Automatic and experimental methods to studying forwarding work

Abstract: Although forwarding has been carried out for 50 years, much is still unknown about this work. This is partly because there are numerous influential factors, and relevant data are often difficult to gather. In current forwarding productivity literature, there is generally a trade-off between representativeness and work element-specificity. Follow-up studies and standardized experiments represent the two extremes, while work observation studies are compromises. A further complication is the lack of consistent nomenclature to facilitate comparisons of findings from different studies. These issues were explored in four studies reported in this thesis. Study I assessed the utility of standardized test paths for enhancing our understanding of the main factors influencing forwarding work, causal relationships among them, and trade-offs. Such knowledge is essential for refining future research. In Studies II and III, the utility of a forest machine manufacturer’s built-in automatic follow-up datalogger was assessed. Study II focused mainly on the suitability of a standard commercial monitoring system for comparative operator-level studies. In Study III, forwarder work element-specific follow-up data were gathered in as detailedly as currently possible using an automatic system. In Study IV, the utility of sensors and dataloggers for gathering technical information on forwarder crane work was assessed. The main conclusions are summarized below. Automated data collection has well known advantages, but such automation for forwarding work is still ongoing. Data from the forwarder’s own monitoring system alone are not, currently, sufficient for unbiased work performance analysis. In addition, access to spatial data on the harvester’s production is needed. Use of untapped technological potential would enable, in many cases, replacement of manual data gathering with automatic methods. However, automatic gathering of data with some important features, e.g. assortment-specificity (load-specific assortment proportions), is currently impossible. Automation enables large datasets to be gathered, but increasing the sample size beyond a certain saturation point provides no further benefits. Instead, including more factors is preferable, even at the cost of slightly smaller datasets. Finally, various innovations and modifications to work practices could substantially improve forwarding efficiency; however, they should be evaluated cautiously, initially by theoretical analysis, to ensure resources are efficiently channelled.

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