Ranking efficient DMUs using cooperative game theory

The problem of ranking Decision Making Units (DMUs) in Data Envelopment Analysis (DEA) has been widely studied in the literature. Some of the proposed approaches use cooperative game theory as a tool to perform the ranking. In this paper, we use the Shapley value of two different cooperative games in which the players are the efficient DMUs and the characteristic function represents the increase in the discriminant power of DEA contributed by each efficient DMU. The idea is that if the efficient DMUs are not included in the modified reference sample then the efficiency score of some inefficient DMUs would be higher. The characteristic function represents, therefore, the change in the efficiency scores of the inefficient DMUs that occurs when a given coalition of efficient units is dropped from the sample. Alternatively, the characteristic function of the cooperative game can be defined as the change in the efficiency scores of the inefficient DMUs that occurs when a given coalition of efficient DMUs are the only efficient DMUs that are included in the sample. Since the two cooperative games proposed are dual games, their corresponding Shapley value coincide and thus lead to the same ranking. The more an ef- ficient DMU impacts the shape of the efficient frontier, the higher the increase in the efficiency scores of the inefficient DMUs its removal brings about and, hence, the higher its contribution to the overall discriminant power of the method. The proposed approach is illustrated on a number of datasets from the literature and compared with existing methods

Multicriteria ranking using weights which minimize the score range

Various schemes have been proposed for generating a set of non-subjective weights when aggregating multiple criteria for the purposes of ranking or selecting alternatives. The maximin approach chooses the weights which maximise the lowest score (assuming there is an upper bound to scores). This is equivalent to finding the weights which minimize the maximum deviation, or range, between the worst and best scores (minimax). At first glance this seems to be an equitable way of apportioning weight, and the Rawlsian theory of justice has been cited in its support.We draw a distinction between using the maximin rule for the purpose of assessing performance, and using it for allocating resources amongst the alternatives. We demonstrate that it has a number of drawbacks which make it inappropriate for the assessment of performance. Specifically, it is tantamount to allowing the worst performers to decide the worth of the criteria so as to maximise their overall score. Furthermore, when making a selection from a list of alternatives, the final choice is highly sensitive to the removal or inclusion of alternatives whose performance is so poor that they are clearly irrelevant to the choice at hand

A Super Efficiency Model for Product Evaluation

This study applies a Super Efficiency Data Envelopment Analysis model to evaluate the efficiency of cars sold on the German market. Efficiency is conceptualized from a customers' perspective as a ratio of outputs that customers obtain from a product relative to inputs that customers have to invest. The output side is modeled as a set of customer-relevant parameters such as performance attributes but also nonfunctional benefits and brand strength. More than 60% of the cars are efficient but the analysis shows marked differences regarding their degree of Super Efficiency. Super Efficiency indicates the extent to which the efficient products exceed the efficient frontier formed by other efficient units. Based on the parameter weights, segments of cars with a particular mix of characteristics can be identified; cars with a comparative advantage relative to their competitors who provide the same mix are characterized as the reference points within a given segment.Customer Value, Data Envelopment Analysis (DEA), Marketing Efficiency, Product Marketing, Super Efficiency Model

New resonance approach to competitiveness interventions in lagging regions: the case of Ukraine before the armed conflict

Regional competitiveness is considered to be an alternative basis for the determination of regional interventions. However, the composite competitiveness indicator is quite sensitive to the weights of sub-indicators, no matter what methodology is being used. To avoid this uncertainty in the determination of regional interventions, we proposed a new non-compensatory resonance approach that is focused on the hierarchical coincidence between weaknesses of NUTS 1 and NUTS 2 regions measuring the extensive and intensive components of competitiveness. Such a coincidence, being perceived as a resonance effect, is supposed to increase the effectiveness of interventions triggering synergetic effects and stirring up local regional potentials. The components of competitiveness are obtained through synthesising DEA methodology and Hellwig's index, correspondingly focusing on the measurement of technical efficiency and resource level. In analysing Ukrainian regions, no correlation between resonance interventions and the composite competitiveness indicator or GDP per capita was found, pointing toward a completely different direction in resonance approach. In western Ukraine, the congestion of six NUTS 2 regions was defined as a homogeneous area of analogous resonance interventions focused on improving business efficiency.Web of Science171562

Internet Based Benchmarking

We discuss the design of interactive, internet based benchmarking using parametric (statistical) as well as non-parametric (DEA) models. The user receives benchmarks and improvement potentials. The user is also given the possibility to search different efficiency frontiers and hereby to explore alternative improvement strategies. An implementation of both a parametric and a non parametric model are presented.Research and Development/Tech Change/Emerging Technologies,

Data envelopment analysis (DEA) and financial ratios : a pro-stakeholders’ view of performance measurement for sustainable value creation of the wind energy

Purpose: The purpose of the paper is to explore business performance in a rather sensitive sector that equally combines economic, environmental and social dimensions. The paper investigates the efficiency of wind farm companies, in a framework of pursuing more diverse stakeholders’ interests Design/Methodology/Approach: Ratios and DEA approaches are combined to measure economic efficiency among the DMUs of a sample of wind farms, using data from their financial statements. Findings: Productivity and effectiveness comprise the performance measured by the economic efficiency. We show that by choosing inputs and outputs that are closely related in forming an appropriate financial ratio, it helps to design and explain more fully the impact of a policy intervention aiming at improving economic efficiency. DEA supplements ratios to design, implement and assess a strategy of benchmarking towards bolstering performance, that favors a wider range of stakeholders. Originality/Value: The study provides an in-depth insight into using Data Envelopment Analysis and financial ratios to study economic efficiency. The approach combines economic, social and environmental dimensions (indirectly) of performance, and the composite ratio Return on Total Assets (ROTA). The analysis caters the specific features of the sector renewable energy and their diverse stakeholders.peer-reviewe

Financial benchmarking of transportation companies in the New York Stock Exchange (NYSE) through data envelopment analysis (DEA) and visualization

In this paper, we present a benchmarking study of industrial transportation companies traded in the New York Stock Exchange (NYSE). There are two distinguishing aspects of our study: First, instead of using operational data for the input and the output items of the developed Data Envelopment Analysis (DEA) model, we use financial data of the companies that are readily available on the Internet. Secondly, we visualize the efficiency scores of the companies in relation to the subsectors and the number of employees. These visualizations enable us to discover interesting insights about the companies within each subsector, and about subsectors in comparison to each other. The visualization approach that we employ can be used in any DEA study that contains subgroups within a group. Thus, our paper also contains a methodological contribution

An evaluation of economic efficiency of finnish regions by DEA and Tobit models

Private sector economic efficiency of 83 Finnish regions (labour market areas) in 1988-1999 is investigated by a two stage procedure. In the first stage non-parametric Data Envelopment Analysis (DEA) is used for calculating a best practise production frontier and relative efficiency scores for the regions. Outputs are regional value added and personal direct real income from employment. Inputs of the study include capital stock, employment by education level, years of schooling and volume of local public sector activity. In the second stage, regional differences in efficiency are studied by explaining inefficiency scores (1-efficiency scores) by econometric Tobit models. Here the independent variables include factors which were not explicitly included in the DEA-models such as size (population), location (economic distance) and production structure of regions.

Efficiency of French football clubs and its dynamics

In the paper we evaluate the efficiency of French football clubs (Ligue 1) from 2004 to 2007 using Data Envelopment Analysis (DEA) with « Assurance Region ». Then, we study the dynamics of clubs’ performances. Contrary to previous works on other championships, best teams in competition or most profitable clubs are not the most efficient units in our sample. High average scores show that French First League is efficient. The first source of inefficiency in the Ligue 1 is linked to size problems and over-investments. Despite an average club performance stable over the period, we exhibit a deterioration of conditions in which clubs operate.Ligue 1, efficiency scores, Data Envelopment Analysis (DEA), Malmquist index, over-investment