The content discusses the potential of leveraging intelligent recommender systems (IRS) as a reactive measure to enhance supply chain resilience. It highlights the following key points:
Supply chains are becoming more complex and vulnerable to disruptions, requiring effective resilience strategies. Current research has mainly focused on developing proactive resilience strategies, while reactive measures are relatively neglected.
The rapid response phase after a disruption is crucial, as ineffective or delayed recovery actions can lead to prolonged shortages. Shortening the time between the initial response and recovery stage is identified as a feasible resilience strategy.
Recommender systems (RS) have the potential to become an effective supply chain disruption risk mitigation tool due to their agility in identifying and leveraging available resources within the supply network.
The proposed conceptual framework utilizes IRS techniques to rapidly identify and recommend available internal and external resources as the first-step response to supply chain disruptions, aiming to bridge the gap between the initial reaction and recovery stages.
The framework is designed to work through the whole response phase, starting with identifying and recommending internal redundancy, followed by external redundancy from the supply network before the recovery stage.
The implementation of this IRS-based framework is discussed, highlighting its capability to promote results rapidly while considering practical constraints such as lead time, production capacity, costs, and inspection results.
The effective collaboration and information sharing between supply chain participants are identified as a fundamental assumption and potential barrier in practical implementation.
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