Introducing Artificial Intelligence to Increase Efficiency in Warehouse Logistics: A Case Study
Keywords:
Artificial intelligence, Predictive analytics, Discrete event simulation, Warehouse optimisation, Demand forecasting, Operations managementAbstract
This paper examines how Artificial Intelligence (AI) forecasting and
discrete-event simulation can support adaptive warehouse planning by
integrating efficiency, workload balance, and operational resilience.
Using distribution warehouse data, machine-learning models forecast
daily delivery occurrence and order quantities, while simulation models
represent warehouse processes, identify capacity constraints, and test
staffing and layout scenarios. The results show that predictive analytics
provides a stable basis for short-term planning, while simulation
identifies order picking as the main operational bottleneck and the area
of highest resource utilisation. The study contributes to cybernetic and
systems-thinking research by operationalising an integrated planning
workflow in which predictive feedback informs simulation-based
experimentation and dynamic capacity alignment. The proposed
framework integrates throughput, resource utilisation, workload
distribution, and labour strain into a single adaptive planning approach.
The paper offers a replicable analytical approach for researchers and
practical guidance for managers seeking smoother workflows and more
sustainable resource use.
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Copyright (c) 2026 Enej Marinič, Igor Perko
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