Tariff structure of Japanese electric power companies: An empirical analysis using DEA

This research explores a Marginal Cost (MC)-based pricing system, using Data Envelopment Analysis (DEA). This DEA technique is widely applied for performance analysis and efficiency measurement in public and private sectors. This article does not follow such a previous research direction, rather directing itself towards the new measurement of MC and Ramsey prices of multiple electric power services. As an important case study, this research applies the proposed DEA approach to examine how much the current tariffs of Japanese electric power services deviate from these MC and Ramsey prices. This type of research has never been explored in Japan, even though such a research effort is needed by many policy makers and consumers. It is hoped that our empirical findings can serve as a policy-making basis for guiding the Japanese electric power industry. This study believes that this Japanese experience is useful for other nations, such as Asia-Pacific countries, where many public industries are regulated by their governments.     

A multiplier bound approach to assess relative efficiency in DEA without slacks

In this paper, we propose a new approach to deal with the non-zero slacks in data envelopment analysis (DEA) assessments that is based on restricting the multipliers in the dual multiplier formulation of the used DEA model. It guarantees strictly positive weights, which ensures reference points on the Pareto-efficient frontier, and consequently, zero slacks. We follow a two-step procedure which, after specifying some weight bounds, results in an “Assurance Region”-type model that will be used in the assessment of the efficiency. The specification of these bounds is based on a selection criterion among the optimal solutions for the multipliers of the unbounded DEA models that tries to avoid the extreme dissimilarity between the weights that is often found in DEA applications. The models developed do not have infeasibility problems and we do not have problems with the alternate optima in the choice of weights that is made. To use our multiplier bound approach we do not need a priori information about substitutions between inputs and outputs, and it is not required the existence of full dimensional efficient facets on the frontier either, as is the case of other existing approaches that address this problem.     

Past-present-future Intertemporal DEA models

It has been well recognized that to thoroughly evaluate a firm’s performance, the evaluator must assess not only its past and present records but also future potential. However, to the best of our knowledge, there are no data envelopment analysis (DEA)-type models proposed in the literature that simultaneously take past, present and, especially, future performance indicators into account. Hence, this research aims at developing a new type of DEA model referred to as Intertemporal DEA models that can be used to fully measure a firm’s efficiency by explicitly considering its key inputs and outputs involving the past-present-future time span. In this research, the proposed Intertemporal DEA models are applied to the performance evaluation of high-tech Integrated Circuit design companies in Taiwan to demonstrate their advantages over other DEA models that ignore intertemporal efficiency.     

Methodological comparison between DEA (data envelopment analysis) and DEA–DA (discriminant analysis) from the perspective of bankruptcy assessment

This study compares DEA (data envelopment analysis) with DEA–DA (discriminant analysis) in terms of bankruptcy assessment. Recently, many DEA researchers propose a use of DEA as a quick-and-easy tool to assess corporate bankruptcy. Meanwhile, other DEA researchers discuss a use of DEA–DA for bankruptcy-based financial analysis. The two groups are very different from the conventional use of DEA because we have long applied DEA to the measurement of operational performance, or productivity analysis. The two research groups open up a new application area (bankruptcy-based financial assessment) for DEA. This study discusses methodological strengths and weaknesses of DEA and DEA–DA from the perspective of corporate failure. The proposed comparative analysis has the three main criteria: (a) how to handle negative data in financial variables, (b) how to handle data imbalance between default and non-default firms, and (c) how to identify a failure process over time. This study finds that DEA is a managerial tool for the initial assessment of corporate failure and DEA is useful for busy corporate leaders and financial managers. In contrast, DEA–DA is useful for researchers and individuals who are interested in the detailed assessment of bankruptcy and its failure process in a time horizon.     

基于FA-DEA聚类的模式分析方法及在我国软件园研究中的应用

模式分析方法日益成为现代经济管理研究的主流方法之一,但是目前关于模式分析的结构化方法的研究还不多见.本文提出了基于FA-DEA聚类的模式研究结构化方法,这个方法区别于以往文献的是采用了混合聚类方法,综合运用关键因素的绝对量信息和效率的相对量信息.基于上述方法,本文确定了我国软件园发展的三种发展模式.并对三个模式的条件、特征以及它们之间的分界与演进做了详细的描述.最后,给出了若干建议.     

Content analysis of data envelopment analysis literature and its comparison with that of other OR/MS fields

Content analysis was performed on the data envelopment analysis (DEA) literature appearing in refereed journals. The extant DEA literature, reported in Gattoufi et al was subdivided into two ways. The first considers all articles appearing during the life cycle of 22 selected major DEA publishing journals. The second considers all post-1995 DEA articles. Content was judged on the basis of a two-point scale representing advancements in theory; a five-point scale indicating contributions to practice; and on seven distinguishable strategies applied by the authors in pursuing their research. Lastly, DEA was compared with similarly obtained results describing the life cycle literatures of flow shop scheduling and of cell manufacturing. This analysis has proven the vitality, robustness, and real-world grounding of DEA on its own and vis-à-vis other OR/MS sub-disciplines. Moreover, the breadth of its diffusion into other disciplines and professions has been shown to be extraordinary.     

A combined use of DEA (Data Envelopment Analysis) with Strong Complementary Slackness Condition and DEA–DA (Discriminant Analysis)

This study discusses a combined use of DEA (Data Environment Analysis) with SCSC (Strong Complementary Slackness Condition) and DEA–DA (Discriminant Analysis). Many studies use DEA to evaluate the performance of various organizations in private and public sectors. A conventional use of DEA is not perfect because it still contains zero in many multipliers. This implies that DEA does not fully utilize information on all inputs and outputs. As a result, DEA produces many efficient organizations. To overcome the methodological difficulty, this study proposes a new use of DEA/SCSC and DEA–DA to reduce the number of efficient organizations.     

Privatization and performance: evidence from telecommunications sector

There is a continuing debate on whether or not ownership changes accompanying privatization of state-owned enterprises (SOEs) lead to performance improvements of such firms. Since profitability measures alone are possibly inappropriate for SOEs, we address this very significant current question using a novel approach. We test the performance of a large sample of telecommunication firms around the world, 4 years before and 4 years after privatization, using two parallel measures namely financial performance measures and production performance measures. Our purpose is to identify if both approaches could corroborate stronger evidence for or against maintained hypothesis of performance improvements after privatization. The overall finding for this industry indicates significant improvements in both financial and production performance after privatization. Further research is warranted to extend this parallel-methods approach to firms in other industries.     

Chapter 7 DEA multiplier analytic center sensitivity with an illustrative application to independent oil companies

The setE of extreme points which are also efficient are of basic importance in defining the efficiency frontier, from which the observations for all other DMUs are evaluated in DEA. A significant question which we address is “What variations in the data can be tolerated before the membership inE is changed?” This topic is explored using (1) a simple illustrative example, and (2) production data for 30 independent oil companies during the period 1983–1985. Data were allowed to vary simultaneously for all observations and in different subsets determined by random drawings of data for points both inE and not inE. The results were found to be robust in this study, thereby lending further support to earlier studies which also found these classifications into efficient and inefficient performers to be robust in DEA. Technical developments for these new methods of sensitivity analysis are supplied. These developments feature an application of analytic center (interior point) algorithms which ensure that the Strong Complementary Slackness Condition (SCSC) is fulfilled. The solutions satisfy a mathematical condition called “centrality”. Generally, the solutions are at interior points calledanalytic centers. At these interior points, continuity of the input-output ratios ensures that DMUs inE remain inE for at least small relative variations in the data, while empirically these properties have been found to extend to much larger variations in the data sets.     

国企重组中的战略重组与整合机制研究——以中国海洋航空集团公司为例

国有企业在我国国民经济发展中占有重要地位,国有企业重组是解决国有企业面临的诸多问题的重要方式,而战略重组与整合是国有企业重组中的重要内容。本文首先提出了国有企业重组中的战略重组与整合机制,然后以中国海洋航空集团公司的成功重组为案例,探讨并总结了其战略重组的成功经验和做法,这能为其它国有企业重组提供重要参考,还有利于丰富国有企业重组的相关理论。