Toward a digital twin to improve the training and performance of forestry operators

Digitizing forestry operations holds potential for enhancing productivity across the components of the forest value chain. A crucial element in this chain is the machine operator, who uses forestry equipment to extract timber from the landscape. Therefore, developing technologies to support and guide these operators can lead to substantial benefits. Digital twins, which can be defined as a digital representation of a physical entity updated in real time, offer a new opportunity when modeling complex systems. However, adapting the digital twin concept to forest operation is a complex matter, as human activities are difficult to model and simulate in specialized work situations. Additionally, digital twin studies have seldom placed emphasis on the human being and specialized worker assistance, even more so for forest operations applications. This paper presents a literature review that mixes narrative and integrative methodologies to evaluate the feasibility of a digital twin combining the operator and the forest machine. As there are a few reports on this specific topic, we expanded our investigation by looking into other fields such as healthcare, mechanical design, and smart factory. From this review we conclude that the existing technologies can be used to create such an operator-forest machine digital twin. Furthermore, we present recommendations about the logic and simulation architecture needed for such an operator-forest machine twin. We also present a proof of concept for such a twin using a commercial vehicle simulator to validate our approach.