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Harvester productivity over machine sizes and mean stem size, based on model v for final felling and model iv for thinning. FF = final felling, and TH = thinning. Mean values from Tables 1 and 2 have been used for variables other than mean stem size and machine size.

Harvester productivity over machine sizes and mean stem size, based on model v for final felling and model iv for thinning. FF = final felling, and TH = thinning. Mean values from Tables 1 and 2 have been used for variables other than mean stem size and machine size.

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Modern computerization facilitates data-gathering from forest machines, and offers new opportunities to develop models for predicting productivity in forest harvest operations. In this study, we analyze the productivity of cut-to-length harvesting and forwarding in thinning and final felling using a routinely recorded follow-up dataset. The data or...

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... contrast, productivity in final felling was posi- tively influenced by the total volume harvested and the harvested volume per hectare, use of an accumu- lating harvester head, the harvester head's mass and, surprisingly, expected daylight limitations. The machine size's effect varied depending on the mean stem size, with larger machines generally being more productive at all mean stem sizes (Figure 3). However, the difference was largest in the middle of the data range, with less difference for both small and larger stem sizes. ...
Context 2
... our models seem to indicate that forwarder productivity was fairly similar in the studied operations and the cited findings. Further noteworthy aspects are that we found that the difference in productivity between load capacities decreased slightly with extraction dis- tance, in both final felling and thinning (Figure 3). A rather stable relationship between load capacities is consistent with the findings of Kuitto et al. (1994), whereas Nurminen et al. (2006) found indications that the differences increase with distance. ...
Context 3
... is quite common practice, but had some unwanted effects on the models since logarithmic transformation forces the models through the origin of the graph. This is natu- rally unfortunate for forwarding models, since their productivity intrinsically ought to increase with decreasing extraction distance and not exhibit such behaviour shown in Figure 3, where models turn towards the origin for extraction distances less than 50-100 metres. Thus, the models are unreliable at such short distances. ...

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... Training can improve the skills of operators to perform certain activities. Training can take place in nature, in a logging machine, as well as the practical skills of operators can be developed with the help of a simulator [5]. However, simulators differ in environmental factors, which sometimes causes problems for the operator in making decisions. ...
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The study aims to determine the applicability of the StanForD 2010 standard in the analysis of the impact of CTL harvester operator training on productivity. The productivity of harvester operators is affected by factors such as tree species, diameter, type of felling, terrain, operator experience, qualifications, and several other factors. However, there are not many studies that, in addition to the above-mentioned factors, have analyzed the impact of periodic training of operators on productivity. The study uses automatically obtained harvester production data from logging service providers. Data are from the John Deere harvester for the period July 2021 to December 2022. The harvester worked in cleaning cutting in the territory of the South Kurzeme forest district. The study used data acquired by two operators who received refresher training during the study period. Harvester Operator No. 1, has work experience of 6 years, and Operator No. 2 has work experience 12 years. Data on the development of four different species of trees in the two months before and two months after the training were used to determine the impact of operator training on productivity. Operator productivity was analyzed in three diameter groups for all species and separately for each tree species. The study found that using the automatically obtained data Operator No. 1 and Operator No. 2 average productivity after training increased by 7% and 29%, respectively. However, the effect of different stem diameters and tree species on productivity changes has been found. Operator No. 1 showed a decrease in productivity when processing deciduous trees in separate diameter groups, while Operator No. 2 showed a decrease in labour productivity in some conifer diameter groups. An in-depth data analysis is needed to find out the reasons for the decline in productivity by expanding the data set used. Introduction In today's mechanized logging, harvester productivity is affected by several factors. Some of the influencing factors cannot be changed, such as tree species, DBH, type of felling, terrain, etc. There are several studies in this direction, where the influence of environmental factors is clarified [1; 2]. However, some factors are subject to change and are largely related to the behaviour of the operators, including psycho-emotional state, speed of reaction, speed of decision-making, and others [3]. One such variable is the training of the operator of the logging machine [4]. Periodic operator training plays a very important role in increasing productivity. Training can improve the skills of operators to perform certain activities. Training can take place in nature, in a logging machine, as well as the practical skills of operators can be developed with the help of a simulator [5]. However, simulators differ in environmental factors, which sometimes causes problems for the operator in making decisions. Training operators in nature is an expensive process because, firstly, the hourly cost of the logging machine itself is high and, secondly, a large part of the cost is fuel costs. Despite these costs, 8-16 hours of training is provided in Latvia, where the instructor follows the work of the operator in person and provides recommendations for more efficient work. Such a training model produces results, but to a large extent, the result depends on the professionalism of the instructor himself, from his ability to assess the situation and make recommendations. Better training results can be achieved by conducting a more detailed analysis of the operator's productivity using automatic harvester data before training. This method of data collection is relatively reliable and low cost [1; 6; 7]. This allows the instructor to focus on developing specific operator competencies during the training. In this study, harvester operator productivity is analyzed using data automatically stored in StanForD 2010 to determine the effect of training on productivity.
... Traversability is of major importance in forestry [9] where heavy vehicles, weighing up to 40 tons when fully loaded, traverse rough and sometimes weak terrain. In [10], digital soil maps were combined with discrete elevation maps to predict the traversability, quantified as driving resistance and divided into rolling, slope, and obstacle resistance. ...
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We present a method that uses high-resolution topography data of rough terrain, and ground vehicle simulation, to predict traversability. Traversability is expressed as three independent measures: the ability to traverse the terrain at a target speed, energy consumption, and acceleration. The measures are continuous and reflect different objectives for planning that go beyond binary classification. A deep neural network is trained to predict the traversability measures from the local heightmap and target speed. To produce training data, we use an articulated vehicle with wheeled bogie suspensions and procedurally generated terrains. We evaluate the model on laser-scanned forest terrains, previously unseen by the model. The model predicts traversability with an accuracy of 90%. Predictions rely on features from the high-dimensional terrain data that surpass local roughness and slope relative to the heading. Correlations show that the three traversability measures are complementary to each other. With an inference speed 3000 times faster than the ground truth simulation and trivially parallelizable, the model is well suited for traversability analysis and optimal path planning over large areas.
... Based on the type of machine used, the results are very in line with other studies conducted on thinning operations and not hard terrain conditions. In Proto et al. [12], average productivity of forwarding was equal to 19.3 m 3 PMH; Cadei et al. [68] show average productivity from 18.5 m 3 PMH15 to 29.4 m 3 PMH15 in three different sites; Eriksson et al. [69] and Hildt et al. [70] reach results in line with the mentioned studies, where the results highlight that productivity is higher for shorter forwarding distances and larger payloads. ...
Article
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Forest mechanisation plays an important role in increasing labour productivity and reducing production costs. This work aims at evaluating various logging scenarios in Calabrian pine high forests, considering technical, economic and environmental aspects. The cut-to-length system was adopted and structured as follows: felling and processing operations were carried out using a medium-sized chainsaw while extraction of the processed material was carried out using three different vehicles for timber extraction: (i) by cable skidder, (ii) by grapple skidder and (iii) by a forwarder. The methodology was based on productivity analysis and production cost analysis, while for environmental performance, the life cycle assessment (LCA) approach was adopted. The selected functional unit (FU) was referred to as 1 h of logging operations. However, to assess the resulting usefulness, further analyses were performed using an alternative FU consisting of 1 m3 of round wood. The study’s outcomes show the complexity in achieving an optimal balance between productivity, economic aspects and sustainable management in forest operations.
... Factors influencing forwarding productivity in CTL systems include: (I) operatorrelated parameters (i.e., skills and experience [11][12][13][14]), which are also related to preceding harvester work, such as pre-bunching and separation of assortments, the positioning of logs and also the concentration of logs along machine operating trails [1,9,15,16]; (II) stand and timber characteristics such as the stem volume [17] or the number of assortments [15,[18][19][20]; (III) terrain-related factors such as slope [21,22] or the extraction distance [16,19,[20][21][22][23]; (IV) technical parameters such as the loading capacity of the machines used or track support [16,20,23]; and (V) general organizational aspects [24], such as the harvested volume per area [19], and, in this context, the total harvesting volume [10] or restrictions related to forest management [25,26], e.g., silvicultural objectives [27]. Indirectly, even the frequency of maintenance influences productivity as it affects the duration of downtime [28]. ...
... Factors influencing forwarding productivity in CTL systems include: (I) operatorrelated parameters (i.e., skills and experience [11][12][13][14]), which are also related to preceding harvester work, such as pre-bunching and separation of assortments, the positioning of logs and also the concentration of logs along machine operating trails [1,9,15,16]; (II) stand and timber characteristics such as the stem volume [17] or the number of assortments [15,[18][19][20]; (III) terrain-related factors such as slope [21,22] or the extraction distance [16,19,[20][21][22][23]; (IV) technical parameters such as the loading capacity of the machines used or track support [16,20,23]; and (V) general organizational aspects [24], such as the harvested volume per area [19], and, in this context, the total harvesting volume [10] or restrictions related to forest management [25,26], e.g., silvicultural objectives [27]. Indirectly, even the frequency of maintenance influences productivity as it affects the duration of downtime [28]. ...
... Factors influencing forwarding productivity in CTL systems include: (I) operatorrelated parameters (i.e., skills and experience [11][12][13][14]), which are also related to preceding harvester work, such as pre-bunching and separation of assortments, the positioning of logs and also the concentration of logs along machine operating trails [1,9,15,16]; (II) stand and timber characteristics such as the stem volume [17] or the number of assortments [15,[18][19][20]; (III) terrain-related factors such as slope [21,22] or the extraction distance [16,19,[20][21][22][23]; (IV) technical parameters such as the loading capacity of the machines used or track support [16,20,23]; and (V) general organizational aspects [24], such as the harvested volume per area [19], and, in this context, the total harvesting volume [10] or restrictions related to forest management [25,26], e.g., silvicultural objectives [27]. Indirectly, even the frequency of maintenance influences productivity as it affects the duration of downtime [28]. ...
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Fully mechanized timber harvesting systems are well established in forest operations worldwide. In cut-to-length (CTL) systems, forwarders are used for extracting logs from the stand. The productivity of a forwarder is related to site-and stand-specific characteristics, technical parameters, organizational aspects, and the individual skills of the operator. The operator's performance during "loading" considerably affects forwarder productivity, since this element occupies nearly 50% of forwarding cycle time in CTL operations. When positioning the forwarder for loading, different loading angles and loading distances arise. Additionally, different log orientation angles in relation to the machine operating trail can be observed. Therefore, an in-depth analysis of loading conditions was conducted. The goal of this pilot case study was to explore the potential impact of different loading angles and distances, and log orientation angles, on time consumption per loading cycle in order to derive indications for more efficient work practices. Therefore, controlled loading sequences were tested on a physical Rottne-F10-based forwarder simulator with an experienced forest machine operator. Three loading angles (45°, 90° and 135° azimuthal to the machine axis) with five loading distances (3, 4, 5, 6 and 7 m), and three log orientation angles (45°, 90°, 135°), resulted in a total of 45 settings, which were tested in 10 repetitions each. The time required for a loading cycle was captured in a time study, applying the snap-back method. Results showed that all three tested variables had a significant influence on time consumption per loading cycle. Loading at an angle of 135°, and from a close (3 m) or far distance (7 m) led to especially increased cycle times. Loading from 4 to 6 m distance could be detected as an optimal loading range. Additionally, log orientation angles of 45° and 90° led to increased loading efficiency. Even if the validity of the results may be limited due to different conditions and influencing factors in field forwarding operations, these data can contribute to a better understanding of the loading element and, in particular, to productivity determining factors of forwarder work.
... In particular, the proposed model relies heavily on just two studies of yarders, one of excavator-based yarding on slopes less than 50% in Malaysia [51] and one of an older swing yarder operating in young Douglas-fir on slopes less than 60% [5]. In addition, the diversity of results for harvesters [26,28,[31][32][33][34]36,38] suggests no single model is likely to accurately represent all machines. Notably, increasing selection of tracked harvesters at later stand ages results from assuming they are fitted with larger capacity processing heads than eight-wheel harvesters (Table S6). ...
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Background: Tethered cut-to-length and cable yarding systems with tethered falling equipment are increasingly used to harvest trees from slopes exceeding 30–60% more safely and at reduced financial cost than less mechanized harvest systems. Existing studies of harvest equipment typically isolate one or two pieces of equipment in a harvest system and often occur on sites with slopes below 50% and trees less than 60 cm in diameter. Methods: We analyzed machine capabilities and productivity regressions to extrapolate existing models to steep slope harvesting of trees up to 115 cm diameter. The resulting individual machine models are integrated into models of cut-to-length and long-log harvest system productivity. We estimated the financial operating costs of the harvest systems considered from equipment pricing and operator wages. Results: Analysis of even-age Douglas-fir (Pseudotsuga menziesii) and western hemlock (Tsuga heterophylla) rotations suggests eight-wheel forwarder productivity, swing yarder productivity, and mechanization of manual chainsaw labor with tethered harvesters as primary controls on harvest costs. Conclusions: The proposed model enables predictions across a greater range of slopes and tree sizes than those previously modeled, creating a foundation for future research into the cost and productivity of steep slope harvesting systems.
... Results related to the determination of the volume conversion factor may contain valuable additional information that could be used for scale applications, i.e. in combination with productivity monitoring (Gullberg 1997, Jiroušek et al. 2007, Eriksson and Lindroos 2014, Manner 2015. The observed average green density of 906 kg m -3 (SEM=13.63 kg m -3 , n=77) showed a good normal distribution for all observations. ...
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When investigating the forwarding process within the timber supply chain, insufficient data often inhibits long-term studies or make real-time optimisation of the logistics process difficult. Information sources to compensate for this lack of data either depend on other processing steps or they need additional, costly hardware, such as conventional crane scales. An innovative weight-detection concept using information provided by a commonly available hydraulic pressure sensor may make the introduction of a low-cost weight information system possible. In this system, load weight is estimated by an artificial neural network (ANN) based on machine data such as the hydraulic pressure of the inner boom cylinder and the grapple position. In our study, this type of crane scale was set up and tested under real working conditions, implemented as a cloud application. The weight scale ANN algorithm was therefore modified for robustness and executed on data collected with a commonly available telematics module. To evaluate the system, also with regard to larger sample sizes, both direct weight-reference measurements and additional volume-reference measurements were made. For the second, locally valid weight-volume conversion factors for mainly Norway spruce (Picea abies, 906 kg m-3, standard error of means (SEM) of 13.6 kg m-3), including mean density change over the observation time (–0.16% per day), were determined and used as supportive weight-to-volume conversion factor. Although the accuracy of the weight scale was lower than in previous laboratory tests, the system showed acceptable error behaviour for different observation purposes. The twice-observed SEM of 1.5% for the single loading movements (n=95, root-mean-square error (RMSE) of 15.3% for direct weight reference; n=440, RMSE=33.2% for volume reference) enables long-term observations considering the average value, but the high RMSE reveals problems with regard to the single value information...
... These machines can also feed the virtual forest with additional data collected by the onboard sensors. At present, the parameters measured are the diameters and length of the logs produced by the harvester: values needed to calculate the felled and processed volume (Eriksson and Lindroos 2014). These are generally used for invoicing timber produced and delivered but can also provide a detailed insight of the quantity of roundwood produced in the harvested plot (Rossit et al. 2019) and draw a balance between net annual increment and harvesting of a given forested area. ...
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Climate-smart forestry can be regarded as the evolution of traditional silviculture. As such, it must rely on smart harvesting equipment and techniques for a reliable and effective application. The introduction of sensors and digital information technologies in forest inventories, operation planning, and work execution enables the achievement of the desired results and provides a range of additional opportunities and data. The latter may help to better understand the results of management options on forest health, timber quality, and many other applications. The introduction of intelligent forest machines may multiply the beneficial effect of digital data gathered for forest monitoring and management, resulting in forest harvesting operations being more sustainable in terms of costs and environment. The interaction can be pushed even further by including the timber processing industry, which assesses physical and chemical characteristics of wood with sensors to optimize the transformation process. With the support of an item-level traceability system, the same data could provide a formidable contribution to CSF. The “memory” of wood could support scientists to understand the response of trees to climate-induced stresses and to design accordingly an adaptive silviculture, contributing to forest resilience in the face of future changes due to human-induced climate alteration.
... The figure initially presented for the Malwa harvesting productivity of 10.84 m 3 ·PMH −1 , is consistent with work by Eriksson and Lindroos (2014). Other comparable data in thinnings with similar tree sizes and tree from are rare, but a study on second thinnings in a pine sawtimber operation by Dembure et al. 2019) study superior tree size and form uniformity is assumed when compared to the trees harvested in this study, hence achieving a higher productivity, r as this was not specified in their study. ...
... Small-scaled harvesting systems, particularly forwarders, are not studied as extensively as larger machines, mainly due to many studies being on final felling operations (Labelle et al. 2016; Williams and Ackerman 2016;Brewer et al. 2018;Proto et al. 2018b;Gagliardi et al. 2020). As a result the Malwa's productivity in this study was lower than previous forwarder studies (Eriksson and Lindroos 2014;Lazdiņš et al. 2016;Williams and Ackerman 2016) mainly due to those machines being of larger capacity than the Malwa. The Malwa, however, is limited by its design capacity in terms of load capacity and potentially speed of loading, when forwarding. ...
... Past studies have discussed fiber loss, waste, and stand damage (Han et al. 2000;Hall and Han 2006;Dembure et al. 2019), though not in relation to whole system operations opting instead to only focus on one component. Others (Eriksson and Lindroos 2014;Hiesl and Benjamin 2015;Laitila et al. 2016;Lazdiņš et al. 2016;Williams and Ackerman 2016) do not consider fiber loss in their methodology and outcomes. This highlights a potential shortcoming in studies where machine productivity is calculated using standing volume which exceeds the actual merchantable volume harvested and extracted.. Furthermore, the predictive power of those models would represent just harvester productivity or just forwarder productivity, as there would be no way to confirm that the volume being utilized is representative of the whole system. ...
Article
An assessment of the feasibility of using a Malwa 560C combination harvester/forwarder in a selection type first thinning operation in the Highveld region of South Africa was conducted. Each of the machine components, harvesting and forwarding, were assessed separately with resulting machine productivities of 10.84 m³·PMH⁻¹ and 5.03 m³·PMH⁻¹ respectively. An interesting result was found indicating a discrepancy between standing volume, volume harvested and volume reaching roadside. These differences were related to felling and processing trees with no merchantable volume (i.e. felling to waste), other inevitable fiber losses during log assortment production and log assortments not forwarded to roadside. To determine system productivity and cost, calculations must be based on volume reaching roadside otherwise harvester actual productivity is inordinately inflated. Recalculating harvester productivity with volume to roadside productivity is reduced to 6.94 m³·PMH⁻¹. The two functions of the machine cannot be viewed as independent activities. The machine productivity is limited by the least productive component, in this case, forwarding. The system performance productivity was found to be 2.92 m³·PMH⁻¹. A system cost based on this productivity was USD 46.78·PMH⁻¹ and USD 16.05·m⁻³. This study highlights the importance of accounting for fiber loss in harvester productivity calculations as well as balancing the system’s productivity to avoid overestimations and incorrect assumption in the supply chain. Furthermore, modeling productivity with data representing the adjusted volume demonstrates what is effectively being produced by the whole cut-to-length harvesting system, considering the effect of tree size and quality variability present on these sites.
... m 3 tree sizes. Variability in tree size is especially accentuated in seminatural managed forests where regeneration and ingrowth of other species commonly occurs, such as the boreal forests of Fennoscandia (Eriksson and Lindroos 2014). Planted forests in contrast, offer opportunities for rationalization and uniformity that are not available to more natural forest management regimes, and these make up a significant and increasing share of the market for CTL technology. ...
... It is well known that tree volume-dependent productivity curves for different machine sizes level out with increasing tree size, albeit at differing thresholds. At the same time, differences in productivity between machines of different sizes in smaller dimensioned timber are more marginal than they are in larger dimensions (Eriksson and Lindroos 2014). Further, in countries applying CTL technology in larger plantation forest industries, the operator wage typically constitutes a smaller proportion, and depreciation a larger proportion, of the overall machine cost than it would in developed economies (Dembure et al. 2019;McEwan et al. 2020). ...
... Harvester purchase price and depreciation is closely correlated with harvester size (Spinelli et al. 2011). Eriksson and Lindroos (2014) categorized the harvesters in their study into six size classes (S, M, L, XL, XXL, XXXL). They developed a set of productivity functions for each harvester size class and show how these machines are generally deployed in trees of differing mean size but that their productivity rates include a considerable overlap. ...
Article
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Long-term machine-derived data sets comprising 140,000 trees were collected from four harvesters of equal age and similar working conditions, into two machine size classes, viz. two Ponsse Bears and two smaller Ponsse Beavers. Productivity functions for each size class were modelled using a nonlinear mixed effects approach. Based on these functions, unit costs and their sensitivity to utilization rates and cost of capital were assessed. Results showed that despite considerably higher capital costs (32%) on the Bear, a 50% higher mean productivity resulted in a unit cost only 17% higher than the Beaver in a disadvantageous scenario (high interest rates and low utilisation), and a 6% lower unit cost than the Beaver in an advantageous scenario (low interest and high utilisation), within the range of tree sizes observed. Between these extremes, only marginal differences in unit costs were observed. This demonstrates that the difference in ownership and operating costs between larger and smaller harvesters is largely negated by the difference in productivity rates. These results can provide useful insight into timber harvester investment decisions. Harvesters from two adjacent size classes can be used interchangeably at the same unit cost within a wide range of tree sizes despite productivity differences. It should be noted that increased repair costs and an eventual reduction in expected economic lifetime on a smaller harvester, or the negative effects of using a larger harvester in smaller trees, e.g. thinning operations, were not taken into account in this work.
... Machine relocations in roundwood harvesting have been included in system analysis modeling in studies of, e.g., [12,23,25,26]. In the Nordic countries, extensive productivity studies for CTL harvesters and forwarders in the current millennium have been conducted by Nurminen et al. [27] and Eriksson and Lindroos [28], and separately for harvesters by Liski et al. [29]. Follow-up studies of machine relocations using a low-bed truck have been reported by Kärhä et al. [8] and Kauppinen [10]. ...
... A calibration of the harvester's productivity function was carried out in the recent larger harvester follow-up study by Jylhä et al. [40] to update the formulas for today's purposes. The same fused forwarder productivity model was used for regeneration cuttings and thinnings, which resulted in slightly higher values in thinnings compared to the studies of, e.g., [28,27]. Productivity functions of forwarders need to be updated for future studies with the use of the system simulator. ...
... In this study, machine utilization rates (MU%) for the harvesters and forwarders were roughly 4-6% higher than in the previous studies by Kärhä et al. [11], Eriksson and Lindroos [28], and Jylhä [40]. For harvesters, MU% ranged between 78 and 81 [28,40], and for forwarders MU% was 84 [28]. ...
Article
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Upscaling an operation typically results in economies of scale, i.e., cost advantages in business, especially when the production unit’s utilization rate can be improved. According to economic studies of mechanized timber harvesting, large wood harvesting entrepreneurs tend to be more successful in business than small entrepreneurs. What are the factors that influence harvesting costs, and how great is their effect on costs? These questions were investigated in mechanized cut-to-length timber harvesting in Eastern Finland by varying (a) the size of the harvesting fleet, (b) the harvesting site reserve, and (c) the timing and duration of the working day of machine relocations, in the case of an entrepreneur using a discrete-event simulation method. Prior to the simulations, harvesting site data were generated from the National Forest Inventory data by the MELA software, and the spatial data analyses by ArcGIS. According to the results, largely because of the low utilization rate of the contractor’s own relocation truck, the harvesting cost of a 2-harvesting-unit (2 HU) scenario was 9% or 6% higher than 4 HU, and 13% or 8% higher than 8 HU, with or without a specifically employed driver of a relocation truck, respectively (the harvesting unit consists of a harvester and a forwarder). In the 4 and 8 HU scenarios, harvesting costs decreased on average by 1% (0.3–1.5), when doubling the size of the harvesting site reserve. With fleet sizes of 6 and 8 HU, good utilization of a relocation truck reduced relocation costs, whereas machine costs only increased a small amount because of a longer machine relocation waiting time than with smaller entrepreneurs. The study raised the importance of entrepreneur-specific planning of machine relocations in the cost-efficient timber harvesting in Finland.