ForestValue - GreenLane – fast tracking value and resilience in industrial wood supply

Forest industries depend on a stable year-round supply of even log quality. GreenLane is a NIBIO-coordinated Era-Net cooperation between Norway, Swede and Austria which focuses on roundwood value-tracking in the supply chain, in order to develop effective managerial responses for improved mill customer value in the face of challenging climate scenarios. The overall goal of the project was to develop virtual supply chain laboratory environments enabling value-tracking and interactive testing of harvesting and transport responses to challenging climate scenarios. The study compares three European case studies; continental-montane, sub-arctic and oceanic conditions where climate challenges vary between regions. In the continental-montane region the primary concern is a high frequency of windstorm damage and resulting need for salvage harvesting. In the Nordic countries higher temperatures require a re-scheduling of earlier winter-harvesting sites to dry summer periods. At the same time, the higher precipitation during the summer requires greater operational agility to exploit weather patterns to maintain even production and transport pace. For the Norwegian case study, the main focus is on developing the capacity for operation agility towards even year-round wood flows with tighter management of lead time limits during critical seasons. The main results from the project are three-fold. The first project result is a common framework for value-tracking, driven by weekly weather data (IBM). This framework is based on national timber grading guidelines from the three project regions and integrates risks for insect infestation (I), blue stain occurrence (B) and moisture content losses (M). The second is a weather-driven framework for forest road bearing capacity (RBC) and seasonal availability. This is based on time-series analysis of transport pace with corresponding weather data connected to site/road characteristics. The main result is the development of the virtual laboratory environment (V-Lab). For Norwegian conditions, seasonal availability is a primary driver for extended lead-times. Weather-based accessibility scenarios were implemented in the V-Lab training environment for weekly coordination of purchase, production and transport between participants. Participants collaborate in selecting stands for purchase, allocating these to harvesting teams for production, before allocating flows to fulfil mill demand within the restrictions of truck capacity and lead-time limits. The RBC availability model was developed specifically for this region and was implemented in the model to quantify weekly availability for harvesting and transport within three climate zones (based on distance from the coast). The training sessions are run as team competitions for maximum net forest value, based on bonus points for high delivery fulfilment and penalties for value loss. The V-Lab facilitates team collaboration at the operational level. Based on the given weather scenario, purchase, production and transport functions coordinate weekly decisions to meet monthly goals for production and transport within their respective restrictions. The new V-Lab delivers operational realism beyond earlier training environments, complete with an empirical benchmarking of team performance. With the research host test, the V-Lab provided an opportunity to examine aspects of operational coordination which not handled in current work processes. Participants identified bottlenecks in work communication, simple improvements as well as more fundamental data needs for improved control of lead times and increased mill customer value. This work continues in a follow-up project with the development of a more general configuration which can be adapted to other geographies. Both the IBM- and RBC-frameworks are driven by currently available weather data. The sector-wide solutions provided by SkogData in Norway and Biometria in Sweden provide the digital architectures necessary to support further development of more advanced functionalities. Their broad implementation of production and transport messaging is unique in Europe and provides a competitive advantage when developing new solutions at the supply-chain level.

The Oceanic V-Lab training environment tracks supply chain KPIs for team decisions in purchase, production and transport for alternative training scenarios (4 seasonal weather scenarios). The value-related KPIs include monthly delivery precision and proportion of volumes delivered within lead-time limits, and corresponding value loss. The operational KPIs include relocation distances for harvesting teams and average transport distance and costs (see GreenLane D4). Scores from 5 NMBU test-teams provide benchmarking for performance (see GreenLane D5). A high net value required both high delivery precision and low log value loss underway. High scores for delivery precision were enabled through first through sufficient production/stocks followed by precise management of available truck capacity to reach delivery plans. Low value losses were enabled by adjusting production in relation to transport in order to keep road-side stocks and lead times within the stipulated lead times.

GreenLane focuses on assortment-specific value-tracking in order to develop managerial responses giving improved mill customer value in the face of challenging climate scenarios. GreenLane builds on the earlier results from MultiStrat to provides a virtual supply chain laboratory which simulates the wood supply chain from harvesting to industry including key performance indicators (KPIs) for both competitive advantage and environmental loading. The value tracking follows logs from harvesting to mill delivery with wood quality deterioration according to storage conditions (weather) and duration, economic evaluation of the actual wood value of the logs at each position in the supply chain. This provides a platform for testing of managerial response of varying configurations in the supply chain in order to formulate best-practice guidelines. The geographical constellation consists of three test cases in three climatic zones of Europe (continental, sub-arctic and oceanic) where each test case is run by the respective research partner (NIBIO, Skogforsk, BOKU) and their respective host forest companies (Allskog, Norra Skogsägarna, ÖBF). The initial work packages (WP1-WP3) guide the development of model functionality. WP4 is focused on implementation of the functionalities and sub-models in the regional test cases and analysis of managerial responses to their selected climate scenarios, supply- and demand disturbances. WP5 (dissemination) makes the results available to the various stakeholder groups enabling implementation in managerial processes. This is achieved by hands-on workshops with wood supply managers of the involved industry partners, providing the opportunity to test and fine-tune innovations. WP6 (management) provides a structure for the initial coordination between industry priorities and research goals (industrial steering committee) as well as later monitoring of work progress and coordination between work packages.