AI-Driven Optimization of Domestic Timber Supply Chains to Enhance U.S. Economic Security

Abstract

 The U.S. timber industry contributes approximately $304 billion annually to the national economy and supports over 950,000 jobs across harvesting, processing, and distribution sectors. However, increasing global competition, environmental regulations, and fragmented logistics have exposed vulnerabilities in domestic timber supply chains. This research explores how Artificial Intelligence (AI) can optimize these supply chains to enhance U.S. economic security and sustainability. Using a conceptual framework grounded in posthumanist theory and socio-technical systems thinking, the study synthesizes case-based insights and scenario modeling from AI applications in forestry logistics. Evidence from similar sectors indicates that AI-enabled route optimization can reduce fuel consumption by up to 22%, while predictive yield estimation can increase harvesting accuracy by 35–40%. The study proposes an AI-driven supply chain model integrating real-time forest data, smart routing algorithms, and predictive demand analytics. It further highlights how such integration redefines human-environment-technology relations in line with posthumanist thought. The findings suggest that adopting AI in timber logistics can not only improve operational efficiency but also reinforce national supply independence, reduce reliance on imports (currently 19.4% of total timber demand), and strengthen economic resilience. This paper calls for immediate interdisciplinary research and policy alignment to responsibly implement AI technologies across the U.S. forestry sector.