Digitization in wood supply – A review on how Industry 4.0 will change the forest value chain

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Computers and Electronics in Agriculture journal homepage: www.elsevier.com/locate/compag

Review

Digitization in wood supply – A review on how Industry 4.0 will change the forest value chain

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Fabian Müllera, , Dirk Jaegerb, Marc Hanewinkela a

Chair of Forestry Economics and Forest Planning, Faculty of Environment and Natural Resources, University of Freiburg, Tennenbacherstr. 4, 79106 Freiburg, Germany Head of Department of Forest Work Science and Engineering, Faculty of Forest Sciences and Forest Ecology, University of Goettingen, Buesgenweg 4, 37077 Goettingen, Germany

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A R T I C LE I N FO

A B S T R A C T

Keywords: Industry 4.0 Virtual forest Digital forest Wood supply Timber

The term Industry 4.0 (I 4.0) has been shaping the discussion about increasing digitization in industrial and service value chains for several years. The concepts and technologies driving the fourth industrial revolution are increasingly moving to focus on forestry’s practice and research. They both are attempting to develop new solutions for the forestry sector by taking advantage of technological spillovers from other industry sectors. Based on an extensive systematic literature review, this article identifies general trends towards a smart wood supply chain and concrete I 4.0 application examples, which are already in practical use or still in the stage of research and development. On a process level, the I 4.0 application examples are described and discussed in detail, ranging from computerized decision support aids to electronic control, machine vision and post-harvest management. A process flow chart visualizes selected findings of the review, showing that the value of I 4.0 mainly lies in the interconnection of process steps along the value chain, with close to unlimited information flow and allocation in an internet of trees and services. This can lead to significant changes and value-adds in harvest planning, harvest organization and control, operations, transport and logistics as well as timber sales. Furthermore, we discuss the latest developments of simulation modelling based on remote sensing data in forestry, which turns out to be the basis for the concept of a virtual forest as digital copy of the reality. With respect to future research, we argue that the benefits of data generation and information flows across organizational borders within an internet of trees and services should be the interest of short and medium-term research. Moreover, we outline that research should not only work on overcoming the technical challenges of I 4.0 in wood supply such as robustness, reliability and accuracy. Socio-economic challenges such as willingness for cooperation, changes in work environments, labor qualification, data autonomy and added value distribution should also be discussed and analyzed. To conclude, with this review we contribute to a scientific discussion about the opportunities of I 4.0 and digitization in wood supply and give an extensive overview of technological developments, applications and challenges that wood supply will face in the future.

1. Introduction Economy and society are facing significant changes through increasing digitization in recent years (Brettel et al., 2014). Technological progresses in hard- and software increase the performance of data retention, processing and transmission, which are driving this development forward (Bartodziej, 2017). In the context of industries and services the term Industry 4.0 (I 4.0) shapes the discussion around the development of increasing digitization, which aims to improve flexibility, productivity and customer orientation (Roth, 2016). The tremendous rise of I 4.0 leads to the discussion of how forestry

can benefit from this development. Several publications are discussing the potential of I 4.0 for the forestry sector and give examples for practical applications (Bayne et al., 2017; Bombosch, 2017; Fitzgerald, 2016). Trade fares as well as conferences of the last three years had I 4.0 on their agenda (Elmia AB, 2017; German Center for Forest Work and Technology, 2017). Scientific research is aiming to develop new solutions for the forestry sector by taking advantage of technological spillovers from other sectors. Although the economic potential of I 4.0 in agriculture and forestry has an estimated increase in added gross value of 15%, which is less than the potential in other sectors, new digital solutions offer the chance to better manage the manifold

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Corresponding author. E-mail addresses: [email protected] (F. Müller), [email protected] (D. Jaeger), [email protected] (M. Hanewinkel). https://doi.org/10.1016/j.compag.2019.04.002 Received 13 April 2018; Received in revised form 13 January 2019; Accepted 2 April 2019 0168-1699/ © 2019 Elsevier B.V. All rights reserved.

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challenges of the present and future (Bauer et al., 2014). The outlined high relevance of this topic makes it necessary to clarify the term I 4.0 in general and in the context of forestry. Based on this clarification, this article consolidates existing research, as well as examples in the practical use of I 4.0, which might change the forest value chain in the future. Therefore, this article seeks to answer the following research questions:

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- What are general trends in research and practice towards I 4.0 in the wood supply? - What are concrete I 4.0 application examples in the wood supply chain on the process level? We focus this literature review on the processes of wood supply, meaning all processes necessary to generate revenues from timber sales starting with harvest planning and ending with timber transport and sales. As the supply of wood is organized differently on a global scale (Uusitalo, 2010) and a literature review of this length would not be able to cover all possible variations of wood supply processes, we limit our perspective and understanding of timber supply to Central Europe. Nevertheless, we also seek to link our findings to other regions and broaden our perspective where necessary to the global scale. Furthermore, we will not limit our literature search to a geographic scope. This article is structured as follows. First of all, we define our understanding of timber supply as procedural framework of this article. Subsequently, the understanding of the origin and the idea of I 4.0, as well as the general I 4.0 concepts and technologies, are outlined and discussed. The literature review methodology is introduced in the third part of the article. The results of the literature review are outlined in a descriptive as well as thematic analysis, covering both quantitative as well as qualitative results. Finally, findings from the literature are discussed and conclusions are drawn with respect to the research questions.

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2.2. The idea of Industry 4.0 The term I 4.0 was shaped in Germany by representatives of politics, business and academia in 2011 as a vision of the fusion of the physical and digital world, mainly in industry and services but also in society (Bartodziej, 2017; Bauernhansl et al., 2014). With I 4.0, the fourth industrial revolution is predicted a-priori and not observed a-posteriori for the first time (Hermann et al., 2016). I 4.0 describes a vision on how industry and society may look like in the future before the revolution itself has taken place. This is one reason why there is no unanimously adopted definition of I 4.0 (cf. Table 1). Therefore, it is appropriate to derive the definition of I 4.0 from the main concepts and key technologies shaping the fourth industrial revolution in future.

2. Definitions 2.1. The wood supply chain The wood supply chain describes the process steps starting with harvest planning and ending with logistics and sales. We define the following five main process steps as part of the wood supply chain:

• Harvest planning: The objective of the technical planning is to

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organizational tasks around the time of the actual harvest, including instruction of the harvesting operators, quality control during the harvest, problem solving and ad hoc support as well as the documentation of execution. Harvest operations: For cut-to-length timber harvesting, operations in the stand can be divided into two main sub-processes, which are generally carried out by different machinery: felling and processing as well as extraction. Felling and processing consists of the process steps of finding a tree, positioning of the harvester, heading towards the tree, felling the tree in the right direction, transferring the tree to the work area (skid trail), delimbing, measuring and bucking, piling logs if possible by assortment close to the skid trail. Extraction consists of the process steps of finding the wood piles, sorting (if necessary) and collecting the logs, transporting the logs to the forest road and finally piling logs at the road or at the landing zone (Uusitalo, 2010). Timber transport and logistics: The management of the transport and logistics process is the final step in the forest as well as the interface to the saw mills. Besides timber inventory, transport organization and scheduling, route optimization, truck operations (localization, navigation, loading, transportation of logs) as well as quality control and documentation of execution are necessary process steps (Uusitalo, 2010). Timber sales: Timber sales can be planned upfront or after the harvest is executed, which is very much dependent upon national standards, timber assortments and best practices of the single organization (Uusitalo, 2010). Besides financial arrangements (Uusitalo, 2010), the main task of sales operations is the most effective allocation of the harvested timber.

prepare a forest stand for harvesting operations and vice versa. The main process steps of harvest planning are (Baumann, 2008): determining harvesting sequence and time, marking trees and off-road transportation lines (e.g. skid trails) and landings if necessary, defining harvesting method, defining timber assortments planned and estimating volume of work. Harvest organization and control: This process step contains all

2.2.1. Industry 4.0 concepts Three I 4.0 concepts can be identified in scientific literature (Bartodziej, 2017; Bauernhansl et al., 2014; Hermann et al., 2016; Kagermann et al., 2013):

• Cyber-physical systems (CPS): I 4.0 describes the merging of the

Table 1 Definitions of I 4.0. Source

Definition

Hermann et al. (2016)

“Industrie 4.0 is a collective term for technologies and concepts of value chain organization. Within the modular structured Smart Factories of Industrie 4.0, cyber physical systems (CPS) monitor physical processes, create a virtual copy of the physical world and make decentralized decisions. Over the Internet of Things (IoT), CPS communicate and cooperate with each other and humans in real time. Via the Internet of Services (IoS), both internal and cross organizational services are offered and utilized by participants of the value chain.”

Industrial Internet Consortium (2015)

“Industry 4.0 is the integration of complex physical machinery and devices with networked sensors and software, used to predict, control and plan for better business and societal outcomes.”

Kagermann et al. (2013)

“[…] the technical integration of CPS into manufacturing and logistics and the use of the Internet of Things and Services in industrial processes. This will have implications for value creation, business models, downstream services and work organization.”

Lu (2017)

“[…] Industry 4.0 can be summarized as an integrated, adapted, optimized, service-oriented, and interoperable manufacturing process which is correlated with algorithms, big data and high technologies.”

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physical with the virtual world (Kagermann, 2014). Thereby, the overarching goal is to create a digital image of reality that resembles it as closely as possible. The premise for this goal is that machines, objects and humans are integrated into the virtual world with the help of sensors which “collect physical data and by means of actuators influence physical procedures” (Kagermann et al., 2013). These smart machines, storage systems and production facilities (smart devices) are connected with each other, building an embedded system, the CPS (Zuehlke, 2010). Internet of Things and Services (IoTS): The network in which cyberphysical systems interact can be described as the Internet of Things (Hermann et al., 2016). This concept describes the “linkage of objects (things) with a virtual representation on the internet or a similar structure to the internet” (Kagermann et al., 2013). In this context objects are not only machines but all devices and humans equipped with sensing, identification, processing, communication and network capabilities (Lu, 2017). The Internet of Services can be understood as the opportunity of offering services as well as production technologies via the internet (Hermann et al., 2016). Smart Factory: The Smart Factory is the center and the final goal of I 4.0. This concept describes a factory in which CPS are the basis for decentralized, real-time communication and self-controlling production processes (Kagermann et al., 2013). The Smart Factory is intended to be more intelligent, flexible and dynamic as it consists of autonomous fractal systems which are linked via the IoTS (Kagermann et al., 2013). Machines and equipment will have the ability to improve processes through self-optimization and autonomous decision making (Roblek et al., 2016). According to Bauernhansl et al. (2014) the Smart Factory is the basic prerequisite to deal with the challenges of increasing complexity of the present and the future.

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2.2.2. Industry 4.0 technologies This section focuses on the technological basis for realizing the I 4.0 concepts. Various publications provide an overview over these technologies (Bartodziej, 2017; Bauer et al., 2014; Kelkar et al., 2014; Lu, 2017; McKinsey Digital, 2015). The most relevant I 4.0 technologies are:

3. Material and methods To analyze the current developments of digitization in wood supply, a systematic literature review is conducted identifying relevant literature over the last seven years (2011–2018). This period seemed appropriate, since the idea of I 4.0 was first articulated by Kagermann et al. (2011) and going beyond this range posed the risk that many publications are already outdated, due to the rapid technological developments of the last decade. This review included peer-reviewed academic articles as well as practitioner literature as an information source, since many of the articles on I 4.0 in forestry have been written for practitioners. To cover both, the search databases were chosen accordingly: a systematic search in Science Direct limiting the search to title, abstract and keywords covered mainly academic articles, whereas a standard Google Scholar search covered gray literature of practitioners and business as well. In Google Scholar we restricted the search to a title-search rather than fulltext search to focus on the most relevant hits available, as suggested by Haddaway et al. (2015) (cf. Table 2). The search strings covered both English and German search terms, which were used in various combinations applying a Boolean operator (OR). Wild-card characters (*) and automatic stemming were used to cover terms with similar word stems like the terms “forest” and

• Localization and identification of objects: The basis for the network of

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the CPS on a device and in a format which is helpful for the human user and for business decision making (Bartodziej, 2017). In this context, handheld devices like tablets or mobile phones are an example of this technology, however the development of intuitive operable interfaces is the focus of I 4.0 (Bauer et al., 2014). Touch interfaces and gesture recognition, voice control as well as virtual and augmented reality, created e.g. via smartglasses, are part of the technological development towards I 4.0 (McKinsey Digital, 2015). Big Data and cloud computing: The interaction of machines with machines or humans throughout a production process will lead to a massive amount of continuously generated data and transported information which must be stored. The cloud technology also called cloud computing is the basis for digital integration platforms where data is stored, analyzed and made available for (collaborative) usage within and across organization borders (Bartodziej, 2017). Cloud computing allows cheaper and more advanced processing of big data, often in form of so called pay-per-use systems (Aldeen et al., 2015). Advanced analytics: Advanced analytics describes advanced statistical methodology, enabling data analysis which was extremely time-consuming or impossible previously (McKinsey Digital, 2015). Application examples of advanced analytics are predictive maintenance or short-term product demand forecasts (Bartodziej, 2017). The advanced analytics technology is very important for the success of I 4.0 as in the future the transformation of “big data” into “valuable data” for better decision making will be the most important issue within the Smart Factory (Kaufmann and Forstner, 2014). Artificial intelligence: Once the idea of advanced analytics is further developed, combined with intensive M2M communication, different forms of artificial intelligence arise. The objective of artificial intelligence is to enable technical artifacts to learn from observation and experiences, allowing them for autonomous decision making and action (Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS, 2016).

CPS is that all objects within the network can be uniquely identified and located digitally as well as physically (Deindl, 2013). There are several technologies that can be named in this context like barcodes or more advanced radio frequency identification (RFID). RFID technology does not only enable identification but also localization of objects within the value chain once transport systems are equipped with appropriate trackers. Other localization options are GNSS such as GPS, dGPS or internal GNSS which can be used inside factories (Bartodziej, 2017). Sensor technology: Sensor technology is an interface between the physical and digital world. Smart sensors not only collect signals but also transform them into digital data and process them further. They process signals about the current state (not only ID and localization) of the physical world into useful information directly. The counterpart of the sensors at e.g. a machinery device are called actuators (Bartodziej, 2017). Machine-to-machine communication (M2M communication): The communication between objects, mainly machines in and between CPS, is an inseparable component of the Smart Factory. Real-time capable cable and radio based communication networks (WLAN interfaces or mobile phone networks) (Bauer et al., 2014) as connecting technology as well as common data standards (Bartodziej, 2017) are essential. Human-machine interaction: As humans will still be an important part of the Smart Factory (Zuehlke, 2010), integrating them into CPS is a very important topic. It is necessary to visualize information from

Table 2 Databases used.

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Databases used

Search restriction

Time restriction

Science Direct Google Scholar

Title, abstract, keywords Title

2011–2018 2011–2018

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Fig. 1. Search term linkage.

“forestry”. The search strings were combined as shown in Fig. 1 to combine relevant search terms in the context of I 4.0, with common search terms to identify forestry related articles using another Boolean operator (AND). As shown in Fig. 1 we took two ways to review the phenomenon of I 4.0. One was to describe it from a general point of view and with respect to the I 4.0 concepts, referring to the search terms identified in Section 2.2.1. The other way was to aim on I 4.0 technologies (cf. Section 2.2.2). With this approach we covered literature about I 4.0 technologies which were not specifically set in the context of the term I 4.0. To filter the relevant articles after the search was executed, the articles were screened starting with the titles and scanning their abstracts. After removing duplicates and non-relevant articles, key messages of the identified literature were consolidated in a database. This database was finally extended by literature identified via cross referencing from other already reviewed publications or articles recommended by experts for detailed review. After the data collection was completed, the data was evaluated qualitatively by categorizing the set of data and finally thematically analyzing the data in depth, with respect to the research goals of this article as proposed by Tranfield et al. (2003). We decided to assign the identified I 4.0 application examples to the process steps of the wood supply chain introduced in Section 2.1 to synthesize the findings of this literature review in a structured way (cf. Section 4.2). Therefore, we modeled the process steps of the wood supply chain on different levels of granularity in a process flow chart (cf. Section 5.1 and Supplementary file), so that the process steps could be disaggregated to a level where application examples could be assigned to a tangible activity, e.g. loading as sub process of the process step extraction. With this approach, we created visibility of the effect of I 4.0 technologies and concepts on the wood supply chain processes (cf. Fig. 2). Based on the findings of the literature review, besides the described process steps (cf. Section 2.1), we introduced the concept of a “virtual forest” which turned out to be the basis for many supply chain related I 4.0 application examples (cf. Section 4.2.1).

4. Results 4.1. Descriptive analysis The systematic keyword-based literature search produced 881 hits, of which 354 were identified as relevant for the topic I 4.0 in forestry after initial title and abstract screening1. Further filtering by relevance led to a final sample of 46 articles which were studied in detail. Based on these articles, 72 additional articles with high relevance were identified from cross-referencing and expert recommendations (cf. Fig. 3). From a quantitative perspective the literature search revealed the following insights:

• The terms I 4.0, Internet of Things or similar terms were not com•

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monly used in the context of wood supply and forestry: Only four of the 19 matches discussed the benefits of I 4.0 in the context of forestry (Athanasiadis et al., 2013; Bayne et al., 2017; Bo and Wang, 2011; Fitzgerald, 2016) (cf. Fig. 4). Advanced analytics and machine-to-machine communication as search words resulted in no hits (cf. Fig. 4). However, several articles from cross-referencing have been classified as articles about advanced analytics or machine-to-machine communication but none of them contained exactly the search words. It seems that in the scientific discussion this term is not commonly used. The topic human-machine interaction was rarely found directly but via cross-referencing. However, the thematic analysis showed, that within the wood supply chain this topic is of research interest, but as described before, often not assigned to the keyword used here (cf. Fig. 4). Research about the use of sensors in forestry and wood supply was intensive in the last years, as is indicated by the fact that it represents 78% of the total sample after abstract screening (cf. Fig. 5). Most of the articles in the sample discuss research results which

1 The reason why a lot of the initial sample hits had to be filtered out is that with the search term “forest” as part of the search string numerous articles have been identified which have nothing to do with forests itself but with the ensemble learning method “random forests”.

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Fig. 2. Theoretical approach.

Fig. 3. Literature search funnel.

trees, topography and soil conditions, available infrastructure and other information, this image can be called the virtual forest (Rossmann et al., 2010). Other synonymous terms which were found in different articles in this context are the digital forest and the concept of digital forestry (Lin et al., 2012; Zhao et al., 2011). The virtual forest cannot be assigned to a specific process step within the wood supply chain but is building a data basis for various application examples along the wood supply chain. Due to the specific characteristics of a forest2, creating a digital image of a forest is far more complex than creating a digital image of an assembly line. The technologies for generating a virtual forest are remote sensing

were based on sensor measurement. Topics like inventory and mapping as well as forest fire detection and fire management could be identified as most relevant topics in forestry sensor research of the last 7 years. Therefore, there was a significant imbalance regarding operational research with focus on wood supply chain management and machinery, with only 10 hits in this search being detected.

4.2. Thematic analysis 4.2.1. The concept of a virtual forest Cyber-physical systems in a smart factory provide a digital image of, e.g. assembly lines. In terms of a smart wood supply chain, a digital image from the forest stand must be generated. As sum of individual

2 For example, its unique nature and uncontrollable but natural influencing factors like weather conditions or topography (Oesten and Roeder (2012)).

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sensing data is still a key challenge on the way to the virtual forest (Bayne et al., 2017). Advanced analytics (AA) and artificial intelligence (AI) techniques solve this problem by translating data from remote sensing into forest attributes (Görgens et al., 2015). Artificial intelligence techniques have been used “with satisfactory performance in forest measurement” (Vieira et al., 2018). Tree species classification (Krahwinkler and Rossmann, 2013), yield estimations (Görgens et al., 2015), diameter at breast height (DBH) and height estimation on single tree level (Vieira et al., 2018) are important examples in this field. Forest attribute measurement is the first modelling step towards a virtual forest. The second step is combining these attributes to model concrete objects like single trees or the forest terrain (cf. Fig. 6). Rossmann et al. (2011) call this step semantic world modelling. Finally, all objects can be consolidated within a 3D GIS database for further simulations. The process from raw data to a single tree based database has so far been barely automated, especially for larger areas, however, efforts towards automation have been made (Sondermann and Rossmann, 2016). To finally lift the concept of a virtual forest from research into practice, it is a main task of commercial service and software providers to develop easy to use desktop and online decision

Fig. 4. Number of hits with relevance after abstract screening, sorted by search words.

Fig. 5. Number of hits for search word “sensor” after abstract screening, sorted by applications.

support systems for commercial forestry, which are based on virtual forest models. Current developments in practice show that this is already ongoing (Institute of Forest Ecosystem Reserach Ltd., Jílové u Prahy, Czech Republic, 2018; Treemetrics Ltd., Cork, Ireland, 2018).

and advanced modelling techniques, which can be summed up under the fields of advanced analytics and artificial intelligence (cf. Fig. 6). According to Rossmann et al. (2011), data acquisition and data handling, as well as acquisition costs, are the main problems of the forest sector in the context of remote sensing applications creating a digital image of the forest.3 Various authors give an overview of the developments of remote sensing in forestry throughout the last decade (Dassot et al., 2011; Torabzadeh et al., 2014; Valbuena et al., 2017; White et al., 2016). They show that remote sensing technology significantly developed throughout the last seven years in terms of surveying technology and accuracy, in addition to surveying platforms and costs. Airborne as well as terrestrial LiDAR technology is still a niche application in forest practice but, as research shows, has the potential to be the enabling technology for the development of a virtual forest. Developments in remote sensing show that processing remote

4.2.2. Harvest planning With a virtual forest as digital copy of the existing forest stands, harvest planning efficiency and accuracy can be significantly improved. Various factors have to be taken into account to determine harvest sequence and time. Salmivaara et al. (2016) aimed to provide decision makers with better data about trafficability by estimating the accessibility of harvest areas based on prediction models. Depending on the trafficability of the stands to be harvested, harvest time and sequence can be defined. Grigolato et al. (2014) demonstrate that the accessibility of stands to be harvested can be determined based on airborne laser scanning data including terrain, infrastructure and stand inventory information, depending on the type of harvesting systems selected. Pichler et al. (2017) assess efficiency and costs for marking potential harvest trees by using terrestrial LiDAR data collected by drones. The

3 Therefore, early applications of remote sensing can be found in interest fields where economic success is not in focus, e.g. fighting illegal logging or forest fire detection and management (Athanasiadis et al. (2013); Šerić et al. (2011)).

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the cloud. This solution enables navigation with a mean error of 0.55 m in a virtual testbed. Hussein et al. (2015) tested another application similar to the approach of Rossmann et al. (2009). They localize the position of the machine based on the match of a 3D LiDAR picture of the surrounding stand to another stem map from aerial orthoimagery up to an average error of 2 m. Combined with additional sensors (e.g. 2D laser scanners or encoders on crane joints) for determining the harvester’s head position, the precise position of the trees felled can be detected (Lindroos et al., 2015). Furthermore, with higher accuracy of the navigation system also the position of the log piles within the stand could be documented. Harvester head position systems have already been implemented in practice (Lindroos et al., 2015), however, their accuracy strongly depends on the positioning system of the machinery, especially under canopy.

• Machine as data collector: Harvesting machines on the one hand Fig. 6. Steps towards a virtual forest.

trees of interest are marked with RFID-tags for location within the stand. However, both costs and efficiency are still not on a sufficient level for this application. Another approach for tree marking is demonstrated by a startup powered by Andreas Stihl AG & Co. KG (Waiblingen, Germany), using a mobile phone as tracking and recording device and connected with a sensor placed on a spray can (Logbuch – Pioniergeist GmbH, Stuttgart, Germany, 2017). However, referring to the concept of the virtual forest, tree marking in general could become obsolete in future. Fransson et al. (2017) give an example of the model-aided determination of potential harvest trees. The trees are selected within a virtual forest and their positions are then transferred to the machine operators for optimal thinning efficiency. On the infrastructure side, the project BesTWay of Andersson et al. (2017) gives an example of how to virtually plan skid trails with a scenario approach, including digital data of terrain, wet areas and stand volumes. Decision support-systems for determining the best suitable harvesting method have also been discussed, however, these examples are limited to a purely economic perspective (Rossmann et al., 2011), not considering additional factors like the availability of contractors and technologies or ecological and social aspects. Nevertheless, with virtual forest data, there is potential to improve the harvest planning process and even test the planning using digital scenarios. Simulations can be used to determine costs and volumes of assortments harvested upfront for public tendering, negotiations with contractors or sales activities.

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4.2.3. Harvesting operations Different topics arise in I 4.0 harvesting operations:

• Orientation and navigation of machine within the forest stand: Determining the position of the machine or parts of the machine like the harvester head (Hauglin et al., 2017; Lindroos et al., 2015) is a key challenge for real-time connectivity of harvesters and forwarders. Collecting data about the position of the machine or machine parts is investigated by using a real-time kinematic GNSS system with a mean error of 0.94 m during a final harvest, meaning under no canopy (Hauglin et al., 2017). However, GNSS is technically limited in terms of accuracy under increasing dense canopy (Rossmann et al., 2009). To solve this problem, Rossmann et al. (2009) introduced a forest machinery navigation system which is based on the concept of the virtual forest as a virtual environment in which the machine can navigate by matching data collected by various sensors (GPS, LiDAR, odometry) with the virtual forest in

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can collect data about their own current state, i.e. pure technical information about the current status of the machine and motor (Ziesak et al., 2015), but also the machine’s operational performance like productivity, reliability, utilization and process times (Arlinger et al., 2017; Gallo et al., 2014; Laforest S., 2014; Olivera et al., 2016). On the other hand, they can collect data about the harvested wood due to the automated bucking system in the harvester. These data include harvested volume (in total, per species and assortment), volume by log, diameter and length distribution as well as damage frequency of the trees (Hauglin et al., 2017; Sellén, 2016). Other wood-related parameters such as the proportion of juvenile wood can be collected with acoustic sensors (Walsh et al., 2014). These sensors are not very common although they are on the market (Woodilee Consultancy Ltd, 2018). Finally, in future, harvesting machinery will also collect data of their environment, i.e. the stand itself (position of trees, DBH, tree heights and terrain) (Rossmann et al., 2009), the infrastructure and its condition such as rut depth (Salmivaara et al., 2018) as well as other objects’ geoposition (Lindroos et al., 2015). Wireless communication of machine with others: The value of the data collected by harvest machinery becomes apparent with the close to real-time transmission of these data. This means that for I 4.0 in the wood supply chain, connecting the harvesting machines as main production factor with each other and with decision makers outside the machines is important. Different applications can be found in this context along the process steps of harvesting operations. First, the localization data collected by the harvester can be used by the forwarder to locate the log piles along the skid trails. This application would help the forwarder operator choosing the optimal route to the piles and if needed, selectively collect the logs assortment by assortment. Furthermore, the forwarder could automatically record the locations of the log piles along the forest road or landing, directly transmitting these locations to the truck drivers. In addition, data about the current location and performance of other machines or manual workforce within the stand could increase the safety throughout operations and improve efficiency, especially when harvesting processes have multiple interfaces with additional need for coordination like cable yarding (Gallo 2014) or combined mechanized and manual felling operations (German Center for Forest Work and Technology, 2016). Besides that, real-time data about the infrastructure’s condition within the forest stand and machine generated data about slippage and motor performance could help decreasing soil rutting (Salmivaara et al., 2018). Human machine interaction and level of automation: Different levels of automation can be achieved in timber harvesting operations, ranging from operator assistance to simplified control (automation level 1) to fully autonomous machinery (level 5) (Lindroos et al., 2017). Some process steps have already been improved

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towards level 1 automation. Examples introduced by Lindroos et al. (2017) are cranes with motion sensors and improved motion control (Cranab AB, 2015), crane steering with boom-tip control, which has recently been advanced by Deere & Company Corp., Moline, Illinois, USA (2018) and active suspension for improved comfort (Ponsse Plc, 2017). The examples given by Lindroos et al. (2017) can be extended by decision support in terms of machine calibration (tire pressure etc.) as it is already provided as a service in agriculture by Deere & Company Corp. (Moline, Illinois, USA) (Jäger et al., 2015). Further examples are decision support for thinning operations4 giving the operator continuous feedback about the thinning intensity in the harvest area based on real-time harvester data (Moeller et al., 2017), advanced decision support for timber bucking, e.g. via acoustic sensors (Walsh et al., 2014) or geolocation matching (Lindroos et al., 2015). Another application which was recently tested by Palonen et al. (2017) is augmented reality technology for crane steering support. This application would also be useful for identifying marked trees in the stand (Rossmann et al., 2011) (cf. Fig. 7).

Fig. 7. Examples for harvesting machine innovations at automation level 1.

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Automation levels beyond this point are still part of theoretical discussions and research in physical or digital testbeds. Teleoperated harvesters with cranes steered by the operator’s head movement and operated by voice control have been tested (Mellberg, 2013; Westerberg and Shiriaev, 2013). Ringdahl et al. (2012) discuss costs and efficiency of harvesting operations combining unmanned teleoperated harvesters with normal forwarders or autonomous forwarders with standard harvesters. Main challenges preventing these unmanned and teleoperated machines from practical implementation are the reliability and accuracy of the sensor technology, safety reasons and the costs associated with automation systems. However, with the emergence of automation in the automotive sector it can be expected that also within the forestry sector autonomous driving and operating technology will be available at lower cost and with increasing reliability. The remaining challenge for forest engineering research will then be the transfer of this technology into the forest.

4.2.5. Transport and logistics The first step of the logistic chain is timber inventory. As already discussed, harvester integrated measurement is able to provide volume information by assortment for every tree harvested. However, to verify this data for sales, additional inventory is made depending on the legal restriction of the country and the sales agreements. Photo-optical wood pile measurement is one I 4.0 application in transport and logistics, which can help to increase measurement’s efficiency and collect data without media interruptions. Some companies already provide this technology, which is increasingly used especially for industrial timber (including saw logs) in Germany (Opferkuch et al., 2017a). Another approach that was tested recently is measuring the wood pile once finally loaded on the truck, using drones (Acuna, 2017). Which of the applications can be used very much depends on sales agreement, defining on which basis the forest owner is paid for the timber. As the transport of timber is the most expensive part of the wood supply value chain (Uusitalo, 2010), it is essential to optimize the timber transport by improved organization, scheduling and routing. Flisberg et al. (2017) give a practical example in the form of a case study about optimal truck routing. Digital data about factors like travel time and road quality, but also safety relevant attributes of the roads, are combined to determine the safest and most efficient route for log trucks. This route finder is already in practical use, generating measurable savings for over 100 Swedish companies every year. Another aspect of truck operations, quality control and stock management could be improved by the I 4.0 driven idea of unlimited identification and localization of products. The traceability of logs has been discussed in forestry for many years, recently dominated by the suggestion of using radio-frequency identification (RFID) for tagging trees i.e. logs (Björk et al., 2011; Häkli et al., 2013; Jung et al., 2009). One application in this context is to attach RFIDs to the logs by the harvester head once they are bucked (Pichler et al., 2017). The RFIDtags then could contain information collected by the harvester about the tree’s attributes and the log’s attributes like the name of the assortment, the location of the stand or even the tree itself (Pichler et al., 2017). The advantages of individual log identification and traceability are vast. Athanasiadis et al. (2013) discuss the use of RFID technology in the context of illegal logging control based on a transparent chain of custody. Björk et al. (2011) introduce the use of RFID for improved stock management in saw mills with significant savings of up to 70%. Häkli et al. (2013) see main advantages of RFID in forestry for optimizing production processes and trouble shooting. Traceability of the logs would also improve feedback circles from the mills to the foresters about quality and attributes of the sawn wood. If the attributes could be

4.2.4. Harvest organization and control Real-time connectivity within the whole production process as promoted with I 4.0 would improve the organization and control process in several respects:

• Operator instruction is easier as detailed digital data of the stand, trees harvested etc. is available in real time. • Managers do not necessarily have to be on site in person for progress and quality control (Arlinger et al., 2017; Sellén, 2016). • Data collecting harvesting machines enable management to perform • •

as for documentation and quality assessment (Arlinger et al., 2017). Post-quality control of the remaining stand or the technical trafficability of the skid trails can be supported by remote sensing technology and unmanned sensing platforms (Nevalainen et al., 2017).

extended data analysis and comparison of performance indicators for benchmarking (Arlinger and Moeller, 2014). Inefficiencies can be eliminated via ad hoc support. Complex multiple-interface harvesting processes can be managed easier or can even be automatically controlled (Gallo et al., 2014). Problem solving can be initiated before problems arise with advanced predictive analytics, e.g. predictive maintenance (Kateris et al., 2014) or yield predictions for truck and mill planning and operations (Asmoarp et al., 2017)

• Post-harvest documentation is easier as all stand and harvesting

related data such as yield, assortments harvested, area harvested etc. is documented by harvest machinery and stored in the cloud for practical uses such as sales arrangements and contractor pay, as well

4 In most countries, harvester operators are deciding which tree has to be felled for thinning on their own. Trees for thinning are not marked upfront by a forester like e.g. in Germany.

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with the degree of detail used in this article. Finally, the process flow chart shows that various organizational units are collaborating within a wood supply chain. Depending on the regional focus, forest enterprises possess their own staff and machinery for harvest planning, operations and timber logistics. In other cases, timber processing enterprises own the complete supply chain. However, more often forest enterprises need to collaborate closely with external contractors (Uusitalo, 2010). Collaboration between the organizational units becomes more complex the more companies as independent organizations become involved. This challenge will be discussed in the next section. We can conclude that the future developments in wood supply are all focusing on the issue of efficient information capturing, supply and allocation. With increasing digitization, the amount of collectable, storable and processable data is steadily increasing. The results of the literature review show that it is an important part of current and future research to demonstrate how this amount of data and information can be sufficiently used (cf. Section 4). Furthermore, it is important to discuss how different applications could be connected and integrated within an efficient supply chain. Finally, the visualization outlines that I 4.0 comes along with significant, sometimes disruptive changes within the supply chain. This development has already been noted for the industrial sector (Christensen, 2016). How to cope with these changes is another question that academic research and forest management have to deal with in future.

directly and automatically linked to the log and its position, discussions about the wood quality of a stand could be started. Big data analyses could help to find new patterns of the stand and the sawn timber quality (Fitzgerald, 2016). However, until today RFID technology is rarely used in commercial forestry practice although its reliability increased while costs decreased (Picchi et al., 2015). The reasons for the slow migration of the technology are not discussed in detail on a scientific level. However, one reason might be that RFID technology has to be implemented in different organizations like the mills, the contractors’ machines and the forestry administration with high efforts for coordination and alignment. 4.2.6. Sales In the future the virtual forest can be the basis of modelling techniques for optimized timber allocation to bring “the right log to the right product” (Alam et al., 2014). Alam et al. (2014) demonstrate in their simulation that, with increasing the certainty of the information about the wood quality, the gross profit gain of a merchandizing yard can be increased by up to 50%. Wood utilization and production efficiency can be improved by matching harvested timber with customer requirements using the transparency created by a virtual forest (Opferkuch et al., 2017b; Pichler et al., 2017). Cloud technology as described in Section 2.2.2 enables connecting saw mills and timber suppliers in real time, which would allow timber suppliers to react immediately to changing markets and ensure efficient timber supply. From a process perspective, this approach would mean changing timber supply from a push to a pull principle of supply. This principle has been successfully implemented in other industry sectors with benefits on both the supplier and the customer sites (Shah and Ward, 2003) and has been already discussed in the forestry sector (Kaiser, 2005).

5.2. Challenges of implementation and technology adoption Forestry and wood supply are starting to develop towards the idea of I 4.0. Various applications are tested in virtual and physical testbeds or even slowly adopted by commercial forestry, others are technically available for several years like LiDAR and RFID, but rarely used in commercial forestry. The implementation and adoption of new technology as an organizational change process poses challenges of both, technical and socio-economic nature (Chesbrough, 2010). Especially in the case of I 4.0 as concept with transformational character these challenges are high (Brettel et al., 2014).

5. Discussion 5.1. Smart wood supply chain – A process flow chart To conclude the findings of this review, a process flow chart was designed visualizing the main results of the thematic analysis (cf. Supplementary file). The visualization shows the smart wood supply chain consisting of interlinked cyber-physical systems. To enhance the readability of this article, the process flow chart is aggregated into the main process steps introduced in Section 2.1 (cf. Fig. 8). All data gathered within the smart wood supply chain is stored within a cloud based multi-user data storage network. The network can be called the internet of trees and services (referring to the idea of the IoTS) as within this network all data about product, production factors including the virtual forest and production processes are stored to provide value-adding information to all stakeholders involved within the supply chain. The visualization provides clear evidence that the value of the identified I 4.0 application examples in Section 4 result from their interconnection. One benefit of this interconnection is the decrease of media disruptions, e.g. from paper-based information to digital information. Less media disruptions result in an increase of performance in terms of time, cost, and processing errors, i.e. less mistakes and misunderstandings (Beimborn and Joachim, 2011). Chen et al. (2017) give initial examples for this interconnection in wood supply, focusing on a technical level. Further research is necessary to demonstrate the benefits of interconnection and the symbiotic effects resulting from connecting different I 4.0 applications already available for the wood supply chain. Moreover, the supplementary process flow chart shows that with increasing automation some manual process steps, which are necessary today, would become obsolete. Examples are manual tree and infrastructure marking, post-harvest timber inventory or manual quality control and documentation of execution. On the harvesting operations side, applications of automation cannot be visualized on process level

5.2.1. Technical challenges As outlined in Section 4, various technical challenges come along with I 4.0 applications in wood supply. Most of them are similar to those in other (industrial) sectors. The introduction of data standards within production processes and beyond is one of these challenges. I 4.0 aims at increased interconnection between different organizations. Without standards as a common language this connection is not possible (Weyer et al., 2015). In the forest sector, different data standards already have been developed to improve data exchange, e.g. ELDATsmart (Kopetzky, 2017), StanForD (Moeller et al., 2011), FHPDAT (Blattert et al., 2012). Although these standards are available, data along the supply chain is not easily accessible at national or international level. Environmental databases could be a role model at this point (Mansuy, 2016). To what extent existing data standards can support new arising I 4.0 applications has to be challenged. Rossmann et al. (2014) give an example of how open source GML based data management for forestry (ForestGML) as a standard could be a starting point towards compatibility and data exchange in the virtual forest. The security of IT-systems as well as data protection is another challenge of I 4.0. Trust in cloud technology very much depends on the trust of the users in data protection and therefore has not only a technical but also policy dimension (Kagermann et al., 2013). Sufficient availability of broadband internet connection (Kagermann et al., 2013) and, finally, the investment risks occurring with the implementation of I 4.0 applications within the production processes can be identified as additional challenges. Many I 4.0 applications require significant changes in work organization and production processes. Business decision makers are hesitant about taking these risks, possibly resulting in 214

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Fig. 8. The wood supply chain within the internet of trees and services.

contractors and customers need to increase their collaboration amongst each other, but also with players from other industry sectors and support early stages of innovation processes (Kubeczko et al., 2006). This especially applies with regard to I 4.0. The question of how the added value of I 4.0 applications within the wood supply chain with its various stakeholders should be divided between the different organizations then becomes crucial (Becker, 2015). In this field of tension, new digital business models could develop to commercialize new I 4.0 applications, e.g. via new services (Chesbrough, 2010). Furthermore, ownership of data has to be discussed with all stakeholders. Clear rules and access rights have to be defined to make sure that data autonomy is also guaranteed within the internet of trees and services (Roblek et al., 2016). Besides these data driven challenges, changing work environments, work organization and losses of jobs are feared on the labor side (Ittermann et al., 2015; Kagermann et al., 2013). However, it is expected that the migration of workplaces from one sector to another will be higher due to I 4.0, rather than the overall loss of jobs (Wolter et al., 2015). Another labor specific challenge is labor qualification. Most forest enterprises have not yet sufficiently qualified their workforce for new digital applications (White et al., 2016). Qualifying labor for I 4.0 is important for all sectors (Kagermann et al., 2013), but in a traditional sector such as forestry, qualification needs might be higher than in advanced industries as for example in the automotive sector.

temporary break-downs or product quality losses (McKinsey Digital, 2015). The forestry sector, characterized by long- term capital commitments, might be more hesitant than others at this point. To overcome this hesitation, it is an important task of applied research to demonstrate, e.g. in form of case studies, how high the added value of I 4.0 applications can be (Alam et al., 2014; Andersson et al., 2017; Häkli et al., 2013). However, this research needs to be done not only from an economic but also from an ecological and social point of view and over longer time periods to satisfy the multifunctional character of forestry and wood supply. Other forestry specific challenges are the robustness and reliability of the equipment (White et al., 2016). Some especially challenging topics are sensor usage in an outside environment, the wireless transmission of data, e.g. via cellular satellites or if possible standard cellular networks (Castonguay and Gingras, 2014), and the complexity of the forest stand itself. Lots of the identified I 4.0 application examples were tested in plantations or within single-layered, single-species coniferous forest stands. Further research is necessary to test and refine these applications to meet higher levels of environmental complexity. 5.2.2. Socio-economic challenges The transformational character of I 4.0 also results in socio-economic challenges. The willingness to cooperate across organizational borders and the trust in other organizations within the supply chain is one of them (Brettel et al., 2014). A principal challenge for the forestry sector is the handling of its high stakeholder fragmentation. Forest owners, forest enterprises, harvesting contractors, timber transport 215

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6. Conclusion With this article, we could identify the latest trends in research and practice towards I 4.0 in wood supply. Our literature review showed that I 4.0 is not a marginal issue in forest science but, as literature proves, a current and important topic. It turned out that the term I 4.0 is not very common in forest science whereas I 4.0 technologies are more broadly discussed. Sensor technology and remote sensing in particular have been an important part of forest research throughout the last decade. We showed that remote sensing is one basis for the concept of a virtual forest, which is the final fundament of many I4.0 applications in wood supply. We see that the virtual forest is slowly integrating into forest management as acquisition and handling of data becomes easier. Artificial intelligence and advanced analytics are enabling technologies for the success of the concept of a virtual forest. We furthermore demonstrated that current research aims to integrate data from the virtual forest as useful information in all processes of wood supply, mainly harvest planning and organization, but also harvesting operations as well as transport and logistics. We identified concrete application examples for I 4.0 within all main process steps of the wood supply chain. They ranged from computerized decision support-systems in harvest planning, but also transport and logistics to electronic control of harvesting operations, machine-to-machine communication, machine vision and automation and post-harvest management systems for quality control. Consolidating all application examples within a process flow chart reveals that efficient information supply and allocation via the internet of trees and services would significantly change the wood supply chain processes as we know them today. Further research is necessary to bring the identified application examples into practice. The main challenges are the accuracy and robustness of the systems, the availability of data standards and robust networks as well as cost-related aspects. However to implement I 4.0 in wood supply, technical and socio-economic challenges have to be considered as well, such as the willingness of cooperation, questions of data autonomy and changing working environments. To what extend the commercial forestry sector is open to a rethink of the wood supply chain, with respect to I 4.0, will be mainly driven by these factors and should also be part of future research discussions. Funding This research did not receive any specific grant from funding agencies in the public, commercial or not-for-profit sectors. Declaration of interest None. Appendix A. Supplementary material Supplementary data to this article can be found online at https:// doi.org/10.1016/j.compag.2019.04.002. References Acuna, M., 2017. Automated volumetric measurement of truckloads through multi-view photogrammetry and 3D image processing software. In: Ackerman, P., Norihiro, J., Ham, H., Brewer, J. (Eds.), Proceedings of the 4th Precision Forestry Symposium. Producing More from Less. Towards optimising value in the bio-economy from data driven decisions, Stellenbosch, South Africa, pp. 19–21. Alam, M.B., Shahi, C., Pulkki, R., 2014. Economic impact of enhanced forest inventory information and merchandizing yards in the forest product industry supply chain. Socio-Econ. Plan. Sci. 48, 189–197. https://doi.org/10.1016/j.seps.2014.06.002. Aldeen, A.S., Abdul, Y., Salleh, M., Razzaque, M.A., 2015. State of the art survey on security issue in cloud computing architectures, approaches and methods. J. Theor. Appl. Inform. Technol. 75, 53–61.

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