Machine learning applications in forest and biomass supply chain management: a review

ABSTRACT

Forest and biomass crops for bioenergy and bioproducts can promote a sustainable bioeconomy while effectively reducing greenhouse gas (GHG) emissions to mitigate global warming. One of the most concerning issues is selecting and using appropriate modeling and analytical technologies to optimize the benefits of multi-feedstock biomass supply chains, including logistics. Machine learning (ML) has been used to solve increasingly complex supply chain problems, providing powerful tools for sustainable forest management and biomass resource development. Existing research is extensive and spans many different ML techniques, but synthesis is needed to help guide the adoption of these rapidly evolving tools. This review summarizes ML applications in forest and biomass supply chain management in terms of data, algorithms, and process examples, with an emphasis on direct application to supply chain management. ML is a viable technique to support strategic, operational, and tactical planning and decision-making in this field and can enhance the environmental and economic performance of diverse forest and biomass supply chains.

KEYWORDS:

• Machine learning
• algorithm
• modeling
• forest resources
• biomass
• supply chain management

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work was supported by the National Institute of Food and Agriculture [20206801231881].