Forest harvest operations productivity forecasting: a decisions tree approach

ABSTRACT

In recent years, machinery used in forest harvesting operations has incorporated the ability to collect data during the harvesting operation automatically. The processing of these data allows for obtaining new perspectives on the harvest characteristics. In this sense, it is that the development of predictive models for harvest productivity is approached by processing the automatically retrieved data. In turn, these new models pave the way to develop new tools for operations management decision-making processes, providing a data-driven approach. In this case, forest productivity is analyzed based on different harvesting operational configurations defined by stand, trees, species, operators, shifts, etc. which make it possible to adequately predict what the wood-harvested volume will be, and thus, synchronize the rest of the supply chain logistics operations. The data processing is done through decision tree methods. Different methods of decision trees based on exhaustive Chi-square automatic interaction detection (CHAID), recursive binary partition, and conditional inference based that also uses binary recursive partition are tested. The results show that decision recursive binary partition methods tend to model the entire spectrum of the target variable in a more balanced manner. While exhaustive CHAID-based methods tend to be more accurate in global terms but more unbalanced. As a general comment of the method, global confusion matrices are around 50% accurate, and some operational configurations and productivity classes are predicted with almost 90% accuracy.

KEYWORDS:

• Machine learning
• forest harvest productivity
• binary recursive partition
• operations management
• data-driven
• operations planning

Disclosure statement

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