AI-Driven Demand Forecasting and Inventory Optimization in U.S. Car Rental Supply Chain Operations: A Technology-Organization-Environment and Resource-Based View Analysis
DOI:
https://doi.org/10.65150/EP-jefrr/V2E8/2026-10Keywords:
Data-driven procurement, Fleet management intelligence, Logistics analytics adoption, Predictive fleet analytics, Vendor coordination strategyAbstract
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.
References
1) Association to Advance Collegiate Schools of Business (AACSB). (2020). 2020 guiding principles and standards for business accreditation. AACSB International. https://www.aacsb.edu/-/media/documents/accreditation/2020-aacsb-business-accreditation-standards-june-2023.pdf
2) Barney, J. B. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99-120.
https://doi.org/10.1177/014920639101700108
3) Becker, G. S. (1964). Human capital: A theoretical and empirical analysis, with special reference to education. University of Chicago Press.
4) Brynjolfsson, E., Mitchell, T., and Rock, D. (2018). What can machines learn, and what does it mean for occupations and the economy? AEA Papers and Proceedings, 108, 43-47. https://doi.org/10.1257/pandp.20181019
5) Bureau of Labor Statistics, U.S. Department of Labor. (2024). Productivity and costs: Fourth quarter and annual averages 2023. https://www.bls.gov/news.release/prod2.nr0.htm
6) CBRE Hotels Research. (2024). New labor challenges arise in 2023. Trends in the Hotel Industry.
https://www.hotel-online.com/press_releases/release/new-labor-challenges-arise-in-2023/
7) Euromonitor International. (2024). The world market for car rental. Euromonitor International. https://www.euromonitor.com/the-world-market-for-car-rental/report
8) Filani, O. M., Olajide, J. O., and Osho, G. O. (2023). Artificial intelligence in demand forecasting and inventory optimization. Journal of Applied Research in Technology and Innovation. https://doi.org/10.62225/2583049X.2023.3.6.4617
9) Grand View Research. (2024). U.S. car rental market size, share and trends analysis report. Grand View Research.
https://www.grandviewresearch.com/horizon/outlook/car-rental-market/united-states
10) Hasan, M. R., Islam, M. R., and Rahman, M. A. (2025). Developing and implementing AI-driven models for demand forecasting in US supply chains. Edelweiss Applied Science and Technology. https://doi.org/10.55214/25768484.v9i1.4308
11) Khwaja, A., and Yang, N. (2022). Quantifying the link between employee engagement, and customer satisfaction and retention in the car rental industry. Quantitative Marketing and Economics, 20, 275-292. https://doi.org/10.1007/s11129-022-09253-6
12) Mordor Intelligence. (2025). United States car rental market size and outlook. Mordor Intelligence.
https://www.mordorintelligence.com/industry-reports/united-states-car-rental-market
13) Nikopoulou, M., Kourouthanassis, P., Chasapi, G., Pateli, A., and Mylonas, N. (2023). Determinants of digital transformation in the hospitality industry: Technological, organizational, and environmental drivers. Sustainability,15(3),2736. https://doi.org/10.3390/su15032736
14) Raj, A., Kumar, R. R., Narayanan, S., Kirca, A. H., and Jeyaraj, A. (2025). Analytics capability, supply chain capabilities, and operational performance: A meta-analytic investigation. Journal of Operations Management, 71(6), 786-805. https://doi.org/10.1002/joom.1376
15) Schultz, T. W. (1961). Investment in human capital. American Economic Review, 51(1), 1-17. https://www.jstor.org/stable/1818907
16) Soppert, M., Oliveira, B. B., Angeles, R., and Steinhardt, C. (2024). On the benefit of combining car rental and car sharing. Journal of Business Economics, 94(9), 1261-1298. https://doi.org/10.1007/s11573-024-01204-9
17) Tornatzky, L. G., and Fleischer, M. (1990). The processes of technological innovation. Lexington Books.
18) Trenerry, B., Chng, S., Wang, Y., Suhaila, Z. S., Lim, S. S., Lu, H. Y., and Oh, P. H. (2021). Preparing workplaces for digital transformation: An integrative review and framework of multi-level factors. Frontiers in Psychology, 12, 620766.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Salami Abdul Mohammed (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
