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Advanced deep learning approaches to predict supply chain risks under COVID-19 restrictions
Mahmoud M Bassiouni1, Ripon K Chakrabortty2, Omar K Hussain3
1Faculty of Computer and Information Science, Egyptian E-Learning University, Egypt.
Expert Systems with Applications
|August 24, 2022
Summary
Deep learning models, including the Temporal Convolutional Network (TCN), can accurately predict global shipment risks during the COVID-19 pandemic. This aids in proactively managing supply chain vulnerabilities and enhancing resilience.
Area of Science:
- Supply Chain Management
- Artificial Intelligence
- Epidemiology
Background:
- The COVID-19 pandemic severely disrupted global supply chains, impacting essential product shipments and highlighting SC vulnerabilities.
- Predicting shipment risks is crucial for mitigating disruptions and enhancing the resilience of global SCs.
Purpose of the Study:
- To propose and evaluate Deep Learning (DL) approaches for predicting international shipment viability amidst COVID-19 restrictions.
- To enhance decision-making for proactive supply chain risk management.
Main Methods:
- Data capture, pre-processing, feature extraction, and classification were employed.
- Feature extraction utilized Recurrent Neural Networks (RNNs) variants (LSTM, BiLSTM, GRU) and Temporal Convolutional Networks (TCN).
- Six classifiers (SoftMax, RT, RF, KNN, ANN, SVM) were tested for shipment risk prediction.
Main Results:
- The Temporal Convolutional Network (TCN) model achieved approximately 100% accuracy in predicting shipment risks under COVID-19 restrictions.
- The study demonstrated the effectiveness of DL models in assessing shipment export feasibility.
Conclusions:
- DL models, particularly TCN, offer a highly accurate solution for predicting supply chain risks during pandemics.
- This research provides a valuable tool for decision-makers to proactively strengthen supply chain resilience.
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