Time expenditure in computer aided time studies implemented for highly mechanized forest equipment

Time studies represent important tools that are used in forest operations research to produce empirical models or to comparatively assess the performance of two or more operational alternatives with the general aim to predict the performance of operational behavior, choose the most adequate equipment or eliminate the useless time. There is a long tradition in collecting the needed data in a traditional fashion, but this approach has its limitations, and it is likely that in the future the use of professional software would be extended is such preoccupations as this kind of tools have been already implemented. However, little to no information is available in what concerns the performance of data analyzing tasks when using purpose-built professional time studying software in such research preoccupations, while the resources needed to conduct time studies, including here the time may be quite intensive. Our study aimed to model the relations between the variation of time needed to analyze the video-recorded time study data and the variation of some measured independent variables for a complex organization of a work cycle. The results of our study indicate that the number of work elements which were separated within a work cycle as well as the delay-free cycle time and the software functionalities that were used during data analysis, significantly affected the time expenditure needed to analyze the data (α=0.01, p<0.01). Under the conditions of this study, where the average duration of a work cycle was of about 48 seconds and the number of separated work elements was of about 14, the speed that was used to replay the video files significantly affected the mean time expenditure which averaged about 273 seconds for half of the real speed and about 192 seconds for an analyzing speed that equaled the real speed. We argue that different study designs as well as the parameters used within the software are likely to produce different results, a fact that should trigger other studies based on variations of these parameters. However, the results of this study give an initial overview on the time resources needed in processing and analyzing the data, and may help researchers in allocating their resources.