Benchmark optimization and attribute identification for improvement of container terminals

The aim of this paper is to optimize the benchmarks and prioritize the variables of decision-making units (DMUs) in data envelopment analysis (DEA) model. In DEA, there is no scope to differentiate and identify threats for efficient DMUs from the inefficient set. Although benchmarks in DEA allow for identification of targets for improvement, it does not prioritize targets or prescribe level-wise improvement path for inefficient units. This paper presents a decision tree based DEA model to enhance the capability and flexibility of classical DEA. The approach is illustrated through its application to container port industry. The method proceeds by construction of multiple efficient frontiers to identify threats for efficient/inefficient DMUs, provide level-wise reference set for inefficient terminals and diagnose the factors that differentiate the performance of inefficient DMUs. It is followed by identification of significant attributes crucial for improvement in different performance levels. The application of this approach will enable decision makers to identify threats and opportunities facing their business and to improve inefficient units relative to their maximum capacity. In addition, it will help them to make intelligent investment on target factors that can improve their firms’ productivity.     

A two-stage DEA model to evaluate the overall performance of Canadian life and health insurance companies

A two-stage data envelopment analysis (DEA) model is created to provide valuable managerial insights when assessing the dual impacts of operating and business strategies for the Canadian life and health (L&H) insurance industry. This new model allows integration of the production performance and investment performance for the insurance companies and provides management overall performance evaluation and how to achieve efficiency systematically for the insurers involved. The results also show that the Canadian L&H insurance industry operated fairly efficiently during the period examined (the year 1998). In addition, the scale efficiency in the Canadian L&H insurance industry is found in this study.     

Novel constraints satisfaction models for optimization problems in container terminals

The growth of containerization and transporting goods in containers has created many problems for ports. In this paper, we systematically survey a literature over problems in container terminals. The operations that take place in container terminals are described and then the problems are classified into five scheduling decisions. For each of the decisions, an overview of the literature is presented. After that, each of the decisions is formulated as Constraint Satisfaction and Optimization Problems (CSOPs). The literature also includes simulations and performance in container terminals. To evaluate any solution methods to the decisions and to measure its efficiency, several indicators are suggested.     

Technical efficiency and economies of scale: A non-parametric analysis of REIT operating efficiency

This study measures technical efficiency and economies of scale for real estate investment trusts (REITs) by employing data envelopment analysis (DEA), a linear-programming technique. Using data from the National Association of Real Estate Investment Trusts (NAREITs) for the years 1992–1996, we find that REITs are technically inefficient, and the inefficiencies are a result of both poor input utilization and failure to operate at constant returns to scale. With respect to scale inefficiency, most REITs are operating at increasing returns to scale, suggesting that REITs could improve performance through expansion. Moreover, we employ regression analysis to determine what characteristics influence the efficiency measures obtained. The results show that internal REIT management is positively related to all measures of efficiency. Increasing leverage is negatively related to REIT input utilization. Finally, increasing REIT diversification across property types enhances scale efficiency (SE) but reduces input usage efficiency.     

我国跨境电商上市企业经营效率研究——基于三阶段DEA-Malmquist指数方法

以33家跨境电商上市企业为研究对象,基于三阶段DEA和Malmquist指数模型从静态和动态两个角度出发,对2013-2018年我国跨境电商上市企业的综合效率、全要素生产效率指数及两者的分解效率进行测度并对比分析,同时分析了宏观经济环境、政府干预和企业自身经营情况等因素对经营效率的影响.结果表明:样本企业经营效率存在个体差异,且外部环境对其经营效率具有显著影响;纯技术效率和规模效率参差不齐是导致综合技术效率分化严重的主要原因.基于此,跨境电商上市企业应积极配合政策变化,加强营销能力,及时调整经营规模,以防范宏观经济风险,提高企业经营效率.     

Determining a sequence of targets in DEA

Data Envelopment Analysis (DEA) can be used for assessing the relative efficiency of a number of operating units, finding, for each inefficient unit, a target operating point lying on the efficient frontier. Most DEA models project an inefficient unit onto a most distant target, which makes its attainment more difficult. In this paper, we advocate determining a sequence of targets, each one within an appropriate, short distance of the preceding. The proposed Constant Returns to Scale approach has two interesting features: (a) the sequence of targets ends in the efficient frontier and (b) the final, efficient target is generally closer to the original unit than the one-step projection is.     

Using DEA and simulation in guiding operating units to improved performance

The potential for the use of DEA and simulation in a mutually supporting role in guiding operating units to improved performance is presented. An analysis following a three-stage process is suggested. Stage one involves obtaining the data for the DEA analysis. This can be sourced from historical data, simulated data or a combination of the two. Stage two involves the DEA analysis that identifies benchmark operating units. In the third stage simulation can now be used in order to offer practical guidance to operating units towards improved performance. This can be achieved by the use of sensitivity analysis of the benchmark unit using a simulation model to offer direct support as to the feasibility and efficiency of any variations in operating practices to be tested. Alternatively, the simulation can be used as a mechanism to transmit the practices of the benchmark unit to weaker performing units by building a simulation model of the weaker unit to the process design of the benchmark unit. The model can then compare performance of the current and benchmark process designs. Quantifying improvement in this way provides a useful driver to any process change initiative that is required to bring the performance of weaker units up to the best in class.     

How to generate regularly behaved production data? A Monte Carlo experimentation on DEA scale efficiency measurement

Monte Carlo experimentation is a well-known approach used to test the performance of alternative methodologies under different hypotheses. In the frontier analysis framework, whatever the parametric or non-parametric methods tested, experiments to date have been developed assuming single output multi-input production functions. The data generated have mostly assumed a Cobb–Douglas technology. Among other drawbacks, this simple framework does not allow the evaluation of DEA performance on scale efficiency measurement. The aim of this paper is twofold. On the one hand, we show how reliable two-output two-input production data can be generated using a parametric output distance function approach. A variable returns to scale translog technology satisfying regularity conditions is used for this purpose. On the other hand, we evaluate the accuracy of DEA technical and scale efficiency measurement when sample size and output ratios vary. Our Monte Carlo experiment shows that the correlation between true and estimated scale efficiency is dramatically low when DEA analysis is performed with small samples and wide output ratio variations.     

Reconciling ratio analysis and DEA as performance assessment tools

The assessment of operational performance remains a fundamental challenge both in practice and in theory. Data envelopment analysis (DEA) is one method developed in production economic theory and applied by researchers to study groups of enterprises. In practice, individual enterprises almost universally rely on simple output–input ratios. Each approach has its strengths and weaknesses, but the theoretical connection between the two has not been fully articulated. This paper uses the framework of DEA to establish a mathematical relationship between DEA efficiency scores and corresponding ratio analysis. The relationship can be expressed as a product of seven components: technical efficiency, technical change, scale efficiency, input slack factor, input substitution factor, output slack factor and output substitution factor.     

Costs and efficiency of higher education institutions in England: a DEA analysis

As student numbers in higher education in the UK have expanded during recent years, it has become increasingly important to understand its cost structure. This study applies Data Envelopment Analysis (DEA) to higher education institutions in England to assess their cost structure, efficiency and productivity. The paper complements an earlier study that used parametric methods to analyse the same panel data. Interestingly, DEA provides estimates of subject-specific unit costs that are in the same ballpark as those provided by the parametric methods. The paper then extends the previous analysis and finds that further student number increases of the order of 20–27% are feasible through exploiting operating and scale efficiency gains and also adjusting student mix. Finally the paper uses a Malmquist index approach to assess productivity change in the UK higher education. The results reveal that for a majority of institutions productivity has actually decreased during the study period.