Prestationsprognos i drivning utifrån driftsuppföljningsdata

Abstract: There are several good reasons to create mathematical functions that can predict, or forecast, the productivity in logging operations. One of these reasons is that the contractor’s piece rate often is based on the forecasted productivity. There are different methods to generate a model that can forecast the productivity in logging operations. This study has used follow-up data from harvesters and forwarders, stem notes, logging site directives and measuring reports in the analysis. The purpose of this study was to identify and analyse a number of factors´ impact on productivity during thinning and final felling. This analysis was done by using available logging site- and follow-up data. The study’s overall aim was to establish a basis for a future contractor remuneration model which takes specific logging site conditions into account. The results from the analysis showed that mean stem size and hauling distance was the two most influential factors for harvesters and forwarders, respectively. This result could also be found in the literature from earlier studies. From the results of the analysis, three different models that could forecast productivity were created. The models could explain the harvester’s productivity to 72 percent during final felling and to 76 percent during thinning. The forwarder´s productivity could be explained to 87 percent by a single model for both final felling and thinning. However, until further evaluations have been done, the use of the prediction models generated from this study should be used with great caution when establishing contractors’ piece rates.