Smart maintenance scheduling via predictive logistics for post-flight engine maintenance in Central Asia

Abstract

This paper presents a data-driven approach to post-flight aircraft engine maintenance based on Smart Maintenance Scheduling via Predictive Logistics (SMS-PL). Unlike conventional time- or cycle-based programs, SMS-PL integrates environmental exposure, operational stress indicators, and digital logistics planning to dynamically prioritize inspections and material preparation. The study focuses on Central Asian operations characterized by large temperature excursions, frequent dust exposure, and dispersed maintenance infrastructure. The proposed framework combines environmental monitoring, flight-path analytics, and maintenance history to form a per-flight Maintenance Severity Index (MSI) that supports proactive decisions. Conceptual analysis indicates that predictive logistics can reduce turnaround delays, improve spare-part positioning, and raise effective fleet readiness under regional constraints. The approach aligns maintenance activity with measured operating context, supporting reliability, efficiency, and sustainability goals in arid environments.