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Scientific Article details

Title A review of decision-support tools and performance measurement and sustainable supply chain management
ID_Doc 74683
Authors Taticchi, P; Garengo, P; Nudurupati, SS; Tonelli, F; Pasqualino, R
Title A review of decision-support tools and performance measurement and sustainable supply chain management
Year 2015
Published International Journal Of Production Research, 53, 21
DOI 10.1080/00207543.2014.939239
Abstract In recent years, interest on sustainable supply chain management (SSCM) has risen significantly in both the academic and business communities. This is confirmed by the growing number of conferences, journal publications, special issues and websites dedicated to the topic. Within this context, this paper reviews the existing literature related to decision-support tools and performance measurement for SSCM. A narrative literature review is carried out to capture qualitative evidence, while a systematic literature review is performed using classic bibliometric techniques to analyse the relevant body of knowledge identified in 384 papers published from 2000 to 2013. The key conclusions include: the evidence of a research field that is growing, the call for establishing the scope of current research, i.e. the need for integrated performance frameworks with new generation decision-support tools incorporating triple bottom line (TBL) approach for managing sustainable supply chains. There is a need to identify a wide range of specific industry-related TBL metrics and indexes, and assess their usefulness through empirical research and case-base analysis. We need mixed methods to thoroughly analyse and investigate sustainable aspects of the product life cycle across the supply chains, through empirical evidence, building and/or testing theory from and in practice.
Author Keywords decision-support tool; performance management; supply chain; sustainability
Index Keywords Index Keywords
Document Type Other
Open Access Open Access
Source Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
EID WOS:000361381400009
WoS Category Engineering, Industrial; Engineering, Manufacturing; Operations Research & Management Science
Research Area Engineering; Operations Research & Management Science
PDF https://e-space.mmu.ac.uk/570774/2/Figures%20and%20Tables%20FINAL.pdf
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