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Sustainable supply network management

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  • Leonardo Marques

Abstract

Purpose - The purpose of this paper is to scrutinise how the sustainable supply chain management (SSCM) literature has discussed knowledge dynamics across the extended supply network, particularly in the contemporary context of fragmented, globally dispersed supply networks. Design/methodology/approach - A systematic approach to reviewing the literature is applied, covering 20 years, starting with 267 references, and narrowing down to 88 articles specifically addressing knowledge diffusion processes across the extended supply network. Findings - This study shows that vertical ties limited to direct suppliers or third-party monitoring of global suppliers are both insufficient. Lack of co-opetition is an impediment to knowledge diffusion. And the debate of whether or not global dispersion is an impediment to knowledge diffusion seems inconclusive. More importantly, there is a lack of network-level studies mapping the diversity of actors in supply networks. Research limitations/implications - First, future SSCM research should shift from an operational focus to strategic knowledge diffusion. Second, the scope of SSCM should expand from linear buyer–supplier relationships to multi-tier and multilateral studies. Special focus should be placed on the literature on social network to support processes that look at the drivers of effective large-scale, global diffusion of sustainability. Originality/value - This review contends that it is paramount to set a new research direction captured in a new definition of “sustainable supply network management”. Future research should overcome the barriers of data collection at the network level in order to contribute to the field’s current challenges, which clearly lies in globally dispersed and complex supply network, not dyads or linear chains.

Suggested Citation

  • Leonardo Marques, 2019. "Sustainable supply network management," International Journal of Productivity and Performance Management, Emerald Group Publishing Limited, vol. 68(6), pages 1164-1190, May.
  • Handle: RePEc:eme:ijppmp:ijppm-12-2017-0329
    DOI: 10.1108/IJPPM-12-2017-0329
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    Cited by:

    1. Leonardo de Assis Santos & Leonardo Marques, 2022. "Big data analytics for supply chain risk management: research opportunities at process crossroads," Post-Print hal-03766121, HAL.
    2. Manu Sharma & Sudhanshu Joshi & Sunil Luthra & Anil Kumar, 2022. "Managing disruptions and risks amidst COVID-19 outbreaks: role of blockchain technology in developing resilient food supply chains," Operations Management Research, Springer, vol. 15(1), pages 268-281, June.

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