2023/09/25 by Mahmoud Ashraf, Ashraf, Mahmoud, Amr Eltawil +3 · 1 citation
Business, Management and Accounting · Engineering · #Big Data and Business Intelligence #Digital Transformation in Industry #Emerging Technologies (cs.ET) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Supply Chain Resilience and Risk Management
paper · pdf · doi:10.48550/arxiv.2309.14557
openalex publication_date 2023/09/25 · openalex created_date 2023/09/28 · openalex updated_date 2026/07/28
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.