MONITORING-DRIVEN DECISION SUPPORT FOR AVIATION SPARE PARTS LOGISTICS

Abstract

The increasing complexity of transport technologies, maintenance operations, and spare-parts logistics requires decision-support approaches that integrate monitoring, information quality assessment, and operational risk evaluation under capacity-constrained conditions. Although existing research has extensively addressed inventory optimization and maintenance scheduling, considerably less attention has been devoted to understanding how monitoring quality and information reliability influence logistics resilience and the continuity of transport systems. This paper proposes a monitoring-driven decision-support framework for integrated transport spare-parts logistics that combines qualitative system modelling with symbolic decision structures and logistics risk assessment. The proposed framework links monitoring processes, information quality, spare-parts availability, repair coordination, and human decision-making into a unified analytical architecture supporting modern transport technologies. Information deficiencies are interpreted as control deviations that propagate through logistics processes and increase the probability of transport asset downtime and service disruption. A conceptual monitoring-to-risk propagation model is introduced to describe the relationship between information quality, automation performance, operator intervention, and logistics resilience under degraded operating conditions. Furthermore, an illustrative multi-criteria decision formulation is presented to analyse trade-offs between transport availability, logistics costs, inventory policies, and expedited dispatch strategies. The proposed modelling approach is intended primarily for conceptual and qualitative analysis, rather than for precise operational prediction. It provides a structured foundation for analysing monitoring-driven logistics resilience, supporting risk-informed decision-making, and facilitating the future development of digital transport technologies, digital twins, and intelligent decision-support systems for integrated transport logistics.