Enhancing Supply Chain Resilience: A Deep Learning Approach to Late Delivery Risk Prediction
2024 (English)In: 2024 4th International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) / [ed] B. Benhala, A. Raihani & M. Qbadou, IEEE, 2024Conference paper, Published paper (Refereed)
Abstract [en]
This study introduces an innovative approach to predict late delivery risks, aiming to strengthen supply chain resilience through smart, data-driven strategies. The approach combines clustering using the Elbow method and multiclassification, incorporating advanced deep learning models like Neural Network(NN), Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM), and Bidirectional Long Short-Term Memory (Bi-LSTM). The findings highlight the effectiveness of the (CNN-LSTM) model in producing more accurate results, ultimately improving supply chain resilience, boosting customer satisfaction, and enabling proactive risk management.
Place, publisher, year, edition, pages
IEEE, 2024.
Keywords [en]
(Bi-LSTM), (CNN-LSTM), (NN), Clustering, Deep Learning, Elbow method, Late Delivery Risk, Supply Chain, Brain, Convolutional neural networks, Customer satisfaction, Learning systems, Risk management, Supply chains, (bidirectional long short-term memory), (convolutional neural network-long short-term memory), (neural network), Clusterings, Convolutional neural network, Neural-networks, Supply chain resiliences, Long short-term memory
National Category
Computer Sciences Production Engineering, Human Work Science and Ergonomics
Identifiers
URN: urn:nbn:se:hj:diva-65696DOI: 10.1109/IRASET60544.2024.10548074Scopus ID: 2-s2.0-85197134131ISBN: 979-8-3503-0950-8 (electronic)OAI: oai:DiVA.org:hj-65696DiVA, id: diva2:1884717
Conference
2024 4th International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET), Fez, Morocco, May 16-17, 2024
2024-07-182024-07-182024-07-18Bibliographically approved