세계 주요 천연가스업체의 효율성 및 생산성 분석: 2단계 DEA 모델과 Malmquist 생산성 지수 모델을 중심으로

초록

Global energy policy is shifting from the past economy-oriented policy to energy security, and recently into the post-2020 era, centered on environment and safety. This change in the global energy paradigm has led to global interest in natural gas, which is considered to be one of the major green and low carbon energy sources. Therefore, comparing and analyzing the efficiency and productivity among the major natural gas companies will be an effective way to build a competitive strategy for natural gas companies. In order to improve the competitiveness of natural gas companies based on the analysis results, benchmarking targets for each company were selected and the target values were derived through linear analysis of input variables and output variables. This study utilizes the two-step Data Envelopment Analysis (DEA) method and the Malmquist Productivity Index (MPI) to analyze the efficiency and productivity of 14 major global natural gas companies. For this purpose, efficiency input and output factors related to efficiency of natural gas companies were selected and the data for 2013-2016 was collected from each company 's annual report and analyzed for efficiency and productivity. In addition, benchmarking targets for inefficient firms were selected to derive inputs for the target level. The results of this study are summarized as follows. First, the efficiency of each DMU in the DEA is subdivided through CCR and BCC models and analyzed by TE (Technical Efficiency), PTE (Pure Technical Efficiency) and SE (Scale Efficiency). As a result, we derive what the inefficient DMU is and what causes the inefficiency. First, the DEA results of the natural gas companies show that the efficiency of the CCR model is about 36%, the efficiency of the BCC model is about 64%, and the efficiency of the SE model is about 36%. This is because the overall efficiency level of natural gas companies is low and is effective in the BCC model to diagnose the internal operation efficiency, but it can be interpreted as ineffective in the CCR model of the scale efficiency. In the end, in order to improve the efficiency of natural gas companies, it would be desirable to first reduce scale inefficiency. Second, as a result of the first-step DEA, KOGAS, Centrica, etc. are derived as efficient companies while Tokyo Gas and Osaka Gas are inefficient companies. In order to analyze the causes of efficiency and inefficiency derived earlier, we conducted a second-step fundamental DEA. In terms of the number employees, KOGAS, INPEX, etc. In this study, it is suggested that not only the first-step DEA but also the second-step DEA are needed in the efficiency analysis because the inefficiency can occur in the second-step DEA even if it is efficient in the first-step DEA. Third, on the other hand, in the case of KOGAS, the rate of change of the overall productivity index decreased by 24% per year in relation to the MPI measurement result. This can be interpreted as the rate of technological change has decreased by 24% per year, which means that the efficiency of the technology is much lower than that of other natural gas companies. In other words, it is a result different from the static analysis result through the DEA. Therefore, this study suggests that DEA and MPI are needed simultaneously in the efficiency analysis because the productivity of DEA can be low even if it is efficient. Fourth, we show how to adjust input variables by analyzing the improvement goal of inefficient DMU. According to the analysis, DMU13 companies can reduce their input variable assets of $5,266millionandthenumberofemployeestoabout790,whileDMU2firmscanbeconvertedintoefficientcompaniesthroughmanagementstrategiesthatreduceassetsto$ 19,197 million and the number of employees to about 4,686. As a result, this study proposes various diagnostic methods to improve the efficiency of major natural gas companies in the world, identifies the ineffective causes, and provides a target value and a response strategy to improve efficiency. The result of this study can be used as a decision-making data for establishing management efficiency plan to develop efficient enterprise into ultra - efficient enterprise, not limited to efficiency of inefficient

키워드