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Unlocking Real-Time Decision-Making in Warehouses: A machine learning-based forecasting and alerting system for cycle time prediction

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  • Aloini, Davide
  • Benevento, Elisabetta
  • Dulmin, Riccardo
  • Guerrazzi, Emanuele
  • Mininno, Valeria

Abstract

In highly automated warehouses characterized by unpredictable demand, timely decision-making is critical to maintaining operational efficiency. This study proposes a forecasting and alerting system for real-time warehouse management. The system utilizes a Machine Learning (ML)-based predictive model to forecast picking order tardiness using Warehouse Management System data, complemented by a real-time alerting mechanism to support operators in in making informed short-term decisions. A case study conducted in a Shuttle-Based Storage and Retrieval Systems (SBS/RS) of a tire distribution company validates the system’s effectiveness. Particularly, several ML techniques were tested to find the best forecasting model, leveraging a set of predictors tailored to the characteristics of the warehouse. Simulation with real data demonstrates significant reductions of peak cycle times and in total cycle time.

Suggested Citation

  • Aloini, Davide & Benevento, Elisabetta & Dulmin, Riccardo & Guerrazzi, Emanuele & Mininno, Valeria, 2025. "Unlocking Real-Time Decision-Making in Warehouses: A machine learning-based forecasting and alerting system for cycle time prediction," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 194(C).
    • Handle: RePEc:eee:transe:v:194:y:2025:i:c:s1366554524005246
    DOI: 10.1016/j.tre.2024.103933
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