The world economy and society are facing fundamental changes as digitalization is shaping many areas of our lives, and the systematic collection and analysis of data is becoming increasingly important [1
]. These developments triggered changes in production processes, which led to the fourth Industrial Revolution, known as Industry 4.0 [2
]. It focuses on cyber-physical systems, internet of things, internet of services, and the smart factory [3
]. Information and communication technologies are a central element of this new Industry 4.0, which aims to connect and computerize traditional industries [1
]. Moreover, its objective is to improve adaptability, resource efficiency, and integration of supply and demand processes between factories facing all challenges, opportunities, and drivers of Industry 4.0 technologies [4
]. In Germany, the promotion and realization of the digitization’s potential is a declared goal of the federal government [1
Digitalization has found its way into forest industry. It is propagated under the term “Forestry 4.0” [6
]. In Forestry 4.0, applications are ranging from computerized support aids to electronic control, machine vision, and post-harvest management [7
]. Its value lies in the connection of process steps along the wood supply chain with a close to unlimited information flow and allocation in an internet of trees and services, which can lead to changes and added value in harvest planning, harvest organization and control, operations, transport, logistics and timber sales. Within the internet of trees and services, a considerable amount of data from different sources is collected. In the last years, remote sensing data has been particularly analyzed, which is one basis for the concept of virtual forests [7
However, there are also other data, such as forest machine data, which can be collected automatically. Modern harvesters are equipped with an on-board computer (OBC) that collects a large amount of information concerning machine parameters and the harvested timber in a standardized format according to the Standard for Forest Machine Data and Communication 2010 [8
], which provides best technical prerequisites for data exchange and use. Additionally, in German forestry, the importance of highly mechanized harvesting systems has steadily increased. In 2018, 65 million m3
of timber were harvested in Germany. Almost 80% of the timber was spruce, fir, pine, and larch [10
]. The main share of this timber was felled, processed, and forwarded by highly mechanized harvesting systems. References from the last two decades agree that about 50% of the timber is provided through highly mechanized systems in Germany and also Bavaria [11
]. This reality increases the need for improved harvester data integration. Another point for using these data are the developing technologies that permit the real-time analysis of big data with the support of artificial intelligence. Predictive analytics such as regression and machine learning techniques (e.g., neural networks as the backbone of deep learning algorithms) offer new possibilities of gaining richer and more comprehensive insights into the company and getting an advantage over the competition [13
] and seem to deliver promising results.
The German forestry sector is facing a variety of challenges which are mostly linked to the increasing demands on the forests. The conservation of vast forest areas as well as the careful and sustainable utilization of wood in connection with climate change are becoming essential. This is particularly the case for calamity situations, such as wind throws/breaks and bark beetle infestations, where it can be useful to provide information about harvested trees as quickly as possible to ensure the efficiency of the wood supply chain. Harvester production data could be an option to deliver first information about the harvested wood for further planning purposes.
Stakeholders of the wood supply chain, such as forest owner associations, forest management companies and their forest, contractors, and customers of the timber industry, have a strong interest in the use of these harvester data for logistic planning, economic benefits, and better customer orientation. In 2018, Labelle et al. stated that the integration of harvester production data such as tree species, assortments, amount of harvested timber or diameter and length information into the German wood supply chain is expected to lead to added value through a more consistent flow of information and optimized networking of the stakeholders involved [14
]. The German cluster of forest and wood is comprised of diverse stakeholders and, thus, it represents a high mixture of processes and structures [15
]. This complexity is one of the reasons for a lack of standardization of business processes and a shortage of predefined data interfaces for optimization. Comprehensive data integration involving all key stakeholders participating in the German wood supply chain is missing in many fields of forestry business, and the reasons for this are strongly discussed [16
There are some studies that have analyzed the general challenges, opportunities, and drivers for the implementation of Industry 4.0 [4
]. As there are already many technical possibilities of using IT in forestry, Müller et al. (2019) recommend, in their article, not only to look at the technical challenges but also analyze the socio-economic challenges, such as willingness for cooperation, changes in work environments, labor qualification, data autonomy, and added value distribution [7
]. Rantala et al. (2020) also recommended analyzing the social and governance aspects of forest data [18
Furthermore, the successful integration of harvester production data in the wood supply chain is tied to various requirements. Therefore, the main objective of this article is to present the requirements and conditions for the continuous use of harvester data throughout the German wood supply chain, in addition to the technical requirements. In detail, the objectives are to analyze (1) which legal basics determine continuous harvester data use, (2) what are the social challenges of harvester data integration, (3) what is the current economic situation of value creation of data and whether it is possible to trade harvester data.
2. Materials and Methods
Two different research approaches were chosen. To address objectives 1 and 3, a narrative literature research was performed and supplemented with expert recommendations, whereas objective 2 was achieved through a qualitative content analysis of 27 expert interviews.
2.1. Legal and Economic Literature Analysis
A three-step approach was used to gather the published findings related to harvester data integration in German wood supply chains.
Within a legal context, the German Basic Law (GG), Civil Code (BGB), Criminal Code (StGB), Copyright Law (UrhG), Federal Data Protection Act (BDSG), Working Hours Act (ArbZG), Personnel Representation Act (BPersVG) and the Work Constitution Act (BetrVG) were reviewed. EU-General Data Protection Regulation (EU-GDPR) and supplementary court judgements or juridical deliberations were also examined. To avoid misinterpretations and the omission of relevant sections, secondary literature, interpretations from lawyers, and literature from practitioners were considered. Legal references were accessed through internal university databases (Göttinger Universitätskatalog and Elektronische Zeitschriftenbibliothek), as the issues of data use and exploitation are only slightly covered in articles in natural scientific databases. Since some suitable OECD articles were found via cross referencing, OECDiLibrary was used as an additional database.
To find suitable legal references and to also achieve the economic goal of this article, the focus was first directed at German laws and articles, but a wider international perspective was also warranted. Time restriction was from 2000 to 2020 and articles written in German and English were considered. Search terms were entered in different combinations using the Boolean operators “AND” and “OR”. The following terms were used to generate results for the legal and economic results section:
References were further supplemented by cross referencing and expert recommendations.
Article titles that seemed appropriate to answer the research questions were first selected for a closer look. After screening the abstracts, irrelevant articles were discarded. In the deeper analysis, topics were formed, e.g., “construction of ownership-similar rights on data” or “data tradability”. These topics were considered as the basis for the article structure. The legal analysis was aiming at identifying the owner of the harvester production data, whereas the economic analysis was set to explore the framework of tradability of harvester production data. However, the research object of this study was unexplored and therefore, results of literature analysis were then transferred to harvester production data.
2.2. Qualitative Content Analysis of Expert Interviews
To examine the stakeholders’ conditions and requirements for harvester data integration in the German wood supply chain, a two-step approach was chosen.
A total of 27 exploratory expert interviews with all in all 30 experts were performed. The interviewees all have professional experience in forestry and were selected on the basis of expertise, their position in the companies interviewed, and also accessibility. The interviews were conducted between April 2018 and February 2019. The participation in the interviews was voluntary. The interviews were conducted with a semi-structured guideline and recorded by a voice recorder. Stakeholders of the wood supply chain of a federal German state forest management enterprise and a private forest owner association, such as employees, forest contractors and representatives of the timber industry in Bavaria were interviewed (Table 1
). The recordings were then transcribed with the software “f4” by two research assistants according to the transcription rules and afterwards validated by the interviewer.
To explore the stakeholders’ conditions on harvester data integration, an exploratory qualitative content analysis was performed. Firstly, the transcripts were anonymized and examined with the software MAXQDA 12 (version 12.3.5). Therefore, the technique of content structuring by deductive category application was applied. Basis for the derivation of structuring the material was the construction of a category system. This was formed out of the theoretical knowledge context as well as certain questions of the interview guideline.
Based on the interview guideline and the study objectives four main categories (challenges of data integration, characteristics of data (interfaces), definition of data ownership and the definition data value) were deductively built. Sub-categories were added inductively during analysis of interview content to assign the interviewees answers to specific topics. Statements referring to these topics were then paraphrased and presented in the results section.
2.3. Definition of Data, Ownership, Possession, and Ownership-Similar Data Sovereignty
A definition of the term “data” is, in general, not straightforward. The international standard ISO/IEC 2382-1:2015-05-00 defines data as “a reinterpretable representation of information in a formalized manner, suitable for communication, interpretation, or processing” [19
]. The German and European laws relevant for this study, do not include a precise definition of harvester production data or in a broader sense, machine data. Section 202 a (2) of the German Criminal Code (StGB) only describes an attribute of data as the possibility to save it in an electronic, magnetic, or otherwise not directly predictable way [20
]. However, personal data is defined in the Federal Data Protection Act (BDSG) (Section 46 No. 1) and the EU-General Data Protection Regulation (Section 4 No. 1) [21
]. Throughout these attempts to define data, it became clear that the concept of information, created through the interpretation of data, is central [23
]. Therefore, harvester production data is defined in this article in accordance with Section 46 No. 1 BDSG [21
], 202 a StGB [20
] and ISO/IEC 2382-1 [19
] as “information about tree species, distribution of assortments, quality or diameter and length information and its associated position, represented on the OBC, based on StanForD 2010 (Standard for Forest Machine Data and Communication)”.
Based on German Civil Code (BGB), ownership is defined as the right to control an object from which others can be excluded, while possession is considered as the exercise of actual power over a physical object [24
]. In the context of this paper, ownership-similar data sovereignty is understood as all legal possibilities to establish sovereignty over data and exclude others from using it.
2.4. Scientific Research Approach
Due to the nature of this project, a mixture between the three spheres (legal, social, and economic) was warranted and addressed through a combination of literature review and qualitative content analysis (Figure 1
). This two-fold research approach was necessary to fully understand the problems and challenges faced with harvester data integration in German wood supply chains. This is especially the case since the topic of harvester data integration remains scarcely reported in literature and, hence, required the input of key stakeholders.
The cross-sphere approach consisting of a narrative literature review and exploratory content analysis of expert interviews revealed the intense connection of the legal, social, and economic spheres. The discussion of challenges, recommendations for handling harvester data in Germany, as well as the consequences for science and practice could show that successful integration of harvester data in German wood supply chains is only possible if all conditions are considered. The approach provided interesting insights that could be important and potentially transferable on an international level.
Basically, since there exists no legal ownership or possession on harvester production data in Germany, but on the data carrier, the forest contractor has, in fact, the power over the harvester production data. If he is willing to share the data with others, the use of the data could be limited through data protection laws and protected against misuse. Moreover, other legal alternatives could be chosen, such as contractual regulations for data handling. The tradability for harvester production data is given, but no fixed value or price for the harvester data have yet been established. The discussed approaches for monetary quantification of harvester data have clear weaknesses in terms of objectivity and quantification of some variables. Therefore, pragmatic approaches are probably the most suitable at present to determine the harvester data value. This is also impacting the social requirements because the interviewees also had no idea of price and value. However, the willingness to share harvester data is increasing, particularly when it is associated with monetary value. To guarantee successful integration of harvester production data into the German wood supply chain, the authors suggest that key stakeholders of the wood supply chain (forest contractors, forest owners, state forest management enterprises, forest owner associations, and timber customers) should join and discuss the possibilities and economic benefits of data exchange. The key stakeholders should individually define their data use and exploitation interests and work out a recommendation on how harvester production data can be exchanged, similar to the example of the Finnish recommendation for principles relating to the ownership, use, and processing of forest machine data.