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The determination of input and output factors is a well-known source of pitfalls when applying data envelopment analysis (DEA). The purpose of this paper is to contribute to overcome the respective problems of input/output factor determination related to factor selection, dual-role factors and undesirable factors.

The problems of input/output factor determination are discussed from a goal-oriented perspective, shedding a new light on the role of input/output factors in DEA. This is exemplified by the case of measuring pharmacy stores’ efficiency concerning their goal of customer retention.

The findings suggest to applying a generalized DEA (GDEA). The three steps of this approach include the development of a system of objectives, the derivation of corresponding performance criteria as well as the construction of cost and benefit functions. These functions build the basis for GDEA models, of which one is exemplarily described and applied to the customer retention case.

While traditional DEA implicitly assumes linear cost and benefit functions, GDEA requires to explicitly specifying these functions. In doing so, the approach contributes to solve the problem of factor selection, the problem of dual-role factors and the problem of undesirable factors.

For determining input/output factors in a consistent and transparent manner, it is recommended to apply GDEA in practical benchmarking studies.

GDEA integrates well-known concepts of multi-criteria decision making into traditional DEA. The new approach helps to cope with the challenges of input/output factor determination in DEA.

This chapter presents a data envelopment analysis (DEA) based relative financial strength (RFS) indicator using accounting data that is predictive of stock market performance of public firms. Such an indicator is indispensable in the fundamental analysis of firms for stock portfolio selections. This methodology requires optimally configuring inputs and outputs for the DEA model such that the strength indicator is maximally correlated with observed stock returns. This optimized RFS indicator providing the maximum predictive strength of stock returns is determined by factors such as asset utilization, leverage, profitability, and growth rates, in addition to the well-known factor, book-to-market ratio. Computational evidence is provided using more than 800 firms covering all major sectors of the U.S. stock market. Using quarterly financial data, we employ the RFS indicator to devise portfolios that yield superior financial performance relative to using portfolios of sector-based funds.

The purpose of this paper is to measure the effectiveness of the integration of Europe by benchmarking economic progress made by the participating nations.

Using data envelopment analysis, this paper compares the relative performance of 26 European Union (EU) nations against one another with seven economic variables as the benchmark parameters from 1999 to 2006.

The paper finds that not all the participating nations were equally efficient at the beginning of the economic integration in 1999. Economic integration does help in achieving convergence in economic performance of 26 EU nations, because 18 of the 26 nations were efficient in 2002. However, this paper finds that after 2002, there is a lack of convergence in the performance of 26 EU nations and some nations have performed more efficiently in contrast to other nations.

The paper identifies the member nations that are lagging behind and make recommendations as to how they can improve their performance to bring them at par with other participating nations.

As cable and satellite industry undergoes transformation in the 21st century with the onslaught of innovation-driven changes, it is important to know which company is doing better and which company is falling behind. This study compares the relative performance of eight cable companies using three factors: operating expense for every dollar of operating revenue, earnings before interest, taxes, depreciation, and amortization, and return on assets. We also evaluate the performance of each firm against itself for the period 2010–2013 to see if they show improvement or deterioration in operating efficiency.

The purpose of this paper is to combine the uncertain methods of type-2 fuzzy sets and data envelopment analysis (DEA) evaluation model together. A new type-2 fuzzy DEA efficiency assessment method is established. Then the proposed procedure is applied to the poverty alleviation problem.

The research method is the DEA model, which is an effective method for efficiency assessment of social–economic systems. Considering the existence of the same efficiency values that cannot be ranked in the proposed DEA model, the balance index is introduced to solve the ranking problem of decision-making units effectively.

The results show that the proposed method can not only measure the efficiency of the existence of uncertain information but also deal with the ranking of multiple efficient decision-making units.

This paper selects type-2 fuzzy DEA model to express a lot of uncertain information in efficiency evaluation problems. We use the parameter decomposition method of type-2 fuzzy programming or the type-2 expectation values indirectly. The balance index is proposed to further distinguish the multiple effective decision-making units. Furthermore, this paper selects rural poverty alleviation in Hainan Province as a case study to verify the feasibility of the method. The relative efficiency values in different years are calculated and analyzed.

Healthcare industry, the largest sector of the US economy, is going through a dramatic transformation as the US economy recovers out of the current recession. In this chapter, we use data envelopment analysis, an operations research technique, to benchmark the performance of 12 publicly managed care organizations against one another for the period 2009–2011. We find that only 6 companies out of 12 are 100% efficient. We also identify the areas in which inefficient companies are lagging behind their efficient peers.

The purpose of this study is to address three managerial issues in a retail network under a centralised regime, including resource allocation, expansion and downsizing. In particular, this study investigates how to optimise resource allocation across retailers and how to decide the optimal size of a retail network for an empirical case, where 27 retailers belong to an automobile parts supplier in Taiwan.

The centralised data envelopment analysis (DEA) approach is used to optimise resource allocation among retailers and to seek the possibility of either an expansion and/or downsizing for a retail network.

The case company knows the best way to (re)allocate its available resources and (re)arrange the outcome goals for its own retailers. The 27 retailers are assigned specific targets for input and output adjustments to become Pareto or technically efficient units. Besides, an experiment is designed to measure how changing the number of retailers affects the group efficiency of the retail network. To keep the current aggregated output level of the retail network, the centralised supplier needs at least 24 retailers. However, the retail network with 37 retailers can achieve maximal group efficiency.

Centralised resource allocation is an inherent feature of most supplier-retailer relationships, where the supplier is usually a central planner with the power to allocate available resources among its own retailers. To the best of our knowledge, this is the first study to assess retail performance under a centralised regime.

Resource allocation has always been a critical problem with significant economic relevance. Many industries allocate the resources based on classical methods such as overall equipment effectiveness (OEE) and data envelopment analysis (DEA). The lack of OEE factors’ weight, how it is defined, analyzed, interpreted and compared in OEE and selection of unrealistic weights, self-appraisal and disability of complete ranking in DEA are challenges that are possible to occur. These defects may result in unfair allocation of the resources. This study aims to overcome the mentioned weaknesses.

In this paper, an approach using a set of various DEA models and Nash bargaining solution (NBS) is designed to solve the resource allocation problem based on OEE, among a set of comparable and uniform DMUs (decision-making units) in a fair way.

The results show that a unique Pareto optimal allocation solution is obtained by the proposed DEA–NBS model among the DMUs. This allocation is more acceptable for players, because the allocation results are commonly determined by all DMUs rather than a specific one. Furthermore, the rankings achieved by the utilized methods and TOPSIS (technique for order preference by similarity to ideal solution) are compared by Spearman’s rank correlation coefficient to validate the resource allocation plan. The findings indicate that the DEA–NBS method has the best correlation with the TOPSIS approach.

To the best of authors’ knowledge, no research has considered the use of DEA and NBS with OEE.

The operations research method of data envelopment analysis (DEA) shows promise for assessing radiotherapy treatment plan quality. The purpose of this paper is to consider the technical requirements for using DEA for plan assessment.

In total, 41 prostate treatment plans were retrospectively analysed using the DEA method. The authors investigate the impact of DEA weight restrictions with reference to the ability to differentiate plan performance at a level of clinical significance. Patient geometry influences plan quality and the authors compare differing approaches for managing patient geometry within the DEA method.

The input-oriented DEA method is the method of choice when performing plan analysis using the key undesirable plan metrics as the DEA inputs. When considering multiple inputs, it is necessary to constrain the DEA input weights in order to identify potential plan improvements at a level of clinical significance. All tested approaches for the consideration of patient geometry yielded consistent results.

This work is based on prostate plans and individual recommendations would therefore need to be validated for other treatment sites. Notwithstanding, the method that requires both optimised DEA weights according to clinical significance and appropriate accounting for patient geometric factors is universally applicable.

DEA can potentially be used during treatment plan development to guide the planning process or alternatively used retrospectively for treatment plan quality audit.

DEA is independent of the planning system platform and therefore has the potential to be used for multi-institutional quality audit.

To the authors’ knowledge, this is the first published examination of the optimal approach in the use of DEA for radiotherapy treatment plan assessment.

The economic crisis of 2007–2009 had a major negative impact on financial institutions in general. Health and life insurance industry continues to face growth challenges even six years after the economic crisis. Due to the challenges faced by health and life insurance industry, several companies in this industry have merged and some decided to get out of this business altogether. This study benchmarks 10 life and health insurance companies on the basis of return on equity, investment yield, and loss ratio for the year 2009 and 2014.