Benchmarking with DEA, SFA, and R

Bogetoft, Peter; Otto, Lars

doi:10.1007/978-1-4419-7961-2

Cited by 469 publications

(407 citation statements)

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“…The points these lines comprise of can represent both observed and 'virtual' DMUs. See Bogetoft and Otto (2011) for a theoretical background on convexity in DEA.…”

Section: Datamentioning

confidence: 99%

“…First, the theoretical distribution of efficiency scores in DEA is generally unknown so a convention in the DEA literature is to use non-parametric tests (Brockett and Golany, 1996;Cooper et al, 2007;Bogetoft and Otto, 2011). Second, because RAM can be used to rank DMUs, it lends itself to the rankings that underlie non-parametric rank statistics (Brockett et al, 2004).…”

Section: Datamentioning

confidence: 99%

“…the eco-efficiency ratios) has been debated in the literature (Kuosmanen and Kortelainen, 2005). Sixth, allowance should be made for the fact that there exist factors affecting eco-efficiency that are beyond managerial control, such as the different bio-physical conditions under which farms operate (see Bogetoft and Otto, 2011;Jan et al, 2012).…”

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confidence: 99%

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An approach to holistically assess (dairy) farm eco-efficiency by combining Life Cycle Analysis with Data Envelopment Analysis models and methodologies

Soteriades

Faverdin

Moreau

et al. 2016

Animal

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Eco-efficiency is a useful guide to dairy farm sustainability analysis aimed at increasing output (physical or value added) and minimizing environmental impacts (EIs). Widely used partial eco-efficiency ratios (EIs per some functional unit, e.g. kg milk) can be problematic because (i) substitution possibilities between EIs are ignored, (ii) multiple ratios can complicate decision making and (iii) EIs are not usually associated with just the functional unit in the ratio's denominator. The objective of this study was to demonstrate a 'global' eco-efficiency modelling framework dealing with issues (i) to (iii) by combining Life Cycle Analysis (LCA) data and the multiple-input, multiple-output production efficiency method Data Envelopment Analysis (DEA). With DEA each dairy farm's outputs and LCA-derived EIs are aggregated into a single, relative, bounded, dimensionless eco-efficiency score, thus overcoming issues (i) to (iii). A novelty of this study is that a model providing a number of additional desirable properties was employed, known as the Range Adjusted Measure (RAM) of inefficiency. These properties altogether make RAM advantageous over other DEA models and are as follows. First, RAM is able to simultaneously minimize EIs and maximize outputs. Second, it indicates which EIs and/or outputs contribute the most to a farm's eco-inefficiency. Third it can be used to rank farms in terms of eco-efficiency scores. Thus, non-parametric rank tests can be employed to test for significant differences in terms of eco-efficiency score ranks between different farm groups. An additional DEA methodology was employed to 'correct' the farms' eco-efficiency scores for inefficiencies attributed to managerial factors. By removing managerial inefficiencies it was possible to detect differences in eco-efficiency between farms solely attributed to uncontrollable factors such as region. Such analysis is lacking in previous dairy studies combining LCA with DEA. RAM and the 'corrective' methodology were demonstrated with LCA data from French specialized dairy farms grouped by region (West France, Continental France) and feeding strategy (regardless of region). Mean eco-efficiency score ranks were significantly higher for farms with <10% and 10% to 30% maize than farms with >30% maize in the total forage area before correcting for managerial inefficiencies. Mean eco-efficiency score ranks were higher for West than Continental farms, but significantly higher only after correcting for managerial inefficiencies. These results helped identify the eco-efficiency potential of each region and feeding strategy and could therefore aid advisors and policy makers at farm or region/sector level. The proposed framework helped better measure and understand (dairy) farm eco-efficiency, both within and between different farm groups.

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“…The points these lines comprise of can represent both observed and 'virtual' DMUs. See Bogetoft and Otto (2011) for a theoretical background on convexity in DEA.…”

Section: Datamentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

See 1 more Smart Citation

An approach to holistically assess (dairy) farm eco-efficiency by combining Life Cycle Analysis with Data Envelopment Analysis models and methodologies

Soteriades

Faverdin

Moreau

et al. 2016

Animal

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“…The frontier (figure 1) is considered as a sign of relative efficiency, which has been achieved and defined by at least one efficient observation (entity) usually called as decision-making unit (DMU), and any DMU that is below the frontier, DMU " E" in our case, is considered as inefficient. Depending on the assumptions followed about fixed and variable inputs, the production frontier determines the reference relative to which the capacity output or technically efficient outputs of the different DMUs are judged (Bogetoft & Otto, 2010). (Note 2) The range of DEA models which have been developed to measure TE and C in different industries fall into the categories of being either input-oriented or output-oriented.…”

Section: Data Envelopment Analysismentioning

confidence: 99%

Efficiency and Capacity Utilization of India’s Marine Fisheries

Teame

2017

MOS

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For decades, the problems of excess capacity and overfishing have been the subject of considerable attentions, since they are the primary reasons for the depletion of fish stocks, reduction of the profitability and economic performance of the fishery sectors at the national and international levels. As a result, estimations of technical efficiency, harvesting capacity, and capacity utilization has become an increasingly important practice in the fishery, since they provide useful information about the optimum allocation of inputs and outputs, and guide policy formulation to combat biological and economic losses. Based on the Johansen (1968) definition of capacity we have examined the technical efficiency, capacity and capacity utilization of the marine fishery sectors of the India's 9 marine states and 4 union territories using an output oriented data envelopment analysis approach. The result of the study shows that majority of the states/union territories have been inefficient and have the capacity to harvest considerably more than what they have actually been harvesting by using the existing resources in an efficient configuration and showed how serious the problem of excess capacity is in the India's marine fishery.

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“…The derivation of the log likelihood function is available in Bogetoft and Otto [23]. With σ 2 replaced with 1 ∑ ( − ( ; β)) 2 =1…”

Section: Stochastic Frontier Analysis (Sfa)mentioning

confidence: 99%

Estimating the Technical Improvement of Energy Efficiency in the Automotive Industry—Stochastic and Deterministic Frontier Benchmarking Approaches

Park

Hildreth

2014

Energies

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Abstract:The car manufacturing industry, one of the largest energy consuming industries, has been making a considerable effort to improve its energy intensity by implementing energy efficiency programs, in many cases supported by government research or financial programs. While many car manufacturers claim that they have made substantial progress in energy efficiency improvement over the past years through their energy efficiency programs, the objective measurement of energy efficiency improvement has not been studied due to the lack of suitable quantitative methods. This paper proposes stochastic and deterministic frontier benchmarking models such as the stochastic frontier analysis (SFA) model and the data envelopment analysis (DEA) model to measure the effectiveness of energy saving initiatives in terms of the technical improvement of energy efficiency for the automotive industry, particularly vehicle assembly plants. Illustrative examples of the application of the proposed models are presented and demonstrate the overall benchmarking process to determine best practice frontier lines and to measure technical improvement based on the magnitude of frontier line shifts over time. Log likelihood ratio and Spearman rank-order correlation coefficient tests are conducted to determine the significance of the SFA model and its consistency with the DEA model. ENERGY STAR ® EPI (Energy Performance Index) are also calculated. OPEN ACCESSEnergies 2014, 7 6197

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Benchmarking with DEA, SFA, and R

Cited by 469 publications

References 0 publications

An approach to holistically assess (dairy) farm eco-efficiency by combining Life Cycle Analysis with Data Envelopment Analysis models and methodologies

An approach to holistically assess (dairy) farm eco-efficiency by combining Life Cycle Analysis with Data Envelopment Analysis models and methodologies

Efficiency and Capacity Utilization of India’s Marine Fisheries

Estimating the Technical Improvement of Energy Efficiency in the Automotive Industry—Stochastic and Deterministic Frontier Benchmarking Approaches

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