AI-ENABLED OPERATIONAL INTELLIGENCE FOR COMMERCIAL TRUCKING: INTEGRATING PREDICTIVE MAINTENANCE, EARLY WARNING, AND DECISION SUPPORT FOR SMALL FLEETS
Abstract
Commercial trucking generates increasingly diverse operational data, yet the practical value of these data depends on whether they can be converted into timely, contextualized decisions. Two persistent challenges are closely connected: identifying developing vehicle risks before failure and enabling small and midsized carriers to manage maintenance, fuel, safety, dispatch, and financial performance with limited analytical resources. This conceptual article integrates these challenges into a unified framework for AI-enabled operational intelligence.
The study uses practice-informed conceptual synthesis of two founder-authored essays on vehicle early warning and AI capabilities for smaller fleets, interpreted in light of research on predictive maintenance, machinery health prognostics, small- and medium-sized enterprise AI adoption, and responsible AI governance. The resulting framework comprises seven layers: data ingestion; data quality and interoperability; contextual baseline modeling; anomaly and exception detection; risk-based prioritization; human-in-the-loop decision support; and feedback, audit, and governance. It connects vehicle-level signals—such as tire-pressure trends, diagnostic codes, mileage, and maintenance history—with cross-functional information concerning fuel use, driver and route context, dispatch constraints, and asset-level costs.
The framework treats AI outputs as evidence-linked review priorities rather than autonomous diagnoses or decisions. Its potential value for smaller carriers is conditional on data quality, workflow fit, staff capability, and accountable human review. Five propositions and a prospective evaluation design are offered for empirical testing. Because no fleet dataset, interviews, or intervention results were available, the article reports a conceptual model rather than measured operational benefits.
Keywords
artificial intelligence; commercial trucking; fleet management; predictive maintenance; early-warning systems; small trucking fleets; operational intelligence; human-in-the-loop decision support.How to Cite
References
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Copyright (c) 2026 Abdulkhamid Abdullaev
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