A double-DEA framework to support decision-making in the choice of advanced manufacturing technologies. Issue 2 (2nd February 2018)

• Record Type: Journal Article
• Authors: lo Storto, Corrado
• Is Part Of: Management decision. Volume 56:Issue 2(2018)
• Page Start: 488
• Page End: 507
• Publication Date: 2018-02-02
• Subjects: Selection -- Advanced manufacturing technologies -- Efficiency -- Quantitative techniques -- Data envelopment analysis -- Benefit of the doubt; Management -- Periodicals; 658.403
• DOI: 10.1108/MD-09-2016-0644
• Languages: English
• ISSNs: 0025-1747

Abstract

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.