Analyzing the solutions of DEA through information visualization and data mining techniques: SmartDEA framework

Data envelopment analysis (DEA) has proven to be a useful tool for assessing efficiency or productivity of organizations, which is of vital practical importance in managerial decision making. DEA provides a significant amount of information from which analysts and managers derive insights and guidelines to promote their existing performances. Regarding to this fact, effective and methodologic analysis and interpretation of DEA solutions are very critical. The main objective of this study is then to develop a general decision support system (DSS) framework to analyze the solutions of basic DEA models. The paper formally shows how the solutions of DEA models should be structured so that these solutions can be examined and interpreted by analysts through information visualization and data mining techniques effectively. An innovative and convenient DEA solver, SmartDEA, is designed and developed in accordance with the proposed analysis framework. The developed software provides a DEA solution which is consistent with the framework and is ready-to-analyze with data mining tools, through a table-based structure. The developed framework is tested and applied in a real world project for benchmarking the vendors of a leading Turkish automotive company. The results show the effectiveness and the efficacy of the proposed framework

The Tale of Two research Communities: The Diffusion of Research on Productive Efficiency

The field of theoretical and applied efficiency analysis is pursued both by economists and people from operational research and management science. Each group tends to cite a different paper as the seminal one. Recent availability of extensive electronically accessible databases of journal articles makes studies of the diffusion of papers through citations possible. Research strands inspired by the seminal paper within economics are identified and followed by citation analysis during the 20 year period before the operations research paper was published. The first decade of the operations research paper is studied in a similar way and emerging differences in diffusion patterns are pointed out. Main factors influencing citations apart from the quality of the research contribution are reputation of journal, reputation of author, number of close followers; colleagues, “cadres of protégés”, Ph.D. students, and extent of network (“invisible college”). Such factors are revealed by the citing papers. In spite of increasing cross contacts between economics and operations research the last decades co-citation analysis reveals a relative constant tendency to stick to “own camp” references.Farrell efficiency measures, data envelopment analysis, DEA, bibliometry

DEA-based benchmarking models in supply chain management: an application-oriented literature review

Data Envelopment Analysis (DEA) is a mathematical methodology for benchmarking a group of entities in a group. The inputs of a DEA model are the resources that the entity consumes, and the outputs of the outputs are the desired outcomes generated by the entity, by using the inputs. DEA returns important benchmarking metrics, including efficiency score, reference set, and projections. While DEA has been extensively applied in supply chain management (SCM) as well as a diverse range of other fields, it is not clear what has been done in the literature in the past, especially given the domain, the model details, and the country of application. Also, it is not clear what would be an acceptable number of DMUs in comparison to existing research. This paper follows a recipe-based approach, listing the main characteristics of the DEA models for supply chain management. This way, practitioners in the field can build their own models without having to perform detailed literature search. Further guidelines are also provided in the paper for practitioners, regarding the application of DEA in SCM benchmarking

On Diffusion of Ideas in the Academic World: the Case of Spatial Econometrics

Spatial econometrics is a fast-growing field in the series of quantitative disciplines, auxiliaries of economics and related social sciences. Space, friction, interdependence, spatiotemporal components, externalities and many other aspects interact and should be treated adequately in this field. The publication of the Paelinck and Klaassen book in the late 1970s generated virtually the field spatial econometrics This article studies the diffusion of spatial econometrics, through experienced history on the one hand, on the other through bibliometric methods. Although this field was an “Invisible College” up to 2006 (absence of any organization in form of association, conference, journal, etc.), the databases depict a fast diffusion in the past and strong prospects for the future.

Perception of Reverberation in Domestic and Automotive Environments

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The Law of One Price in Data Envelopment Analysis: Restricting Weight Flexibility Across Firms

The Law of One Price (LoOP) states that all firms face the same prices for their inputs and outputs in the competitive market equilibrium. This law has powerful implications for productive efficiency analysis, which have remained unexploited thus far. This paper shows how LoOP-based weight restrictions can be incorporated in Data Envelopment Analysis (DEA). Utilizing the relation between the industry level and the firm level cost efficiency measures, we propose to apply a set of input prices that is common for all firms and that maximizes cost efficiency of the industry. Our framework allows for firm-specific output weights and variable returns-to-scale, and preserves the linear programming structure of the standard DEA. We apply the proposed methodology for evaluating research efficiency of economics departments of Dutch Universities. This application shows that the methodology is computationally tractable for practical efficiency analysis, and that it helps in deepening the DEA analysis.Data Envelopment Analysis; Law of One Price; industry-level efficiency; weight restrictions; research efficiency

Confident-DEA: A Unified Approach For Efficiency Analysis With Cardinal, Bounded And Ordinal Data

This paper proposes an extension to the existing literature in DEA, the authors call Confident-DEA approach. The proposed new approach involves a bi-level convex optimization model, and hence NP-hard, to which a solution method is suggested. Confident-DEA constitutes a generalization of DEA for dealing with imprecise data and hence a potential method for forecasting efficiency. Imprecision in data is defined as two forms, one is bounded data and the second is cardinal data. Complementing the methodology proposed by Cooper et al (1999) which provides single valued efficiency measures, Confident-DEA provides a range of values for the efficiency measures, e.g. an efficiency confidence interval, reflecting the imprecision in data. For the case of bounded data, a theorem defining the bounds of the efficiency confidence interval is provided. For the general case of imprecise data, that is a mixture of ordinal and cardinal data, a Genetic-Algorithm-based meta-heuristic is used to determine the upper and lower bounds defining the efficiency confidence interval. To the best knowledge of the authors, this is the first work combining Genetic algorithms with DEA. In both cases of imprecision, a Monte-Carlo type simulation is used to determine the distribution of the efficiency measures, taking into account the distribution of the bounded imprecise data over their corresponding intervals. Most of previous DEA works dealing with imprecise data implicitly assumed a uniform distribution. Confident-DEA, on the other hand, allows for any type of distribution and hence expands the scope of the analysis. The bounded data used in the illustrative examples are assumed to have truncated normal distributions. However, the methodology suggested here allows for any other distribution for the data

Soft systems methodology: a context within a 50-year retrospective of OR/MS

Soft systems methodology (SSM) has been used in the practice of operations research and management science OR/MS) since the early 1970s. In the 1990s, it emerged as a viable academic discipline. Unfortunately, its proponents consider SSM and traditional systems thinking to be mutually exclusive. Despite the differences claimed by SSM proponents between the two, they have been complementary. An extensive sampling of the OR/MS literature over its entire lifetime demonstrates the richness with which the non-SSM literature has been addressing the very same issues as does SSM