Selecting productive logging machine operators

Friday 5 Nov 2021

 
The Vienne Test System, followed by the use of simulators, results in productive operators being selected and their subsequent learning curve being quicker with less machine damage.

Researchers used people with no simulator or logging machine experience to test whether applicable aptitude tests, such as the Vienne Test System, could successfully select operators that would be productive machine operators. The abstract of the research summaries the study and results for us. Interesting results were obtained regarding the gaming habits of the group and gender.

Simulators are used worldwide for various applications in different industries (e.g., aviation and medicine), generally to train prospective operators for actual work situations. The forest industry is no exception, with numerous studies – mostly in countries such as Finland, Norway, Switzerland and the United States of America – showing that simulator-based training has many advantages, especially for fast and inexpensive learning. Little information is available, however, relating to the preselection of harvesting operators prior to simulator-based training.

The aim of this study was to determine whether harvesting simulators could be used in conjunction with the Vienna Test System to identify potential harvesting operators. A mixed methods approach (quantitative work study data and qualitative questionnaire data) was used to determine differences among 14 volunteer participants, each of which spent a total of ten hours using the simulator.

After completing demographic questionnaires, participants used the Vienna Test System. The test is designed to measure hand–eye coordination, the ability to concentrate for long periods, and the participant’s cognitrone (concentration performance), and it is used in the mining industry as a pre-selection tool for heavy machine operators.

Preliminary results show that the Vienna Test System was able to pre-identify individuals who are fast and productive. Many studies have indicated that effective and efficient operators require these abilities and more. Learning improved at different rates among participants over the ten hours spent on the simulator.

The research was published in Australian Forestry, Volume 84(1) 2021. The authors are K Schwegman, R Spinelli, N Magagnotti, M Ramantswana and A McEwan.

Source: tandfonline



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