Data envelopment analysis (DEA) - Thirty years on

In this paper, we examine the cross-efficiency concept in data envelopment analysis (DEA). Cross efficiency links one decision-making unit's (DMU) performance with others and has the appeal that scores arise from peer evaluation. However, a number of the current cross-efficiency approaches are flawed because they use scores that are arbitrary in that they depend on a particular set of optimal DEA weights generated by the computer code in use at the time. One set of optimal DEA weights (possibly out of many alternate optima) may improve the cross efficiency of some DMUs, but at the expense of others. While models have been developed that incorporate secondary goals aimed at being more selective in the choice of optimal multipliers, the alternate optima issue remains. In cases where there is competition among DMUs, this situation may be seen as undesirable and unfair. To address this issue, this paper generalizes the original DEA cross-efficiency concept to game cross efficiency. Specifically, each DMU is viewed as a player that seeks to maximize its own efficiency, under the condition that the cross efficiency of each of the other DMUs does not deteriorate. The average game cross-efficiency score is obtained when the DMU's own maximized efficiency scores are averaged. To implement the DEA game cross-efficiency model, an algorithm for deriving the best (game cross-efficiency) scores is presented. We show that the optimal game cross-efficiency scores constitute a Nash equilibrium point.

The DEA Game Cross-Efficiency Model and Its Nash Equilibrium

http://www.deafrontier.net/papers/IJPE_Cross.pdf

Alternative secondary goals in DEA cross-efficiency evaluation

Cross-efficiency evaluation has long been suggested as an alternative method for ranking decision making units (DMUs) in data envelopment analysis (DEA). This paper proposes a neutral DEA model for cross-efficiency evaluation. Unlike the aggressive and benevolent formulations in cross-efficiency evaluation, the neutral DEA model determines one set of input and output weights for each DMU from its own point of view without being aggressive or benevolent to the other DMUs. As a result, the cross-efficiencies computed in this way are more neutral, neither aggressive nor benevolent. The neutral DEA model is then extended to a cross-weight evaluation, which seeks a common set of weights for all the DMUs. Numerical examples are provided to illustrate the applications of the neutral DEA model and the cross-weight evaluation in DEA ranking.

A neutral DEA model for cross-efficiency evaluation and its extension

Research highlights? The cross efficiency evaluation method was reviewed and its main shortcomings were described when the ultimate average cross efficiency scores were used. ? The assumption of average was eliminated and the Shannon entropy was utilized to determine the weights for the ultimate cross efficiency scores. ? An empirical example was illustrated and some future research directions were pointed out. This paper firstly reviews the cross efficiency evaluation method which is an extension tool of data envelopment analysis (DEA), then we describe the main shortcomings when the ultimate average cross efficiency scores are used to evaluate and rank the decision making units (DMUs). In this paper, we eliminate the assumption of average and utilize the Shannon entropy to determine the weights for ultimate cross efficiency scores, and the procedures are introduced in detail. In the end, an empirical example is illustrated to examine the validity of the proposed method. Some future research directions are also pointed out.

Determination of weights for ultimate cross efficiency using Shannon entropy

Cross-evaluation has been touted as a powerful extension of Data Envelopment Analysis that provides, not only a unique ordering among the Decision Making Units (DMUs), but also eliminates unrealistic weighting schemes without requiring the elicitation of weight restrictions from application area experts. The goal of this paper is to prove, in the single-input, multiple-output case, cross-evaluation implicitly uses a single fixed set of weights. We demonstrate how this unseen fixed set of weights may still be unrealistic.

/pdf/the-fixed-weighting-nature-of-a-cross-evaluation-model-27p0xklecc.pdf

The Fixed Weighting Nature of A Cross-Evaluation Model

https://pdxscholar.library.pdx.edu/cgi/viewcontent.cgi?article=1040&context=etm_fac

Further Examination of Moore's Law with Data Envelopment Analysis

This article explores the impact of project management (PM) standardization on project effectiveness. It emerged from the observation of three companies: Intel, Armstrong World Industries, and Oreg...

Impact of Project Management Standardization on Project Effectiveness

How do you measure the rate of change in a product category? Simple measures such as product life cycles do not necessarily measure performance change over time. Similarly, extrapolating a single performance criterion does not account for design tradeoffs. This paper builds upon previous work of using data envelopment analysis (DEA) to measure incremental innovation in technology by applying it to the online transaction processing market. A variable returns to scale DEA model is utilized to determine an annual rate of change in benchmarks based on data provided by the Transaction Processing Performance Council. This rate of change may then be used to forecast possible future performance trendsetters of the TPC. The underlying model of a database system used for this study was based upon price to performance, the DEA technique demonstrated here allows for much more complex models to be examined.

/pdf/assessing-the-rate-of-change-in-the-enterprise-database-29vg73gp1v.pdf

Assessing the rate of change in the enterprise database system market over time using DEA

Technology Forecasting with Data Envelopment Analysis (TFDEA) offers an effective means to determine technological capability over time without the burden of fixed a-priori weighting schemes. However, a commonly used extension of TFDEA can create situations where there are multiple feasible solutions. This can introduce challenges in reproducing results for practitioners. This paper resolves this issue by extending TFDEA in a manner consistent with common Data Envelopment methodologies to ensure consistent and reproducible results. The extension is then demonstrated numerically using a well-known data set of fighter jet technology forecasting in which this issue of multiple optima was first observed.

http://ieeexplore.ieee.org/iel5/6008221/6017605/06017650.pdf

Lane Inman

Papers

The Fixed Weighting Nature of A Cross-Evaluation Model

Further Examination of Moore's Law with Data Envelopment Analysis

Impact of Project Management Standardization on Project Effectiveness

Assessing the rate of change in the enterprise database system market over time using DEA

Resolving the issue of multiple optima in Technology Forecasting using Data Envelopment Analysis