An Adaptive Work Study Method for Identifying the Human Factors that Influence the Performance of a Human-Machine System
The objective of this research was to develop a methodology for adjusting machine and work models after a short work period using a harvester simulator and the application of factor analysis. The methodology integrates work models into a harvester system controller that controls the machine's complex work cycle under conditions in which the phases of the work cycle overlap to varying degrees. During the automatic work phase analysis, four principal components of the harvester's work were identified and were subsequently integrated within a multidomain productivity model. We then compared and analyzed the performance of five operators using a harvesting simulator under similar work conditions. We found that operator productivities were mostly similar. However, the work model differed among the operators, because similar harvesting productivity could be attained on the basis of different stem-processing decisions and different fuel consumption rates. We used the detailed productivity and work phase data provided by the automatic monitoring system to identify the most important work phases in each work model. The results are satisfactory and the methodology can be easily used in adaptive controllers that modify a machine's behavior to suit each operator's unique work model. The potential for adjusting the work model (e.g., during training), thereby improving productivity and reducing fuel consumption, is high but must be confirmed through additional experiments or during real-world work studies.
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Document Type: Research Article
Publication date: 2012-08-02
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