Highlights• Recent developments in on-board technology enables automatic collection of follow-up data on forwarder work.• Time consumption per load was more strongly associated with Loading drive distance than with extraction distance, indicating that the relevance of extraction distance as a main indicator of forwarding productivity should be re-considered.• Data, within variables, were positively skewed with a few exceptions with normal distributions. AbstractRecent developments in on-board technology have enabled automatic collection of follow-up data on forwarder work. The objective of this study was to exploit this possibility to obtain highly representative information on time consumption of specific work elements (including overlapping crane work and driving), with one load as unit of observation, for large forwarders in final felling operations. The data used were collected by the John Deere TimberLink system as nine operators forwarded 8868 loads, in total, at sites in mid-Sweden. Load-sizes were not available. For the average and median extraction distances (219 and 174 m, respectively), Loading, Unloading, Driving empty, Driving loaded and Other time effective work (PM) accounted for ca. 45, 19, 8.5, 7.5 and 14% of total forwarding time consumption, respectively. The average and median total time consumptions were 45.8 and 42.1 minutes/load, respectively. The developed models explained large proportions of the variation of time consumption for the work elements Driving empty and Driving loaded, but minor proportions for the work elements Loading and Unloading. Based on the means, the crane was used during 74.8% of Loading PM time, the driving speed was nonzero during 31.9% of the Loading PM time, and Simultaneous crane work and driving occurred during 6.7% of the Loading PM time. Time consumption per load was more strongly associated with Loading drive distance than with extraction distance, indicating that the relevance of extraction distance as a main indicator of forwarding productivity should be re-considered.

No abstract

No abstract

In machine work, the productivity, energy efficiency, and the quality of the work depend strongly on the skills of the human operator. This paper proposes a hierarchical method for skill evaluation of human operators in machine work during their normal work. The method refines skill metrics obtained from work cycle recognition -based evaluation system proposed earlier by the authors. The proposed skill components are: machine controlling skills, control parameter tuning skills, knowledge of the work technique and strategy, and planning and decision making skills. The skill components in each task are evaluated by a dedicated fuzzy inference system, whose rule base is generated automatically. The method is utilized to evaluate skills of nine operators of a cut-to-length forest harvester.

No abstract