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Delay Prediction

Yunbo Long 483 words 3 minutes Machine Learning Forecasting Logistics

Delay prediction is a critical capability in modern supply chain management, where even small disruptions in delivery timelines can cascade into significant operational and financial consequences. As global supply chains grow more complex and interconnected, the ability to anticipate delays—before they occur—has become a key differentiator for resilient organisations.

Traditional approaches to managing delivery delays relied heavily on historical averages and rule-based heuristics. However, advances in machine learning and data-driven analytics have opened new avenues for predictive modelling. Techniques ranging from gradient boosting and random forests to deep learning and graph neural networks are now being applied to forecast delays at various stages of the supply chain—from supplier lead times and manufacturing throughput to last-mile logistics.

A growing body of research, including work from the Supply Chain AI Lab at the University of Cambridge, has demonstrated the value of combining structured supply chain data (e.g., purchase orders, shipment records) with external signals (e.g., weather, port congestion, geopolitical events) to improve prediction accuracy. These methods not only enhance operational planning but also support proactive risk mitigation strategies.

We invite you to explore the curated collection of key publications below, offering insights into the evolution and state of the art in supply chain delay prediction.

List of Publications

  1. Xu, L., Brintrup, A. and Chattopadhyay, I., 2024. Delay prediction in supply chains using graph neural networks. IFAC-PapersOnLine, 58(19), pp.801-806. [PDF]
  2. Brintrup, A., Pak, J., Ratiney, D., Pearce, T., Wichmann, P., Woodall, P. and McFarlane, D., 2020. Supply chain data analytics for predicting supplier disruptions: A case study in complex asset manufacturing. International Journal of Production Research, 58(11), pp.3330-3341. [PDF]
  3. Carbonneau, R., Laframboise, K. and Bhardwaj, A., 2008. Application of machine learning techniques for supply chain demand forecasting. European Journal of Operational Research, 184(3), pp.1140-1154. [PDF]
  4. Baryannis, G., Validi, S., Dani, S. and Antoniou, G., 2019. Supply chain risk management and artificial intelligence: State of the art and future research directions. International Journal of Production Research, 57(7), pp.2179-2202. [PDF]
  5. Cavalcante, I.M., Frazzon, E.M., Forcellini, F.A. and Ivanov, D., 2019. A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing. International Journal of Information Management, 49, pp.86-97. [PDF]
  6. Ni, D., Xiao, Z. and Lim, M.K., 2020. A systematic review of the research trends of machine learning in supply chain management. International Journal of Machine Learning and Cybernetics, 11, pp.1463-1482. [PDF]