Use of automatically obtained data in the quantitative and qualitative evaluation of harvester operator training
The study aims to find out the efficiency of periodic training of the harvester (cut-to-length) operator, using the automatically saved data of the harvesterʼs information system. It has been established that logging service providers and training institutions do not analyze the operatorʼs work before starting the training; therefore, the training is carried out according to certain, standard programs, without going into the previous performance of each trainee operator. The research uses data automatically saved by Ponsse harvesters during the year, obtained from Ponsse Manager. The study found that by using automatically saved data of the harvester information and performing data grouping with subsequent analysis, it is possible to determine the progress in the execution of specific stem processing operations and to identify operations where the instructor should pay increased attention during training. The research analyzed the data of 3 operators, obtained while working with Ponsse harvesters in clear-cutting. In the study, it was found that operator A reduced stem processing time by 3%, labor productivity increased by 15%, and fuel consumption per l m-3 decreased by 14% over 3 months. Operator B, after training, saw a 20% reduction in stem processing time, a 13% increase in work productivity, and a 5% increase in fuel consumption l m-3 over 3 months. Operator C had a 10% increase in stem processing time, a 1% increase in labor productivity, and a 2% increase in fuel consumption l m3 after training.
Authors
- Linards Sisenis (ORCID: https://orcid.org/0009-0006-6414-2464)
- Ziedonis Sarmulis
- Aigars Strubergs (ORCID: https://orcid.org/0000-0002-2155-1763)
Institutions
- Latvia University of Life Sciences and Technologies (LV)
Publication Details
- Journal
- Research for Rural Development/Research for Rural Development (Online)
- Published
- 2024-11-27
- DOI
- https://doi.org/10.22616/rrd.30.2024.009
- Primary Topic
- Mining and Industrial Processes
- Type
- article
- Field-Weighted Citation Impact
- 0.00