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In this chapter, we illustrate the use of data envelopment analysis, an operations research technique, to analyze the financial performance of the seven largest retailers in the United States by benchmarking a set of financial ratios of a firm against its peers. Data envelopment analysis clearly brings out the firms that are operating more efficiently in comparison to other firms in the industry, and points out the areas in which poorly performing firms need to improve.

Data envelopment analysis (DEA) is a multicriteria technique which can take into account multiple inputs and outputs to produce a single aggregate measure of relative efficiency for a set of comparable units. DEA takes into consideration other objectives by including the appropriate variables as part of the DEA model. However, as we will demonstrate, collapsing all the inputs and outputs of several objectives into one aggregate performance measure weakens DEA's ability to discriminate the individual impact of each of these objectives. In this chapter, we apply a multiple objective extension to DEA, called multiple objective DEA (MODEA), which simultaneously controls the weights assigned to the variables found in more than one objective. This MODEA approach more fully measures the impact of each objective and allows the decision-maker to address trade offs among these objectives. The usefulness of the MODEA approach is demonstrated by applying it to the hypothetical example.

The study on output allocative efficiency considering the emission trading is meaningful to allocate emission quota in order to promote production efficiency of industry. This chapter studies the output allocation problem with constraints to profit and pollution goals, and proposes three types of output allocative efficiency measures, including the comprehensive output allocative efficiency, the profit-oriented output allocative efficiency based on pollution constraint, and the pollution-oriented output allocative efficiency based on profit constraint, which aim to maximize the total profit and (or) minimize the total pollution. The proposed measures are used to evaluate the output allocative efficiencies of 32 paper mills along the Huai River in China, and different parameters are tested with sensitivity analysis to examine the changes of optimal output combination. This chapter helps the enterprise to optimize the decision of production and helps the government to formulate a reasonable plan of pollution control and treatment.

An area of increasing importance has been the use of quality measures in the study of health care. One specific application involves the performance of nursing homes. Previous studies using data envelopment analysis (DEA) methodology to study this problem have revealed several problems, including the selection of quality output measures and the assignment of weights to these measures that result in minimizing their impact. In this chapter, we will use weight restrictions as an effective means of including important quality measures in the DEA model and allowing the DEA results to discriminate among high- and low-quality performing nursing homes.

This chapter investigates the financial performance and technical efficiency of the 26 listed firms in the services sector of the Philippine Stock Exchange over the period 1998–2007, using the DuPont system and the super-efficiency data envelopment analysis (SE-DEA). Empirical findings revealed a negative return on equity for the sector and the presence of outliers in the sample. We also verified a robust significant association between the financial and technical performances of the sector.

The chapter offers new significant contributions to knowledge in terms of the multidimensional performance evaluation and the efficiency of the stock market, especially in developing economies, which has not been a well-researched area. Managerial implications are also identified for the improvement of the firms’ management and the usefulness of the SE-DEA model in performance management.

DMUo is efficient if and only if the maximum value of ho is equal to 1. Model (1) is solved for each DMU. Decision makers can use these efficiency ratings to identify those DMUs, which need improvement. A survey of DEA models and applications is available in the work by Charnes, Cooper, Lewin, and Seiford (1995).

Public schools in the United States continue their struggle with the divergent goals of improving performance and reducing spending. For almost a decade, they have been challenged to comply with the federal No Child Left Behind Act (NCLB). In many local districts, those goals have been pursued with the reality of funding reductions, and the problem now exacerbated by budget shortfalls due to the global economic crisis. In the present situation, solutions based on efficiency and economy are worthy of renewed examination.

This chapter employs data envelopment analysis (DEA) in a large-scale study of 447 public school districts in the State of Missouri. It develops a baseline DEA model to measure district efficiencies. Then it classifies districts using a relative wealth variable (rich and poor) and attempts to determine the degree to which that classification changes the baseline model.

The study concludes that using a relative wealth variable in the analysis produces more robust results than the baseline model. It further demonstrates that funding allocation decisions may be improved by including a relative wealth variable in the decision-making processes.