Returns to scale and most productive scale size in DEA with negative data

Sahoo, Biresh K.; Khoveyni, Mohammad; Eslami, Robabeh; Chaudhury, Pradipta

doi:10.1016/j.ejor.2016.05.065

Cited by 27 publications

(11 citation statements)

References 57 publications

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“…However, there can be a major crisis while screening CRS items from the VRS efficient list in presence of zero values. It is suggested that the principles of Sahoo et al [31] can alternatively be used as an alternative. Secondly, it is observed that the last step requires a careful attention as only CRS efficient DMUs are expected to be fed in this case.…”

Section: Limitation Of the Modelmentioning

confidence: 99%

“…Validation of the new origin using the model of Sahoo et al [31] The Multiplier model prescribed by Sahoo et al [31] when applied on both E & H (which were termed as CRS efficient) same output (mentioned in Tab. 11).…”

Section: Case With Mixed Type Of Multi-input and Output Problemmentioning

confidence: 99%

“…Authors induced the theme of a neighbourhood analysis to observe the changes in the output and input sets. Sahoo et al [31], however, pointed out the inability of the model to explore the CRS efficient DMUs & also the Most Productive Scale Size (MPSS). Authors proposed an alternative non-radial model to eradicate these problems.…”

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confidence: 99%

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Construction of Pseudo CRS frontier for a negative data using RTS model of Allahyar & A modified multiplier BCC model

Sarkar

2021

RAIRO-Oper. Res.

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Performance measurement of Decision Making Units (DMU) possessing an array of positive and negative type of data has been an extensively researched topic in Data Envelopment Analysis. However, assessment of Returns to Scale (RTS) under negative data problem is rarely witnessed without the steps referred by Allahyar, M. (2015). Authors purported a solution around the vicinity of the Decision Making Unit under examination to predict the nature of the Return to Scale of a firm. The extant investigation is aimed to extend the research of Allahyar, M. (2015) to identify a Pseudo Frontier for a negative data problem under Constant Return to Scale. In addition to it, a new origin based on the provided data is also computed with a view to convert the entire data set into a positive dataset. However, this approach seems to be ineffective to create a frontier under the multiple input output scenario. In this regard, a new variation of the Multiplier form of BCC model is proposed here to detect the new origin for the sake of designing the Pseudo CRS Frontier. Small examples are added for the elaboration of the CRS efficient DMUs using methods described by Allahyar, M. (2015) and identification of the New Origin from the Multiplier form of BCC model.

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Section: Limitation Of the Modelmentioning

confidence: 99%

Section: Case With Mixed Type Of Multi-input and Output Problemmentioning

confidence: 99%

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Construction of Pseudo CRS frontier for a negative data using RTS model of Allahyar & A modified multiplier BCC model

Sarkar

2021

RAIRO-Oper. Res.

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“…Zhou et al (2007) highlighted that the non-radial DEA seems to be more effective in measuring the environmental performance, since this approach has a high discriminating power in evaluating the DMU's efficiencies. Cook and Seiford (2009) presented a detailed review of the DEA techniques, while Sahoo et al (2016) and Liu et al (2016) proposed innovative DEA approaches.…”

Section: Deamentioning

confidence: 99%

A competitive analysis of EU ports by fixing spatial and economic dimensions

2020

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The purpose of this paper is to evaluate the efficiencies of ten of the leading European ports. The motivation of the research refers to the relevant topic of selection of indicators that can be involved in the comparative analysis. Concerning the theoretical model, the authors’ efforts are especially directed towards the usage of the stochastic frontier analysis (SFA) and of the data envelopment analysis (DEA). These techniques have been widely adopted for benchmarking and performance evaluation by involving indicators based on data from National Accounts. If one of these indicators, such as labour force consistency, is not available at a specific level of aggregation, detailed assumptions are needed to address this complication. The present study proposes an additive model in order to provide an estimation of ports’ economic activities by fixing the port activity boundaries and the spatial perimeter of the firms investigated. Several NUTS (Nomenclature of Territorial Units for Statistics) levels and NACE (EU Statistical Classification of Economic Activities) codes are fixed to offer a useful comparable labour indicator. Empirical results reveal that each port area presents a combination of the NACE categories which significantly impact the efficiency that can reach very high performance values through both the SFA and DEA techniques. Since the managers can choose which sectors to improve, which particular improvement strategies to support, which specific service to add, their decisions impact this performance evaluation, and their performance can be verified through the approaches proposed.

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“…Roughly speaking, we have constant/non-decreasing/non-increasing RTS iff optimal u ¼/ = / 5 0. These results were generalized in Banker and Thrall (1992) or Sahoo et al (2016) for efficient production points with variable u's, i.e., non-unique RTS situations. The authors in Førsund (1996), Førsund and Hjalmarsson (2004), Førsund et al (2007), and Fukuyama (2000) advanced the RTS context by models of neoclassical production theory and developed equations of scale elasticity for efficient and non-efficient DMUs; an overview and a discussion regarding the concept of RTS are provided in Tone and Sahoo (2003) or Jahanshahloo and SoleimaniDamaneh (2004).…”

Section: Introductionmentioning

confidence: 99%

Scaling production and improving efficiency in DEA: an interactive approach

Rödder

Kleine

2017

J Ind Eng Int

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DEA models help a DMU to detect its (in-)efficiency and to improve activities, if necessary. Efficiency is only one economic aim for a decision-maker; however, up-or downsizing might be a second one. Improving efficiency is the main topic in DEA; the long-term strategy towards the right production size should attract our attention as well. Not always the management of a DMU primarily focuses on technical efficiency but rather is interested in gaining scale effects. In this paper, a formula for returns to scale (RTS) is developed, and this formula is even applicable for interior points of technology. Particularly, technical and scale inefficient DMUs need sophisticated instruments to improve their situation. Considering RTS as well as efficiency, in this paper, we give an advice for each DMU to find an economically reliable path from its actual situation to better activities and finally to most productive scale size (mpss), perhaps. For realizing this path, we propose an interactive algorithm, thus harmonizing the scientific findings and the interests of the management. Small numerical examples illustrate such paths for selected DMUs; an empirical application in theatre management completes the contribution.

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Returns to scale and most productive scale size in DEA with negative data

Cited by 27 publications

References 57 publications

Construction of Pseudo CRS frontier for a negative data using RTS model of Allahyar & A modified multiplier BCC model

Construction of Pseudo CRS frontier for a negative data using RTS model of Allahyar & A modified multiplier BCC model

A competitive analysis of EU ports by fixing spatial and economic dimensions

Scaling production and improving efficiency in DEA: an interactive approach

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