Disruption Detection for a Cognitive Digital Supply Chain Twin Using
  Hybrid Deep Learning
Mahmoud Ashraf Amr Eltawil Islam Ali
Purpose: Recent disruptive events, such as COVID-19 and Russia-Ukraine
conflict, had a significant impact of global supply chains. Digital supply
chain twins have been proposed in order to provide decision makers with an
effective and efficient tool to mitigate disruption impact. Methods: This paper
introduces a hybrid deep learning approach for disruption detection within a
cognitive digital supply chain twin framework to enhance supply chain
resilience. The proposed disruption detection module utilises a deep
autoencoder neural network combined with a one-class support vector machine
algorithm. In addition, long-short term memory neural network models are
developed to identify the disrupted echelon and predict time-to-recovery from
the disruption effect. Results: The obtained information from the proposed
approach will help decision-makers and supply chain practitioners make
appropriate decisions aiming at minimizing negative impact of disruptive events
based on real-time disruption detection data. The results demonstrate the
trade-off between disruption detection model sensitivity, encountered delay in
disruption detection, and false alarms. This approach has seldom been used in
recent literature addressing this issue.