Recovery strategies for a disrupted supply chain network: Leveraging blockchain technology in pre- and post-disruption scenarios

Supply chain networks have become larger, more complex and more challenging to manage, especially considering the multitude of risks and disruptions that may manifest. As such, a disruption can wreak havoc to a supply chain network, rendering the ability of a firm to respond to these disruptions with appropriate recovery strategies paramount. In this paper, we analyze such recovery strategies in a supply chain network. The specific model we develop aims at predicting a disruption that may occur in a context where smart contracts have been implemented based on blockchain technology. Within this setting, we suggest appropriate measures to be un-dertaken by an organization to mitigate the disruption and avoid negative performance outcomes as much as possible. If the disruption cannot be avoided, the proposed genetic algorithm-based approach focuses on adopting re-active measures to manage the post-disruption reality. As such, we effectively integrate both pre-and post-disruption scenarios to offer wholistic decision-support in an integrated fashion, extending prior work which mostly developed guidance only for either pre-or post-disruption responses. Specifically, we study the perfor-mance of a complex multi-echelon supply chain network, involving multiple suppliers, manufacturers, and distributors, under various conditions. The insights derived discern the effect of mitigation measures during a disruption, offering valuable guidance for decision makers.

Automated summary

This paper analyzes recovery strategies in supply chain networks. The study develops a specific model that predicts disruptions in a context where smart contracts based on blockchain technology are implemented. The proposed genetic algorithm-based approach offers reactive measures to manage the post-disruption reality. The insights derived provide valuable guidance for decision makers.