AI-Driven Demand Forecasting and Inventory Optimization in U.S. Car Rental Supply Chain Operations: A Technology-Organization-Environment and Resource-Based View Analysis

Abstract

This paper examines AI-driven demand forecasting adoption as the independent variable and fleet inventory accuracy and supply chain cost efficiency as the dependent variables in United States car rental operations. The United States car rental industry generated revenues of approximately $43.9 billion in 2024, with the five largest operators accounting for 65 percent of global market revenue. Car rental supply chain performance, specifically the ability to match vehicle availability by class and location to reservation demand in real time, is a primary determinant of both fleet utilization rates and customer satisfaction outcomes. Traditional rule-based and statistical demand forecasting methods fail to capture the complex, multi-variable demand patterns that drive vehicle class shortfalls, excess inventory costs, and emergency procurement premiums in car rental supply chains. Drawing on the Resource-Based View of the firm, the Technology-Organization-Environment framework, and the meta-analytic findings of a 2025 study across 174 studies and 104,296 informants confirming analytics capability as a positive driver of supply chain performance, this paper conducts a systematic narrative literature review, maps five AI technique categories applicable to car rental supply chain forecasting, applies the TOE framework to adoption barriers, develops five testable hypotheses, and proposes an empirical validation design. The analysis finds that AI-driven demand forecasting capability satisfies the Resource-Based View conditions for sustained competitive advantage in car rental supply chain management, and that the primary barriers to realizing this advantage are organizational rather than technological.