volume: 32, issue: 2
volume: 32, issue: 2
volume: 31, issue: 1
volume: 36, issue: 2
volume: 36, issue: 2
volume: 38, issue: 2
volume: 39, issue: 2
The performance of a harvesting crew in terms of its ability to transform inputs into outputs
is influenced by discretionary factors within the unit’s control, such as the selection of machines
and operators. However, factors associated with the operating environment, such as
terrain slope and tree size that are outside the direct control of management, can also influence
harvesting system efficiency. Using data on forest harvesting operations in New Zealand, this
paper applies an established four-stage Data Envelopment Analysis (DEA) procedure to estimate
the managerial efficiency of independent forest harvesting contractors, while taking into
account the influence of the operating environment. The performance of 67 harvesting contractors
is evaluated using seven inputs, one output (system productivity) and three operating
environment factors in an input-oriented, variable return to scale DEA. The results show that
the operating environment including terrain slope, log sorts and piece size influence the efficient
use of inputs by harvesting contractors. A significant difference is observed between the
mean managerial efficiency of the crews before and after controlling for the influence of the
operating environment, the latter being higher by 11%. This study provides evidence that
without accounting for the influence of the operating environment, the resulting DEA efficiency
estimates will be biased; the performance of crews in favourable operating environment
would be overestimated and those in unfavourable environment underestimated.
volume: 42, issue:
Technology development, in terms of both capability and cost-effective integration, is moving at a fast pace. While advanced robotic systems are already commonplace in controlled workspaces such as factories, the use of remote controlled or autonomous machines in more complex environments, such as for forest operations, is in its infancy. There is little doubt autonomous machinery will play an important role in forest operations in the future. Many machine functions already have the support of automation, and the implementation of remote control of the machine where an operator can operate a piece of equipment, typically in clear line-of sight, at least is commonly available. Teleoperation is where the operator works from a virtual environment with live video and audio feedback from the machine. Since teleoperation provides a similar operator experience to working in the machine, it is relatively easy for an operator to use teleoperation. Autonomous systems are defined by being able to perform certain functions without direct control of a human operator. This paper presents opportunities for remote control, teleoperated machines in forest operations and presents examples of existing developments and ideas from both forestry and other industries. It identified the extraction phase of harvesting as the most logical placement of autonomous machines in the near-term. The authors recognise that, as with all emerging technologies and sectors, there is ample scope for differences in opinions as to what will be commercially successful in the future.
volume: 42, issue:
Cable yarders are often the preferred harvesting system when extracting trees on steep terrain. While the practice of cable logging is well established, productivity is dependent on many stand and terrain variables. Being able to continuously monitor a cable yarder operation would provide the opportunity not only to manage and improve the system, but also to study the effect on operations in different conditions.
This paper presents the results of an automated monitoring system that was developed and tested on a series of cable yarder operations. The system is based on the installation of a Geographical Navigation Satellite System (GNSS) onto the carriage, coupled with a data-logging unit and a data analysis program. The analysis program includes a set of algorithms able to transform the raw carriage movement data into detailed timing elements. Outputs include basic aspects such average extraction distance, average inhaul and outhaul carriage speed, but is also able to distinguish number of cycles, cycle time, as well as break the cycles into its distinct elements of outhaul, hook, inhaul and unhook.
The system was tested in eight locations; four in thinning operations in Italy and four clear-cut operations in New Zealand, using three different rigging configuration of motorized slack-pulling, motorized grapple and North Bend. At all locations, a manual time and motion study was completed for comparison to the data produced by the newly developed automated system. Results showed that the system was able to identify 98% of the 369 cycles measured. The 8 cycles not detected were directly attributed to the loss of GNSS signal at two Italian sites with tree cover. For the remaining 361 cycles, the difference in gross cycle time was less than 1% and the overall accuracy for the separate elements of the cycle was less than 3% when considered at the rigging system level. The study showed that the data analyses system developed can readily convert GNSS data of the carriage movement into information useful for monitoring and studying cable yarding operations.
volume: 42, issue:
Fires in forest machines are typically catastrophic in terms of machine destruction and can develop rapidly to be a risk to the machine operator. They are an issue worldwide and there can be larger consequences such as starting a major forest fire. This paper describes trends in machine fire occurrences in the New Zealand forest harvesting sector. A total of 224 machinery fire incidents were recorded over an 8 year period from 2007 to 2014. Trends in forest machinery fires in the sector were identified and summarized. Late morning (10 am-noon) and mid-afternoon (2–4 pm) showed the highest incidence of machine fire, corresponding to periods with the highest level of work. Excluding the main holiday months, there was a correlation of machine fires to average monthly temperature. Summary statistics on causes of fire ignition showed that 40% were attributed to electrical and hydraulic faults; however, some remain unidentified as the fires commenced after work was completed. A short survey of industry managers was carried out to ascertain machine fire perceptions. 67% agreed that machine fire was an issue, and only 33% thought the current industry procedures were sufficient to mitigate them. The report concludes with proactive measures to reduce the incidence of forest machine fire risk.
volume: 42, issue:
Cable yarding is a well establish technology for the extraction of timber in steep terrain. However, it is encumbered with relatively low productivity and high costs, and as such this technology needs to adapt and progress to remain viable. The development of biomass as a valuable byproduct, and the availability of processors to support yarder operations, lend themselves to increasing the level of whole-tree extraction. Double-hitch carriages have been developed to allow for full suspension of whole-tree and tree-length material. This study compared a standard single-hitch to a double-hitch carriage under controlled conditions, namely in the same location using the same yarder with downhill extraction. As expected, the double-hitch carriage took longer to load up (+14%), but was able to achieve similar productivity (10–11 m3 per productive machine hour) through increased inhaul speed (+15%). The importance of this study is that it demonstrates both the physical and economic feasibility of moving to whole-tree extraction using the double-hitch type carriage for longer corridors, for settings with limited deflection, or areas with lower tolerance for soil disturbance.
volume: 44, issue:
The demand for increased efficiency in timber harvesting has traditionally been met by continuous technical improvements in machines and an increase in mechanisation. The use of active and passive sensors on machines enables improvements in aspects such as operational efficiency, fuel consumption and worker safety. Timber harvesting machine manufacturers have used these technologies to improve the maintenance and control of their machines, to select and optimise harvesting techniques and fuel consumption. To a more limited extent, it has also been used to evaluate the time taken to complete tasks. The systematic use of machine sensor data, in a central database or cloud solution is a more recent trend.
Machine data is recorded over long periods of time and at high resolution. This data therefore has considerable potential for scientific investigations. For mechanised timber harvesting operations, this could include a better understanding of the interaction between productivity and operational parameters, which first of all requires an efficient determination of cycle time.
This study was the first to automatically delimitate tower yarder cycle times from machine sensor data. In addition to machine sensor data, cycle times were collected through a traditional manual time and motion study, and cycle times from both studies were compared to a reference cycle time determined from video footage of the yarder in operation.
Based on three days of detailed time study, the total cycle time in the classic manual time (–1.3%) and in the machine sensor data (–1.2%) was only slightly shorter than in the reference study, and the average cycle time did not differ significantly (classic manual time study: –0.08±0.94 min, p=0.997; machine sensor data study: –0.08±0.26 min, p=0.997). However, the accuracy of the machine sensor approach (RMSE=0.92) was more than three times higher than that of the classic manual time study (RMSE=0.27).
With the integration of sensors on forestry machines now being commonplace, this study shows that machine sensor data can be reliably interpreted for time study purposes such as machine or system optimisation. This eliminates the need for manual time study, which can be both cumbersome and dependent on the experience of the observer, and allows long term data sets to be obtained and analysed with comparatively little effort. However, a truly automated time study needs to be supplemented with automated determination of and linkage to other operational parameters, such as yarding and lateral yarding distance or load volume.