Concurrent estimation of efficiency, effectiveness and returns to scale

Abstract

In recent years, data envelopment analysis (DEA) has been widely used to assess both efficiency and effectiveness. Accurate measurement of overall performance is a product of concurrent consideration of these measures. There are a couple of well-known methods to assess both efficiency and effectiveness. However, some issues can be found in previous methods. The issues include non-linearity problem, paradoxical improvement solutions, efficiency and effectiveness evaluation in two independent environments: dividing an operating unit into two autonomous departments for performance evaluation and problems associated with determining economies of scale. To overcome these issues, this paper aims to develop a series of linear DEA methods to estimate efficiency, effectiveness, and returns to scale of decision-making units (DMUs), simultaneously. This paper considers the departments of a DMU as a united entity to recommend consistent improvements. We first present a model under constant returns to scale (CRS) assumption, and examine its relationship with one of existing network DEA model. We then extend model under variable returns to scale (VRS) condition, and again its relationship with one of existing network DEA models is discussed. Next, we introduce a new integrated two-stage additive model. Finally, an in-depth analysis of returns to scale is provided. A case study demonstrates applicability of the proposed models.

Firm capability assessment via the BSC and DEA

“…A data envelopment analysis (DEAs) is a data-oriented technique proposed by Charnes et al (1978). It was developed for measuring the relative efficiencies within a group of decisionmaking units (DMUs) that utilize some inputs to generate a set of outputs (Dobos and V€ or€ osmarty, 2019;Hilmola et al, 2015;Khodakarami et al, 2016;Lin and Chen, 2017;Yu and Hsiao, 2018;Seydel, 2006). A data envelopment analysis can identify the strengths and weaknesses of each industrial unit based on industry criteria.…”

Investigating the Sustainability of Return to Scale Classification in a Two‐Stage Network Based on DEA Models

“…Tis method is able to evaluate returns to scale to the right and left of the given unit in all conditions. In the same vein, Khodakarami et al [37] in their article "concurrent estimation of efciency, efectiveness, and returns to scale" studied the efciency, efectiveness, and return to scale of DMUs simultaneously. Machado et al [38] research the hypothesis that economies of scale are a typical feature of the generation market in Brazil used by the cost structure of the electricity generation companies by using a translog cost function in Brazil during the period 2000-2010.…”

Simultaneous evaluation of efficiency, input effectiveness, and output effectiveness

“…Well-known methods for measuring productivity by assessing efficiency and effectiveness are available in literature: Network DEA (Cook et al , 2010), integrated two-stage DEA (Chen et al , 2009), ICCR (integrated Charnes et al , 1978) and IBCC (integrated Banker et al , 1984) models (Chiou et al , 2010), and classic DEA models (Chiou and Chen, 2006; Karlaftis, 2004). According to Khodakarami et al (2016), critical points can be found in those studies. Arguments include problem of non-linearity, suggesting paradoxical improvement strategies, evaluating efficiency and effectiveness in two independent environments, dividing a decision-making unit (DMU) into two autonomous departments for performance evaluation, and problems associated with determining economies of scale.…”

“…Arguments include problem of non-linearity, suggesting paradoxical improvement strategies, evaluating efficiency and effectiveness in two independent environments, dividing a decision-making unit (DMU) into two autonomous departments for performance evaluation, and problems associated with determining economies of scale. With respect to the mentioned challenges, Khodakarami et al (2016) defined a number of properties as important features of DEA-based productivity assessment and developed a series of linear DEA methods to estimate efficiency, effectiveness, and returns to scale of DMUs, simultaneously.…”

“…The main objective of this paper is to propose a novel model for productivity estimation by evaluating efficiency, input effectiveness, and output effectiveness, simultaneously. Idea of this paper is different from Khodakarami et al (2016) as this paper uses concept of target setting and extent to which they are attained. Note that the effectiveness is somehow related to predefined targets.…”