Machine Learning Applications in Biomass Supply Chain Management and Optimization

Abstract

Forest and biomass resource utilization for bioenergy and bioproducts is crucial for mitigating climate change and promoting a sustainable bioeconomy. Given that the biomass supply chain is a complex system, one of the most concerning issues is selecting and using appropriate modeling and analytical technologies to optimize the advantages of multi-feedstock biomass supply chains. Machine learning (ML) can enhance biomass supply chain management (BSCM) efficiency and sustainability. It can address the complexities in cultivation, harvesting, preprocessing, storage, transportation, and conversion. ML workflows involve data collection, preprocessing, model training, and optimization, using algorithms for prediction and decision-making. Accurate supply and demand forecasting via ML improves production planning and inventory management. Despite its potential, ML applications in BSCM need to deal with challenges such as data availability and quality, interpretability of models, and their generalization capabilities. Overcoming such challenges requires interdisciplinary efforts in data management and model development to fully leverage ML’s applicability.