A double-DEA framework to support decision-making in the choice of advanced manufacturing technologies

Abstract

Purpose The purpose of this paper is to propose a methodological framework that combines several data envelopment analysis (DEA) models to deal with the problem of evaluating and ranking advanced manufacturing technologies (AMTs) without introducing any subjectivity in the analysis. Design/methodology/approach The methodology follows a two-phase procedure. First, the relative efficiency of every technology is calculated by implementing different DEA cross-efficiency models generating the same number of high-order indicators as efficiency vectors. Second, high-order indicators are used as outputs in a SBM-DEA super-efficiency model to obtain a comprehensive DEA-like composite indicator. Findings The framework is implemented to evaluate a sample of flexible manufacturing systems. Comparing it to other methods, results show that the methodology provides reliable information for AMTs selection and effective support to management decision-making. Originality/value This paper contributes to the body of knowledge about the utilization of DEA to select AMTs. The framework has several advantages: a discriminating power higher than the basic DEA models; no subjective judgment relative to weights necessary to aggregate single indicators and choice of aggregation function; no need to perform any transformation normalizing original data; independence from the unit of measurement of the DEA-like composite indicator; and great flexibility and adaptability allowing the introduction of further variables in the analysis.

DEA cross‐efficiency ranking method considering satisfaction and consensus degree

“…Based on Shannon entropy, Karagiannis and Karagiannis (2020) presented a weighting scheme for constructing composite indicators. Besides, some practical applications are studied by scholars combining DEA and Shannon's entropy (Bian and Yang, 2010;Lo Storto, 2016;Lo Storto, 2018;Ang et al, 2021;Behdani and Darehmiraki, 2019). In addition, the similarity of the sequence curves and the order relation will be concluded through grey incidence analysis.…”

An SNA-DEA Prioritization Framework to Identify Critical Nodes of Gas Networks: The Case of the US Interstate Gas Infrastructure

“…A more useful DEA-based approach to rank DMUs is offered by the calculation of the DMU cross-efficiency [59]. The cross-efficiency DEA method has been extensively utilized to generate rankings among units because it has a stronger discriminating power than conventional DEA [59][60][61]. In the proposed framework, two cross-efficiency DEA models are implemented to generate separate rankings of the same interstate natural gas network nodes employing different sets of SNA metrics either as inputs or outputs.…”

Efficiency, Conflicting Goals and Trade-Offs: A Nonparametric Analysis of the Water and Wastewater Service Industry in Italy

“…Since its introduction, DEA has been extensively employed in economic analysis to calculate measures of efficiencies in various situations and industries, such as transportation [29,31,32], government and public sector [33][34][35], public utilities [21,22,[36][37][38], healthcare [39][40][41], education [42,43], banking and finance [44,45], energy and environmental studies [46][47][48][49][50][51][52], manufacturing and information technology [53][54][55][56].…”