![Sample our Engineering & Technology journals, sign in here to start your access, latest two full volumes FREE to you for 14 days]({"type":"image" , "src" : "/sda/5933/TOC-Subject-Sample-2021-Engineering-Tech.jpg"})
Abstract
It is well-known that machine operators vary in their performance when undertaking mechanized forestry harvesting operations. Nevertheless, the human factor is still largely disregarded in productivity calculations. In the present study, operator performance is evaluated by analysing archived production data collected automatically by computers on-board single grip harvesters driven by 32 operators working in 3,351 stands over a period of three years. The experimental conditions were all approximately the same. The effect of the operators is modelled by a multilinear regression analysis. Seventeen operators were found to have performance levels that differed significantly from the mean model. Together, ‘tree volume’ and ‘operator’ explained 84% of the overall variance. However, since 37.3% of the variance in productivity is explained by the operator, the influence of the operator on productivity is quite large. The minimum and maximum significant mean productivity values for all the operators differed by a factor of 2.2, which reduced to a factor of 1.8 if only data from experienced operators were analysed, although this still demonstrates that the best operators are nearly twice as productive as the worst. The operator, therefore, has an important influence on productivity and should be considered a key factor in productivity models.