A Reputational-Risk-Based Match Selection Framework for Collaborative Networks in the Logistics Sector

Vítor Anes, António Abreu, Ana Dias, João Calado

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
43 Downloads (Pure)

Abstract

Collaborative networks in the logistics sector have proven to be a solution that both meets environmental footprint reduction goals and addresses the impact of rising fuel prices on logistics companies, especially for small-and medium-sized enterprises. Despite these benefits, these collaborative networks have not received the desired amount of participation due to reputational risk. This paper develops a framework for assessing and managing reputational risk to encourage logistics companies’ participation in collaborative networks. To this end, customer satisfaction factors were correlated with logistics operations, and this correlation was then modeled using the Bowtie method, fault trees, event trees, reliability theory, and the Monte Carlo model. The results show that it is possible to implement a structured model that can be easily put into practice. Using an illustrative case study, it is also possible to prioritize three companies according to their reputational risk as assessed by the proposed model. The developed model can promote the sustainability of collaborative networks in the logistics industry by assessing and consistently reducing reputational risk, thus supporting the strengthening of the relationship between suppliers, logistics service providers, and end customers.

Original languageEnglish
Article number4329
Number of pages24
JournalSustainability (Switzerland)
Volume14
Issue number7
DOIs
Publication statusPublished - 6 Apr 2022

Keywords

  • collaborative networks
  • logistics
  • Monte Carlo method
  • risk assessment and management
  • sustainability
  • transportation sector

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