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

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

Transportation Research Part E: Logistics and Transportation Review, 2025, vol. 194, issue C

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.

Keywords: Artificial Intelligence; Decision-making; Supply Chain Management; Machine Learning; Automated warehouse; Real-time prediction (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.tre.2024.103933

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