Selecting successful harvester operators through aptitude tests and demographics

Abstract

ABSTRACT 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 pre-selection 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, 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.