengthen financial management. 305

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