Fleet Analytics and Operational Error Detection in U.S. Car Rental Operations

Abstract

This paper examines the relationship between fleet analytics adoption as the independent variable and operational error detection outcomes as the dependent variable in United States car rental operations. Despite substantial investment by major car rental operators in artificial intelligence-driven analytics platforms, the managerial capability required to oversee, interpret, and govern automated outputs is not being developed systematically. Drawing on Human Capital Theory and the Technology-Organization-Environment framework, the paper conducts a systematic narrative literature review of peer-reviewed research in fleet management, operations management, machine learning, and car rental revenue management. Five fleet analytics domains generating operational error detection demands are mapped: dynamic pricing and revenue management, fleet repositioning analytics, predictive maintenance, demand forecasting, and customer segmentation. Barriers to effective analytical oversight are identified across the three framework dimensions and are found to be concentrated in the organizational and environmental dimensions rather than the technological one. Fleet analytics platforms are deployed at major operators; the managerial capability to govern them is not keeping pace with deployment. High staff turnover, data fragmentation, absent industry mandates, algorithm interpretability constraints, and margin pressure collectively prevent operators from realizing the full value of their analytics investments. The paper proposes a three-level reskilling framework covering operational analytics literacy, human-AI collaboration skills, and algorithmic governance capability, calibrated to operators at different organizational scales. Industry associations are identified as having the institutional capacity to address the coordination failure that prevents individual operators from investing adequately in analytics oversight capability.