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<p>This study investigates the feasibility of using standardized SAE J1939 data from forestry machines, to enhance the decision-making process and ensure the sustainability of forest operations. The study, specifically focusing on a clam-bunk skidder, was conducted between January and September 2023, across three sites in northeast Italy in permeant-cover forest in the Alps region. The main objectives were to identify work phases, determine their durations, calculate fuel consumption, and create models for production and fuel consumption per work cycle, considering extraction distance and terrain slope. The feasibility of the Automated Time Study (ATS) methodology was tested for the first time in a mountainous region with varying slopes and stand compositions. Results showed that over 82% of working cycles were successfully identified, with 60–70% accuracy in identifying work elements within cycles. This high identification rate allows machine operators to detect bottlenecks and improve efficiency, additionally, this methodology aims to predicting future operational impacts and costs based on statistical analysis implemented by using a big data approach. However, the ATS methodology has limitations, potentially leading to significant estimation errors, thus critical thinking and effective communication with machine operators are essential to obtain accurate data.</p>

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Modelling full stems skidding in northeast Italian Alps through engine data acquisition and analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月28日

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  • Narcis Mihail Bacescu,
  • Omar Piol,
  • Bruce Talbot,
  • Luca Marchi,
  • Stefano Grigolato

摘要

This study investigates the feasibility of using standardized SAE J1939 data from forestry machines, to enhance the decision-making process and ensure the sustainability of forest operations. The study, specifically focusing on a clam-bunk skidder, was conducted between January and September 2023, across three sites in northeast Italy in permeant-cover forest in the Alps region. The main objectives were to identify work phases, determine their durations, calculate fuel consumption, and create models for production and fuel consumption per work cycle, considering extraction distance and terrain slope. The feasibility of the Automated Time Study (ATS) methodology was tested for the first time in a mountainous region with varying slopes and stand compositions. Results showed that over 82% of working cycles were successfully identified, with 60–70% accuracy in identifying work elements within cycles. This high identification rate allows machine operators to detect bottlenecks and improve efficiency, additionally, this methodology aims to predicting future operational impacts and costs based on statistical analysis implemented by using a big data approach. However, the ATS methodology has limitations, potentially leading to significant estimation errors, thus critical thinking and effective communication with machine operators are essential to obtain accurate data.