Machine Learning for Proactive Supply Chain Risk Management: Predicting Delays and Enhancing Operational Efficiency

[1] V.G. Cannas, M.P. Ciano, M. Saltalamacchia, et R. Secchi, Artificial intelligence in supply chain and operations management: a multiple case study research, International Journal of Production Research, pp. 1–28, juill. 2023, doi: 10.1080/00207543.2023.2232050. Search in Google Scholar

[2] M. Schmidt, J.T. Maier, et L. Härtel, Data based root cause analysis for improving logistic key performance indicators of a company’s internal supply chain, Procedia CIRP, vol. 86, pp. 276–281, 2019. Search in Google Scholar

[3] H. Aboutorab, O.K. Hussain, M. Saberi, et F.K. Hussain, A reinforcement learning-based framework for disruption risk identification in supply chains, Future Generation Computer Systems, vol. 126, pp. 110–122, 2022. Search in Google Scholar

[4] J.-W. Bi, H. Li, et Z.-P. Fan, Tourism demand forecasting with time series imaging: A deep learning model, Annals of tourism Research, vol. 90, p. 103255, 2021. Search in Google Scholar

[5] Z. Sazvar, K. Tafakkori, N. Oladzad, et S. Nayeri, A capacity planning approach for sustainable-resilient supply chain network design under uncertainty: A case study of vaccine supply chain, Computers & Industrial Engineering, vol. 159, p. 107406, 2021. Search in Google Scholar

[6] S. Abbasi, M. Daneshmand-Mehr, et A. Ghane Kanafi, Green Closed-Loop Supply Chain Network Design During the Coronavirus (COVID-19) Pandemic: a Case Study in the Iranian Automotive Industry, Environ Model Assess, vol. 28, n^o 1, pp. 69–103, févr. 2023, doi: 10.1007/s10666-022-09863-0. Search in Google Scholar

[7] G. Van Voorn, G. Hengeveld, et J. Verhagen, An agent-based model representation to assess resilience and efficiency of food supply chains, Plos one, vol. 15, n^o 11, p. e0242323, 2020. Search in Google Scholar

[8] R. Rajesh, Flexible business strategies to enhance resilience in manufacturing supply chains: An empirical study, Journal of Manufacturing Systems, vol. 60, pp. 903–919, 2021. Search in Google Scholar

[9] Y. Li, Y. Yang, K. Zhu, et J. Zhang, Clothing Sale Forecasting by a Composite GRU – Prophet Model With an Attention Mechanism, IEEE Trans. Ind. Inf., vol. 17, n^o 12, pp. 8335–8344, déc. 2021, doi: 10.1109/TII.2021.3057922. Search in Google Scholar

[10] S. Jomthanachai, W.P. Wong, et K.W. Khaw, An Application of Machine Learning to Logistics Performance Prediction: An Economics Attribute-Based of Collective Instance, Comput Econ, vol. 63, n^o 2, pp. 741–792, févr. 2024, doi: 10.1007/s10614-023-10358-7. Search in Google Scholar

[11] Z.H. Kilimci et al., An improved demand forecasting model using deep learning approach and proposed decision integration strategy for supply chain, Complexity, vol. 2019, Consulté le: 17 février 2024. [En ligne]. Disponible sur: https://www.hindawi.com/journals/complexity/2019/9067367/abs/ Search in Google Scholar

[12] A.D. Ganesh et P. Kalpana, Future of artificial intelligence and its influence on supply chain risk management - A systematic review, Computers & Industrial Engineering, vol. 169, p. 108206, 2022. Search in Google Scholar

[13] L. Yu, S. Wang, et K.K. Lai, Developing an SVM-based ensemble learning system for customer risk identification collaborating with customer relationship management, Front. Comput. Sci. China, vol. 4, n^o 2, pp. 196–203, juin 2010, doi: 10.1007/s11704-010-0508-2. Search in Google Scholar

[14] G. Zheng et al., DRN: A Deep Reinforcement Learning Framework for News Recommendation, in Proceedings of the 2018 World Wide Web Conference on World Wide Web – WWW ’18, Lyon, France: ACM Press, 2018, pp. 167–176. doi: 10.1145/3178876.3185994. Search in Google Scholar

[15] E. Mangortey et al., Application of Machine Learning Techniques to Parameter Selection for Flight Risk Identification, in AIAA Scitech 2020 Forum, Orlando, FL: American Institute of Aeronautics and Astronautics, janv. 2020. doi: 10.2514/6.2020-1850. Search in Google Scholar

[16] N. Rezki et M. Mansouri, Improving supply chain risk assessment with artificial neural network predictions, AL, vol. 10, n^o 04, pp. 645–658, déc. 2023, doi: 10.22306/al.v10i4.444. Search in Google Scholar

[17] X. Zhu, A. Ninh, H. Zhao, et Z. Liu, Demand Forecasting with Supply-Chain Information and Machine Learning: Evidence in the Pharmaceutical Industry, Production and Operations Management, vol. 30, n^o 9, pp. 3231–3252, sept. 2021, doi: 10.1111/poms.13426. Search in Google Scholar

[18] J.C. Alves et G.R. Mateus, Multi-echelon Supply Chains with Uncertain Seasonal Demands and Lead Times Using Deep Reinforcement Learning. arXiv, 12 janvier 2022. Consulté le: 28 février 2024. [En ligne]. Disponible sur: http://arxiv.org/abs/2201.04651 Search in Google Scholar

[19] J. Bender et J. Ovtcharova, Prototyping machine – learning-supported lead time prediction using AutoML, Procedia Computer Science, vol. 180, pp. 649–655, 2021. Search in Google Scholar

[20] A.T. Dosdoğru, A. Boru İpek, et M. Göçken, A novel hybrid artificial intelligence-based decision support framework to predict lead time, International Journal of Logistics Research and Applications, vol. 24, n^o 3, pp. 261–279, mai 2021, doi: 10.1080/13675567.2020.1749249. Search in Google Scholar

[21] M.C. Camur, S.K. Ravi, et S. Saleh, Enhancing Supply Chain Resilience: A Machine Learning Approach for Predicting Product Availability Dates Under Disruption. arXiv, 28 avril 2023. Consulté le: 26 août 2023. [En ligne]. Disponible sur: http://arxiv.org/abs/2304.14902 Search in Google Scholar

[22] P. Sarbas et al., Development of Predictive Models for Order Delivery Risk in a Supply Chain: A Machine Learning Approach, in Emerging Trends in Mechanical and Industrial Engineering, X. Li, M.M. Rashidi, R.S. Lather, et R. Raman, Éd., in Lecture Notes in Mechanical Engineering, Singapore: Springer Nature Singapore, 2023, pp. 571–581. doi: 10.1007/978-981-19-6945-4_43. Search in Google Scholar

[23] R. Lolla et al., Machine Learning Techniques for Predicting Risks of Late Delivery, in Data Science and Emerging Technologies, vol. 165, Y.B. Wah, M.W. Berry, A. Mohamed, et D. Al-Jumeily, Éd., in Lecture Notes on Data Engineering and Communications Technologies, vol. 165, Singapore: Springer Nature Singapore, 2023, pp. 343–356. doi: 10.1007/978-981-99-0741-0_25. Search in Google Scholar

[24] F. Steinberg, P. Burggräf, J. Wagner, B. Heinbach, T. Saßmannshausen, et A. Brintrup, A novel machine learning model for predicting late supplier deliveries of low-volume-high-variety products with application in a German machinery industry, Supply Chain Analytics, vol. 1, p. 100003, 2023. Search in Google Scholar

[25] M. Söderholm, Predicting Risk of Delays in Postal Deliveries with Neural Networks and Gradient Boosting Machines. 2020. Consulté le: 7 avril 2024. [En ligne]. Disponible sur: https://www.diva-portal.org/smash/record.jsf?pid=diva2:1467609 Search in Google Scholar

[26] A. Thomas et V.V. Panicker, Supply Chain Data Analytics for Predicting Delivery Risks Using Machine Learning, in Applications of Emerging Technologies and AI/ML Algorithms, M. K. Tiwari, M.R. Kumar, R.T.M., et R. Mitra, Éd., in Asset Analytics, Singapore: Springer Nature Singapore, 2023, pp. 159–168. doi: 10.1007/978-981-99-1019-9_16. Search in Google Scholar

[27] R. Al-Saghir, Predicting Delays in the Supply Chain with the Use of Machine Learning, 2022, Consulté le: 7 avril 2024. [En ligne]. Disponible sur: https://repository.rit.edu/theses/11492/ Search in Google Scholar

[28] K. Douaioui, R. Oucheikh, et C. Mabrouki, Enhancing Supply Chain Resilience: RIME-Clustering and Ensemble Deep Learning Strategies for Late Delivery Risk Prediction, Log-Forum, vol. 20, n^o 1, pp. 55–70, 2024. Search in Google Scholar

[29] A. Thomas et V.V. Panicker, Application of Machine Learning Algorithms for Order Delivery Delay Prediction in Supply Chain Disruption Management, in Intelligent Manufacturing Systems in Industry 4.0, B.B.V.L. Deepak, M.V.A.R. Bahubalendruni, D.R.K. Parhi, et B.B. Biswal, Éd., in Lecture Notes in Mechanical Engineering, Singapore: Springer Nature Singapore, 2023, pp. 491 – 500. doi: 10.1007/978-981-99-1665-8_42. Search in Google Scholar

[30] H. Abouloifa et M. Bahaj, Predicting late delivery in Supply chain 4.0 using feature selection: a machine learning model, in 2022 5^th International Conference on Advanced Communication Technologies and Networking 3, IEEE, 2022, pp. 1–5. Consulté le: 7 avril 2024. [En ligne]. Disponible sur: https://ieeexplore.ieee.org/abstract/document/9993969/ Search in Google Scholar