Spare Parts Inventory Management in Freight Transport Logistics under Multifactor Uncertainty

Abstract

This article investigates approaches to adaptive spare parts inventory management in the context of freight transport logistics under conditions of multifactor uncertainty. The study accounts for the influence of variable vehicle loads, seasonal demand fluctuations, fleet technical condition, and risks of disruption in logistics processes. The use of digital technologies – particularly telematics systems, GPS monitoring, and Internet of Things (IoT) solutions – is substantiated for collecting and analysing operational data. This enables the formation of dynamic wear profiles and highly accurate forecasting of spare parts demand.

A conceptual adaptive inventory management model is proposed, integrating scenario modelling, empirical coefficients, and predictive algorithms based on real-world freight transport data. The developed model enables timely responses to changes in vehicle utilisation intensity, optimises inventory levels, reduces storage costs, and minimises downtime risks. A comparative analysis of traditional and adaptive inventory strategies confirms the advantages of flexible approaches in unstable environments.

The research findings can be applied to improve fleet maintenance efficiency, enhance logistics planning, and support the implementation of digital solutions in inventory management systems. The proposed models offer practical value for transport companies seeking to improve delivery reliability, reduce costs, and adapt to contemporary market challenges.

Key words: adaptive inventory management, spare parts, freight transport, telematics, Internet of Things (IoT), maintenance, digital logistics.