Supply chain data analytics for predicting supplier disruptions: a case study in complex asset manufacturing
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DOI: 10.1080/00207543.2019.1685705
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Cited by:
- Dmitry Ivanov & Alexandre Dolgui, 2022. "Stress testing supply chains and creating viable ecosystems," Operations Management Research, Springer, vol. 15(1), pages 475-486, June.
- Farheen Naz & Anil Kumar & Abhijit Majumdar & Rohit Agrawal, 2022. "Is artificial intelligence an enabler of supply chain resiliency post COVID-19? An exploratory state-of-the-art review for future research," Operations Management Research, Springer, vol. 15(1), pages 378-398, June.
- Papanagnou, Christos & Seiler, Andreas & Spanaki, Konstantina & Papadopoulos, Thanos & Bourlakis, Michael, 2022. "Data-driven digital transformation for emergency situations: The case of the UK retail sector," International Journal of Production Economics, Elsevier, vol. 250(C).
- Kong, Lingxuan & Zheng, Ge & Brintrup, Alexandra, 2024. "A federated machine learning approach for order-level risk prediction in Supply Chain Financing," International Journal of Production Economics, Elsevier, vol. 268(C).
- Balezentis, Tomas & Zickiene, Agne & Volkov, Artiom & Streimikiene, Dalia & Morkunas, Mangirdas & Dabkiene, Vida & Ribasauskiene, Erika, 2023. "Measures for the viable agri-food supply chains: A multi-criteria approach," Journal of Business Research, Elsevier, vol. 155(PA).
- Spreitzenbarth, Jan & Stuckenschmidt, Heiner & Bode, Christoph, 2021. "The state of artificial intelligence: Procurement versus sales and marketing," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Ringle, Christian M. & Blecker, Thorsten (ed.), Adapting to the Future: How Digitalization Shapes Sustainable Logistics and Resilient Supply Chain Management. Proceedings of the Hamburg Internationa, volume 31, pages 223-243, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
- Ivanov, Dmitry & Dolgui, Alexandre & Sokolov, Boris, 2022. "Cloud supply chain: Integrating Industry 4.0 and digital platforms in the “Supply Chain-as-a-Service”," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 160(C).
- Simonetto, Marco & Sgarbossa, Fabio & Battini, Daria & Govindan, Kannan, 2022. "Closed loop supply chains 4.0: From risks to benefits through advanced technologies. A literature review and research agenda," International Journal of Production Economics, Elsevier, vol. 253(C).
- Meike Schroeder & Sebastian Lodemann, 2021. "A Systematic Investigation of the Integration of Machine Learning into Supply Chain Risk Management," Logistics, MDPI, vol. 5(3), pages 1-17, September.
- Gonçalves, João N.C. & Sameiro Carvalho, M. & Cortez, Paulo, 2020. "Operations research models and methods for safety stock determination: A review," Operations Research Perspectives, Elsevier, vol. 7(C).
- Chowdhury, Priyabrata & Paul, Sanjoy Kumar & Kaisar, Shahriar & Moktadir, Md. Abdul, 2021. "COVID-19 pandemic related supply chain studies: A systematic review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 148(C).
- Antoshchenkova, Vitalina & Onegina, Viktoriya & Gutsul, Tetiana & Boblovskyi, Oleksandr & Kravchenko, Yuliia, 2023. "Methodological approach for determining the size of the optimal raw material zone in the logistics system of dairy processing enterprise," Agricultural and Resource Economics: International Scientific E-Journal, Agricultural and Resource Economics: International Scientific E-Journal, vol. 9(1), March.
- Ivanov, Dmitry, 2023. "Intelligent digital twin (iDT) for supply chain stress-testing, resilience, and viability," International Journal of Production Economics, Elsevier, vol. 263(C).
- Burgos, Diana & Ivanov, Dmitry, 2021. "Food retail supply chain resilience and the COVID-19 pandemic: A digital twin-based impact analysis and improvement directions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
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