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With rapid social development and deepening division of labor, more and more complex projects are required to be carried out in a team form. When evaluating team performance, previous research has usually treated team as a united entity. However, the operating environment of the team has a significant impact on its members and the interaction between them greatly influences the team's efficiency. To better evaluate team performance, we propose a circle loop to illustrate the relationship between the operating environment of the team and its members. A two-stage DEA model with feedback is developed to evaluate the team performance, together with the efficiencies of the operating environment and team members as well as their impacts on overall efficiency. Various conditions of the team are discussed to illustrate that team performance depends on the assumption of the conditions.

Reviews previous research on the efficiency and performance of financial institutions and uses Siems and Barr’s (1998) data envelopment analysis (DEA) model to evaluate the relative productive efficiency of US commercial banks 1984‐1998. Explains the methodology, discusses the input and output measures used and relates bank performance measures to efficiency. Describes the CAMELS rating system used by bank examiners and regulators; and finds that banks with high efficiency scores also have strong CAMELS ratings. Summarizes the other relationship identified and recommends the use of DEA to help analysts and policy makers understand organizations in greater depth, regulators and examiners to develop monitoring tools and banks to benchmark their processes.

As a Data Envelopment Analysis (DEA) extension tool, cross-evaluation method was developed to evaluate Decision Making Units’ (DMUs) performances in a competitive situation with limited demand. It identifies DMUs with best performances and rank them by applying peer evaluation mode instead of self-evaluation mode. However, it has limitations in efficiency improvement. That is, it fails to give direct information on how to improve efficiencies of the inefficient DMUs. In this chapter, we propose an alternative way to apply cross-evaluation in efficiency improvement. First, an appropriate and feasible suggestion is proposed to minimize the variation between the weights of a DMU's own optimal Charnes-Cooper-Rhodes (CCR) efficiency and the weights guaranteeing its cross-efficiency score. We exploit several transformations to convert nonlinear programming into a linear one. As a result, an overall optimal set of the weights is obtained, which precisely illustrate the preferences of decision makers and exact characteristics of production process of the evaluated DMU. A further discussion is advanced to examine the existence of non-uniqueness of the weights and to differentiate various sets of the optimal weights by suggesting a unique feasible set of multipliers to best represent the alternative weights selection criterion. Moreover, we develop several models to reallocate the inputs and outputs of inefficient DMUs with minimum amelioration as well as consideration of the preference of decision makers. Finally, we apply our models to evaluate competitive advantages of Chinese cities.

The global financial meltdown of late 2008 threatened the survival of many banks, insurance companies, automakers, and other institutions, further contributing to the economic slowdown already underway in the United States and abroad. The ensuing recession has negatively impacted on the airline industry in the United States with losses running into billions. In this chapter, we illustrate the use of data envelopment analysis (DEA), an operations research technique, to analyze the operating efficiency of the US airline industry by benchmarking a set of ratios that assess the operating efficiency of a firm against its peers. DEA clearly brings out the airline(s) that is (are) operating more efficiently in comparison to other airlines in the industry, and points out the areas that poorly performing airlines need to improve.

As of 1982, federal credit unions were allowed to add select employee groups and thus create institutions with multiple-group common bonds. We examine the efficiency of single bond and multiple bond federal-chartered credit unions by using data envelopment analysis (DEA), a non-parametric, linear programming methodology. Results indicate that multiple bond credit unions have better pure technical efficiency than single bond credit unions. However, single bond credit unions appear to be more scale efficient than the multiple bond credit unions. Our results also indicate that members of multiple bond credit unions may derive greater wealth gains than members of single bond credit unions.

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).

Forecasting is an important tool used to plan and evaluate business operations. Regression analysis is one of the most commonly used forecasting techniques for this purpose. Often forecasts are produced based on a set of comparable units such as individuals, groups, departments, or companies that perform similar activities. We apply a methodology that includes a new independent variable, the comparable unit's data envelopment analysis (DEA) relative efficiency, into the regression analysis. In this chapter, we apply this methodology to compare the performance of commercial banks over a 10-year time period.

Data envelopment analysis (DEA) is used to determine the relative efficiency of the top-ranked gynecology departments in the United States as designated by the U.S. News & World Report ranking. DEA is a linear programming base procedure used to determine the relative efficiency of operating units that have similar characteristics. Efficiency scores are calculated by comparing two different input sets to the performance of each gynecological department. Ranking based on DEA more completely and accurately represents gynecological departments. Further, DEA makes it possible to fairly compare specific departments. The new ranking coupled with the efficiency score accrued by each hospital will motivate and guide hospital administrators to improve the performance of hospital gynecology departments by better utilizing expensive resources.

This research deals with the evaluation of the efficiency of consumer freight package delivery. Model inputs include number of delivery and administrative employees, labor hours, operating costs, and number of deliver vehicles. Outputs include number of packages delivered, percent on-time, percent lost, percent damaged, revenue per package, and customer satisfaction. The methodology used to evaluate the efficiency of the decision-making units (DMUs) under consideration is data envelopment analysis (DEA) involving multiple criteria. The inclusion of additional criteria beyond basic DEA efficiency can improve discriminating power between DMUs and also tends to yield more reasonable weights on model inputs and outputs.

Forecasting is an important tool used by businesses to plan and evaluate their operations. One of the most commonly used techniques for forecasting is regression analysis. Often forecasts are produced for a set of comparable units which could be individuals, groups, departments, or companies that perform similar activities such as a set of banks, a group of mangers, and so on. We apply a methodology that includes a new variable, the comparable unit's data envelopment analysis relative efficiency, into the regression analysis. This chapter presents the results of applying this methodology to the performance of commercial banks.