Integrated Decision-Making Architecture Merging Predictive Maintenance, Risk Assessment, and Revenue Optimization in Rental Fleet Management

Abstract

Component degradation that stays below the OBD-II diagnostic trouble code threshold escapes fixed-threshold maintenance scheduling in rental fleets. Revenue management literature classifies vehicle condition as an exogenous constraint on capacity allocation. The economic cost of maintenance timing sits outside the optimization objective in most published formulations. This paper specifies an architecture in which component-health estimation, behavioral risk scoring, and revenue-lifecycle optimization share a single time-indexed vehicle state. Maintenance timing resolves through a demand-aware window search subordinate to a hard safety override; an explicit priority hierarchy addresses the failure mode a purely multiplicative composite score produces when a single factor approaches zero. Evaluated through an 18-month field pilot across 1200 vehicles in the Phoenix, Arizona metropolitan market, the architecture achieves 91.4% empirical interval coverage on calibrated remaining-useful- life estimates, close to the 90% nominal target. Component degradation surfaces a median of 11.3 days before functional failure on cases that stay below a diagnostic trouble code threshold. Behavioral risk estimates converge within 2.6 minutes when cross-session history exists; a kinematics- only baseline converges at 14.1 minutes. Opportunity cost per maintenance event drops by 29.5% under demand coefficients of variation at or above 0.35, a benefit that narrows to 6.8% once demand approaches uniformity. Prior insurance-telematics findings require multi-month accumulation for stable risk scoring. An open question remains: whether cumulative behavioral risk exposure should enter the lifecycle score as a temporal pattern, distinguishing accumulation trajectories that reach the same aggregate value through different event sequences.