Abstract
This paper introduces a Long-Short Term Memory (LSTM) neural network-based method for Time-To-Recovery (TTR) prediction within a cognitive digital supply chain twin framework to enhance supply chain resilience and improve decision-making under disruption. The introduced method is applied to a virtual three-echelon supply chain network modelled using discrete event simulation. The virtual system was used to train an LSTM neural network model to predict TTR under several disruption scenarios. Results show that predicted TTR values tend to be relatively lower than the actual values at early disruption stages, then improve throughout the progression of the disruption effect on the supply chain network.
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