The progression of forest operations technology and innovation

Abstract

Globally, from the time of early mechanisation through to the early 2000s, trends in forest operations and supply-chain research, development and engineering (RD&E) centred around improving mechanical performance. Projects improved understanding of what affected machine performance and productivity and then developed, tested and deployed improved technologies or work methods to increase machine productivity. More recently, multiple criteria decision-making (MCDM) techniques were introduced to operations research to include environmental and social factors with the aim of improving harvesting system selections (Blagojević et al. 2019). RD&E included the development of onboard systems technology that both helped better measure and manage operations, and further developments in modelling and analytics. An ongoing meta-study driven by the Swedish forest industry demonstrated real and predicted (post-1990s) productivity gains through this approach (Fig. 1). There was a significant change in the trend from the mid2000s, with an actual fall in productivity identified (Fig. 2). Effectively, new advances in technology in that timeframe were more focused on value and were prepared to compromise productivity and costs in the interests of higher-value product realisation and increased volume recovery. Although the performance of comparable mechanised Australian plantation operations is equivalent to the described Scandinavian operations, there tends to be greater variability across Australian operations and a larger gap between the best and poorest. When first discovered, it was a point of concern that the improvements that had been so important for keeping forest supply chains economically competitive had stopped and possibly even regressed. A closer review reveals a shift in the focus of the RD&E effort to not only look at cost reductions, as delivered through higher operational productivity, but also to take a broader view of value—getting higher-value product out-turn at a reasonable cost. Although machine performance and productivity remained important, increased RD&E effort was now being applied to the production ofmore valuable products. New harvesting technologies were being developed tomeasure trees in real time and support optimal—or near-optimal—product segregations (Marshall & Murphy 2004). Later, this focus on value further evolved to increase the total volume recovered by minimising wastage and expanding the types and range of products recovered, like biomass for energy (Ghaffariyan et al. 2017). Technology was developed tomake products from stems that in the past were too small or for which the form was too poor to be considered economically viable. Developments like multi-stem processing heads helped increase the overall return on investment (ROI) per area of forest and brought more forest areas into consideration for production (Gingras 2004). Most recently, RD&E efforts have created technologies to extend the scope of mechanical operations to more difficult terrain and steep slopes; although not delivering the same gross productivity that similar mechanised operations can achieve on flatter terrain, these technologies have proved relative gains to the motor manual alternatives on steep slopes and improved ROI. With the emergence of new forest products, particularly biomass for heat energy, the potential to influence and improve wood quality through forest operations and supply-chain management became an important area of RD&E, with continuing efforts to control and manage features like moisture content to improve resource value and reduce logistics costs (Strandgard & Mitchell 2017). A number of studies have identified operator performance as a key component in determining forest machine productivity (Purfürst & Erler 2011) as well as log volume and value recovery. Previously, determining the underlying causes of operator differences typically required careful observations of each operator. Although detailed manual observations are still required in some circumstances, recent developments in onboard computer technology have enabled highly detailed observations of operator actions that can be used to train new or poorer-performing operators. Also, there is potential for advanced sensors onboard harvesting equipment to detect tree-quality features in real time (Miettinen et al. 2010). A key RD&E challenge is to improve the capacity of logging contractors and forest managers to use data captured by onboard computers and harvesting heads. The health and well-being of machine operators and forestry workers can have significant impacts on work quality and efficiency. Studies have also linked operator performance to occupational health and safety conditions, work schedules and machine ergonomics (Murphy et al. 2014). Work accidents affect labour costs due to the absences required for medical recovery. Potočnik et al. (2017), who studied accidents in forest harvesting operations in Slovenia in the period 1990–2005, reported a total of 846 accidents in all forest operations. Of these, 68% occurred during tree cutting, 24% during skidding and 8% during tending operations. Other researchers have indicated that tree felling and wood extraction cause a larger number of accidents than loading and transportation. The main root causes of accidents in different types of activity are personal errors such as lack of personal protective equipment, operator error and the application of poor techniques. Personal errors may be addressed with improved work-safety training and follow-up. There is a need for better enforcement of accident reporting and for workers to recognise that there is value in the improvements implemented. In addition to RD&E into technologies in forest operations and supply chains, considerable efforts have been expended on the creation and improvement of forestry-specific frameworks for descriptive and predictive analytics. In many cases, these tools have been developed to consolidate the knowledge gained from operational trials so that companies can combine multiple finite results (same supply-chain function) to model harvesting systems or supply chains. These frameworks can then be deployed to integrate knowledge from RD&E into business and management systems and decisions. AUSTRALIAN FORESTRY 2020, VOL. 83, NO. 1, 1–3 https://doi.org/10.1080/00049158.2020.1723044