PMID- 35999828 OWN - NLM STAT- PubMed-not-MEDLINE LR - 20221221 IS - 0957-4174 (Print) IS - 0957-4174 (Electronic) IS - 0957-4174 (Linking) VI - 211 DP - 2023 Jan TI - Advanced deep learning approaches to predict supply chain risks under COVID-19 restrictions. PG - 118604 LID - 10.1016/j.eswa.2022.118604 [doi] AB - The ongoing COVID-19 pandemic has created an unprecedented predicament for global supply chains (SCs). Shipments of essential and life-saving products, ranging from pharmaceuticals, agriculture, and healthcare, to manufacturing, have been significantly impacted or delayed, making the global SCs vulnerable. A better understanding of the shipment risks can substantially reduce that nervousness. Thenceforth, this paper proposes a few Deep Learning (DL) approaches to mitigate shipment risks by predicting "if a shipment can be exported from one source to another", despite the restrictions imposed by the COVID-19 pandemic. The proposed DL methodologies have four main stages: data capturing, de-noising or pre-processing, feature extraction, and classification. The feature extraction stage depends on two main variants of DL models. The first variant involves three recurrent neural networks (RNN) structures (i.e., long short-term memory (LSTM), Bidirectional long short-term memory (BiLSTM), and gated recurrent unit (GRU)), and the second variant is the temporal convolutional network (TCN). In terms of the classification stage, six different classifiers are applied to test the entire methodology. These classifiers are SoftMax, random trees (RT), random forest (RF), k-nearest neighbor (KNN), artificial neural network (ANN), and support vector machine (SVM). The performance of the proposed DL models is evaluated based on an online dataset (taken as a case study). The numerical results show that one of the proposed models (i.e., TCN) is about 100% accurate in predicting the risk of shipment to a particular destination under COVID-19 restrictions. Unarguably, the aftermath of this work will help the decision-makers to predict supply chain risks proactively to increase the resiliency of the SCs. CI - (c) 2022 Elsevier Ltd. All rights reserved. FAU - Bassiouni, Mahmoud M AU - Bassiouni MM AD - Faculty of Computer and Information Science, Egyptian E-Learning University, Egypt. FAU - Chakrabortty, Ripon K AU - Chakrabortty RK AD - School of Eng. & IT, UNSW Canberra at ADFA, Australia. FAU - Hussain, Omar K AU - Hussain OK AD - School of Business, UNSW Canberra at ADFA, Australia. FAU - Rahman, Humyun Fuad AU - Rahman HF AD - Capability Systems Centre, School of Eng. & IT, UNSW Canberra at ADFA, Australia. LA - eng PT - Journal Article DEP - 20220819 PL - United States TA - Expert Syst Appl JT - Expert systems with applications JID - 9884333 PMC - PMC9389854 OTO - NOTNLM OT - COVID-19 OT - Classifiers OT - Convolutional network OT - Deep learning OT - Supply chain risk OT - Temporal convolutional network COIS- The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. EDAT- 2022/08/25 06:00 MHDA- 2022/08/25 06:01 PMCR- 2022/08/19 CRDT- 2022/08/24 01:43 PHST- 2022/05/07 00:00 [received] PHST- 2022/08/04 00:00 [revised] PHST- 2022/08/14 00:00 [accepted] PHST- 2022/08/25 06:00 [pubmed] PHST- 2022/08/25 06:01 [medline] PHST- 2022/08/24 01:43 [entrez] PHST- 2022/08/19 00:00 [pmc-release] AID - S0957-4174(22)01656-6 [pii] AID - 118604 [pii] AID - 10.1016/j.eswa.2022.118604 [doi] PST - ppublish SO - Expert Syst Appl. 2023 Jan;211:118604. doi: 10.1016/j.eswa.2022.118604. Epub 2022 Aug 19.