Network DEA: efficiency analysis of organizations with complex internal structure

Efficiency of US airlines: A strategic operating model

Data envelopment analysis (DEA) assumes that inputs and outputs are measured on scales in which larger numerical values correspond to greater consumption of inputs and greater production of outputs. We present a class of DEA problems in which one or more of the inputs or outputs are naturally measured on scales in which higher numerical values represent lower input consumption or lower output production. We refer to such quantities as reverse inputs and reverse outputs. We propose to incorporate reverse inputs and outputs into a DEA model by returning to the basic principles that lead to the DEA model formulation. We compare our method to reverse scoring, the most commonly used approach, and demonstrate the relative advantages of our proposed technique. We use this concept to analyze all 30 Major League Baseball (MLB) organizations during the 1999 regular season to determine their on-field and front office relative efficiencies. Our on-field DEA model employs one output and two symmetrically defined inputs, one to measure offense and one to measure defense. The defensive measure is such that larger values correspond to worse defensive performance, rather than better, and hence is a reverse input. The front office model uses one input. Its outputs, one of which is a reverse output, are the inputs to the on-field model. We discuss the organizational implications of our results.

https://www.jstor.org/stable/info/41770150

Data envelopment analysis with reverse inputs and outputs.

Benchmarking methods, primarily non-parametric techniques such as Data Envelopment Analysis, have become well-established and informative tools for economic regulation, in particular in energy infrastructure regulation. The axiomatic features of the non-parametric methods correspond closely to the procedural and economic criteria for good practice network regulation. However, critique has been voiced against the robustness of best-practice regulation in presence of uncertainty regarding model specification, data definition and collection. Incorrect data may result from structural sources, such as heterogeneous technologies; deterministic approaches applied to stochastic data generation processes or poorly defined scope of activity. Specifically within regulation, reporting may also be biased through individual gaming or collusive behavior, including the intentional provision of absurd data in order to stall or perturb regulatory process (here called maverick reporting). We review three families of outlier detection methods in terms of their function and application using a data set from Swedish electricity distribution, illustrating the different types of outliers, contrasting with the actual analysis ex post. This paper investigates the foundation of the critique both conceptually and by describing the actual state-ofthe-art used in energy network regulation using frontier analysis models in Sweden (2000e2003) and in Germany (2007-). Finally, the paper concludes on the role of outlier detection as a mean to implement regulation with higher robustness.

Ronald N. Norton

Papers

Firm-Specific Productive Efficiency Offsets in the Development of a Price Cap Formula

Accounting for site characteristics in DEA: leveling the playing field