Artificial intelligence in the forest products supply chain: current applications and open challenges

Abstract The forest products supply chain (FPSC) is a complex distributed network that transforms raw forest resources into finished goods. It faces inherent complexities because of factors like divergent processes, coordination of independent business units, volatile markets, logistical challenges, and resource constraints. As supply chains across industries become more data driven, artificial intelligence (AI) has emerged as a powerful tool for optimizing supply chain operations. However, there has been limited research that systematically investigates the usage of such technologies in the FPSC. Here, we used a combination of a systematic literature review and a hermeneutic approach to examine the existing implementations and recent advancements of AI applications in the FPSC, and discuss key research challenges and future opportunities for AI adoption. It was found that a wide range of AI-based applications and algorithms were developed for specific purposes along the FPSC. For example, reinforcement learning was found to be especially suitable for spatial planning while convolutional neural networks were favoured for species classification and quality assurance from image data. Using a framework developed for this review, we highlight underexplored domains and open challenges which relate to fibre supply, forest operations, log storage, and transportation. AI methodologies are still rarely applied for tasks like harvest block allocation, inventory policy, and forest road layout design. For these underexplored domains, we suggest methodological solutions adopted from broader supply chain research which we assume to have high transferability potential to the FPSC. With this review, we aim on guiding stakeholders in leveraging AI for enhanced operational efficiency and informed decision-making.