Applications of machine learning for decision support in biomass supply chains: A systematic review

Effective planning of biomass supply chains (BSC), which involve collection, transportation, pre-processing, storage, conversion, and delivery of bioproducts, is essential to ensure efficiency and sustainability. Recently, machine learning (ML) has been adopted to address the supply chain’s complexities for effective planning. ML provides dynamic and data-driven solutions that enhance decision-making. It has been applied for predicting biomass yields, forecasting supply and demand, optimizing logistics and facility location, and improving the efficiency of conversion processes. This review paper highlights the role of ML in BSC planning. This study considers biomass sources such as food processing residues, animal waste (e.g., manure), in addition to forest-based and agricultural-based biomass, examining processes across all stages of a supply chain from upstream to downstream. We examine ML models in previous studies based on their learning paradigms: supervised, unsupervised, and reinforcement learning, and the type of performed analytics: predictive, and both predictive and prescriptive analytics. Challenges related to data availability, computational requirements, and model generalization limit ML applications in BSCs. Future research could focus on scalable and adaptable models for preprocessing, transportation, and harvesting activities by addressing the uncertainty. Integrating advanced ML could significantly enhance the resiliency, sustainability, and efficiency of BSCs, supporting bioeconomy advancement and the achievement of sustainability goals.