Adaptive identification of supply chain disruptions through reinforcement learning

Hamed Aboutorab, Omar K. Hussain, Morteza Saberi, Farookh Khadeer Hussain, Daniel Prior

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Proactive identification and the management of disruption risks play a crucial role in the achievement of a global supply chain's aims. Given the velocity and volume by which such disruption events occur, it is impractical to expect supply chain managers to determine the occurrence of such events manually. Given the pressures facing global supply chains due to the COVID-19 crisis, it is important for supply chain managers to proactively identify disruption risks to their supply chains and manage them to either achieve the outcomes or develop plans by which resilience against them can be built. In this paper, we demonstrate how the integration of natural language processing and reinforcement learning, which are fundamental artificial intelligence methods, can be used to assist supply chain risk managers in the timely identification of such disruption events. We explain in detail our proposed approach, namely RL-SCRI and show its superiority over the current models in achieving its aim.

Original languageEnglish
Article number123477
JournalExpert Systems with Applications
Publication statusPublished - 15 Aug 2024


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