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

A Comparative Analysis of Organic and Conventional Farming trough the Italian FADN

This paper presents some results from a wider research on economic and environmental sustainability of organic farming. It aims to compare organic and conventional farming in order to identify some of the main differences between those groups of farms that participated in the official Farm Accountancy Data Network (FADN)-2003. The study is organized in two sections. The first part, after a brief literature review of the most recent statistical methodologies applied to identify the two similar groups of farms, presents some key economic variables (production, costs and revenues) and the most widely-used structural, economic and balance sheet indexes. The second part describes findings from a case study on the Italian fruit-growing sector. A non-parametric input-oriented frontier analysis (Data Envelopment Analysis, DEA)was used to evaluate which technique makes better use of their disposable productive inputs. Findings show that organic farmers can (partially) overcome the productivity gap (with respect to conventional ones) by more efficient use of their inputs (with respect to their own frontier)

Organic and Conventional Farming: a Comparison Analysis through the Italian FADN

This paper shows some results arisen from a wider research on economic and environmental sustainability of organic farming. It focuses on organic and conventional farming comparison through an investigation of Italian FADN data. In order to identify some of the main differences between organic and conventional farms a “distance analysis” has been carried out. The study aims to highlight some of the main characteristics of those two groups of farms to better address differences (if any) in production technology, costs and revenues. Furthermore it shows the findings of a non-parametric efficiency analysis on the Italian olive-growing farms. The purpose is to estimate difference in efficiency and productivity between organic and conventional olive producers. Results reveal that looking at the average values on Invested Areas, conventional farms’ Gross Production is significantly higher than the organic ones, as the Net Margin, as the Net Product and Costs. The average values on Total Labour Force instead, shown that, even if conventional farms still have higher values than organic ones, the “distance” become shorter. That means that the two groups are quite similar and that, even if organic farms still produce a lower “economic value”, they better compensate productive factors, especially in terms of Labour Force. Regards to efficiency analysis, we found that organic olive-growing farms are more able in using their disposable resources (with reference to their own frontier), and the higher efficiency permits them to compensate the lower productivity with respect to the conventional farms

Organic and Conventional Farming: a Comparison Analysis through the Italian FADN

This paper shows some results arisen from a wider research on economic and environmental sustainability of organic farming. It focuses on organic and conventional farming comparison through an investigation of Italian FADN data. In order to identify some of the main differences between organic and conventional farms a “distance analysis” has been carried out. The study aims to highlight some of the main characteristics of those two groups of farms to better address differences (if any) in production technology, costs and revenues. Furthermore it shows the findings of a non-parametric efficiency analysis on the Italian olive-growing farms. The purpose is to estimate difference in efficiency and productivity between organic and conventional olive producers. Results reveal that looking at the average values on Invested Areas, conventional farms’ Gross Production is significantly higher than the organic ones, as the Net Margin, as the Net Product and Costs. The average values on Total Labour Force instead, shown that, even if conventional farms still have higher values than organic ones, the “distance” become shorter. That means that the two groups are quite similar and that, even if organic farms still produce a lower “economic value”, they better compensate productive factors, especially in terms of Labour Force. Regards to efficiency analysis, we found that organic olive-growing farms are more able in using their disposable resources (with reference to their own frontier), and the higher efficiency permits them to compensate the lower productivity with respect to the conventional farms.FADN Sampling Organic Farming Distance Efficiency Analysis

Robust DEA efficiency scores: A probabilistic/combinatorial approach

In this paper we propose robust efficiency scores for the scenario in which the specification of the inputs/outputs to be included in the DEA model is modelled with a probability distribution. This proba- bilistic approach allows us to obtain three different robust efficiency scores: the Conditional Expected Score, the Unconditional Expected Score and the Expected score under the assumption of Maximum Entropy principle. The calculation of the three efficiency scores involves the resolution of an exponential number of linear problems. The algorithm presented in this paper allows to solve over 200 millions of linear problems in an affordable time when considering up 20 inputs/outputs and 200 DMUs. The approach proposed is illustrated with an application to the assessment of professional tennis players

COOPER-framework: A Unified Standard Process for Non-parametric Projects

Practitioners assess performance of entities in increasingly large and complicated datasets. If non-parametric models, such as Data Envelopment Analysis, were ever considered as simple push-button technologies, this is impossible when many variables are available or when data have to be compiled from several sources. This paper introduces by the ‘COOPER-framework’ a comprehensive model for carrying out non-parametric projects. The framework consists of six interrelated phases: Concepts and objectives, On structuring data, Operational models, Performance comparison model, Evaluation, and Result and deployment. Each of the phases describes some necessary steps a researcher should examine for a well defined and repeatable analysis. The COOPER-framework provides for the novice analyst guidance, structure and advice for a sound non-parametric analysis. The more experienced analyst benefits from a check list such that important issues are not forgotten. In addition, by the use of a standardized framework non-parametric assessments will be more reliable, more repeatable, more manageable, faster and less costly.DEA, non-parametric efficiency, unified standard process, COOPER-framework.

Microfinance institutions and efficiency

Microfinance Institutions (MFIs) are special financial institutions. They have both a social nature and a for-profit nature. Their performance has been traditionally measured by means of financial ratios. The paper uses a Data Envelopment Analysis (DEA) approach to efficiency to show that ratio analysis does not capture DEA efficiency.Special care is taken in the specification of the DEA model. We take a methodological approach based on multivariate analysis. We rank DEA efficiencies under different models and specifications; e.g., particular sets of inputs and outputs. This serves to explore what is behind a DEA score. The results show that we can explain MFIs efficiency by means of four principal components of efficiency, and this way we are able to understand differences between DEA scores. It is shown that there are country effects on efficiency; and effects that depend on Non-governmental Organization (NGO)/non-NGO status of the MFI