David Holtkemper
email: david.holtkemper@fir.rwth-aachen.de

Günther Schuh

Blockchain as Middleware+

Keywords: Blockchain, Supply-Chain-Management, Middleware+, Internet of Production, Supply-Chain-Data-Management

In supporting decision making of manufacturing companies, the added value of cross-domain data exchange for aggregating information is well established in enterprise organization research and is represented, for example, in the reference model "Internet of Production" (IoP). Currently, there is little research regarding the role of Blockchain technology in such a reference model and how specifically the IoP needs to be expanded to address cross-company data exchange. This paper presents a proposal for such an extension to outline the use of Blockchain technology and to elaborate the open research demands for implementation. In particular, desk research and the development of concrete use cases for cross-company data exchange between business application systems were carried out. The results are, on the one hand, extending the IoP by a third dimension, which corresponds to the supply chain, and, on the other hand clarification of the role Blockchain technology can take in this context.

Introduction

Challenges in Supply Chain Management

The decline in value added per individual company observed in recent decades will continue over the next few years [START_REF] Schmieder | Qualitätsmanagement im Einkauf[END_REF]. This is facilitated by increasingly complex products, which in turn means that more and more different suppliers have to be integrated into the value chain for the production of a product [START_REF] Schuh | Enabling Radical Innovation through Highly Iterative Product Expedition in Ramp up and Demonstration Factories[END_REF]. There is a need to cooperate with more and more value creation partners. At the same time, ever-shorter product life cycles and the associated ever more frequent changes to products mean that valueadding partners have to be changed more and more quickly.

Increasing competitive pressure and the shift from the seller to the buyer's market are forcing companies to exploit the potential for optimization through digital data exchange, previously considered to be too costly or insufficiently productive [START_REF] Cooper | What's Next?: After Stage-Gate[END_REF]. For example, the use of data from the supplier can prevent a company from having to recollect data itself, which often avoids inaccuracies and duplication of effort. Collaboration concepts such as just-in-sequence, in which the supplier delivers the parts according to the sequence at the car manufacturer, should reduce inventory and handling costs. The prerequisite is that the car manufacturer transmits the sequence to the supplier -digitally to keep the administrative costs reasonably low. Ultimately, therefore, there is the need for quickly established digital networking with many value-added partners. The concept of the organization of the data exchange along a value chain should be called Supply-Chain-Data-Management in the further.

1.2

Digital connection / Industry 4.0 "Industry 4.0" has established as term for the mass connection of information and communication technologies [START_REF] Schuh | Industrie 4.0 Maturity Index. Die digitale Transformation von Unternehmen gestalten. acatech -Deutsche Akademie der Technikwissenschaften[END_REF]. It refers to the real-time capable, intelligent, horizontal and vertical networking of people, machines, objects and information and communication technology in order to be able to manage complex systems [START_REF] Bauer | Industrie 4.0 -Volkswirtschaftliches Potenzial für Deutschland[END_REF]. For implementation in production, Klocke et al. define the reference architecture "Internet of Production" (see chapter 2) and present some use cases of such connection.

As part of change requests in production, technical changes in the form of state changes of actually released products are necessary [START_REF] Jarratt | Engineering change: an overview and perspective on the literature[END_REF][START_REF] Langer | Änderungsmanagement[END_REF]. Due to the increasing dynamisation of the industrial environment, technical change management has become increasingly important [START_REF] Jarratt | Engineering change: an overview and perspective on the literature[END_REF][START_REF] Storbjerg | Development of an engineering change management capability framework for enterprise transformation[END_REF]. Due to the many negative effects of such requests (budget overruns, time delays), companies usually try to avoid them if possible. Cost drivers in change management are in particular cross-departmental coordination efforts and media discontinuities in the participating operational application systems. A significant improvement of the change request process could therefore be achieved, among other things, through meaningful linking of CAQ, PLM, CAD, FEM, and ERP systems: The introduction of the change request without this link often leads to large expenses in the manual transfer of data between the systems mentioned and ultimately to inconsistent data and ultimately to high error and failure costs. [START_REF]Internet of Production für agile Unternehmen[END_REF] The design of additional systems for technical process stabilization in order to retrofit them to a machine is necessary, for example, if the machine has critical structural vibrations during operation. These vibrations could be alleviated with the help of individually designed multi-mass dampers. Such additional systems are usually easier to implement than structural adjustments. The design of such systems requires the availability of data such as the frequency and waveform as well as the equivalent oscillating mass of the machine structure. Therefore at least data from the CAD and the FEM system are necessary. [START_REF]Internet of Production für agile Unternehmen[END_REF] The use cases presented impressively show the added value of connecting various business application systems, but remain at the corporate level. Even in those use cases in which customers are involved, not their systems but self-used systems with customer data are used. In the following chapter, the Internet of Production is introduced, presenting a framework that is able to address the digital connection between business application systems used in the presented use cases.

Internet of Production

Fig. 1 shows the reference architecture of the Internet of Production, which was presented at the Aachen Machine Tool Colloquium in 2017 and culminated in 2018 in the eponymous Aachen Cluster of Excellence sponsored by the DFG. Seen from left to right, it includes the three phases "Development Cycle", "Production Cycle" and the "User Cycle". At the lowest level, the raw data usually generated in the respective phases and (graphically above) the application software in which this data is managed are displayed. At the highest level, the vision of decision support Smart Expert systems is presented, which support the employees in the respective phases. They feed on the layer of so-called "smart data", ie data which has already been pre-refined by analytical methods. Input for these data analyzes comes from the already mentioned application software systems. The access to the proprietary operational application systems therefore is allowed by a Middleware+, which, however, has not been further specified in its design. 2

Cross-company Internet of Production (Three-dimensional)

The attempt to expand the IoP with regard to cross-company data connection is carried out per level below.

The lowest level of raw data and application software systems remains company-specific. The attempt to create data through the use of common systems across companies would fail due to the aspiration to quickly achieve connection with new partners. Any change of value-added partners would be associated with the introduction of new business application systems -at least if it can be assumed that systems that are not globally consistent will be used. With the example of ERP systems it becomes clear: More than 2000 different systems are currently offered on the market [START_REF] Bach | Marktspiegel Business Software ERP/PPS 2017/2018. Anbieter -Systeme -Projekte[END_REF]. Correspondingly, a cross-company representation of the IoP would have to show a separate raw data and application software layer.

The Smart Expert view, including the apps shown there, contains expert systems that specifically provide those users with information that is relevant to them. Howev- er, the information to be extracted from each company's data may differ significantly from company to company. For example, it may be helpful for the customer of an aluminum parts supplier to see how well their supplier has met the prescribed component tolerances. The employees of the aluminum supplier, on the other hand, are more likely to receive orders from the customer in the Smart Expert System. In many cases, there is a high overlap of the contents of the Smart Expert systems along the supply chain -once developed apps can be used in several companies. However, standardization of the level must be avoided. This results in a direct conclusion for the Smart Data layer. Features such as data integration or the data model, as well as the data analysis method to use, are highly dependent on what results are displayed on the Smart Expert level. Consequently, the same applies here as for the Smart Expert level: there is certainly a high degree of reusability of the functions used. However, standardization does not make sense.

The situation is different at the level of Middleware+: especially in this area, a breakthrough in the form of cross-company connection is indicated and would enable many use cases along the supply chain. A Middleware+, which would ensure overall management of data access to proprietary systems and data there, would enable organizations to efficiently use data from their value-added partners. A connection via a shared Middleware+ would also reduce the connection costs of new partners, since the number of interfaces is considerably reduced -the rapid change of the value-added partners and the connection of many partners for the realization of Supply-Chain-Data-Management are therefore possible.

Fig. 2. Cross-company Internet of Production

The selected form of representation extends the IoP by one dimension: the different value creation stages. Two problems of this form of representation should be briefly addressed. On the one hand, this representation could give the impression that expressly only classical value chains with a defined sequence are displayed instead of value networks. In fact, the order of the companies here is not decisive, a connection between company 1 and company 3 is possible. On the other hand, in the industrial supplier-customer relationship, the "user cycle" of one company is often the "production cycle" of the follow-up company. This is not explicitly made clear in the presen-tation, but also irrelevant for multilateral networking. The fact that test data in the CAQ could come from the customer's BDE is already taken into account by the fact that the Middleware+ is consistent across all cycles.

For the concrete design of such a cross-company Middleware+ is to be noted that there is a conflict of trust between the companies. Even with in-house connection, it has become apparent that the specific technical design of a Middleware+ is not trivial. In the case of a cross-company perspective, it is to be expected that this arrangement will be much more difficult. While there is an escalation level in the organization of a corresponding implementation project with a higher hierarchical level, which can simply make a decision in the event of disagreements between the stakeholders, such an escalation level is missing in the cross-company case. Furthermore, simple authentication methods for in-house connection can prevent data from being illegally manipulated -this is not the case with cross-company connection. Centralized approaches (platforms) have so far hardly established themselves for such sensitive business areas. Blockchain technology was introduced in 2008 as a technology which, without a trusting intermediary, should make data forgery-proof and highly available; even with this technology, the industrial use is already much discussed, but hardly brought into the real application [START_REF] Nakamoto | Bitcoin: A Peer-to-Peer Electronic Cash System[END_REF]. The mentioned Blockchain technology will be further considered in more detail for fit as Middleware+ in cross-company IoP.

3

Blockchain as Middleware+

Blockchain technology

A scientifically recognized, uniform definition of the term "Blockchain" does not currently exist [START_REF] Schlatt | Blockchain: Grundlagen, Anwendungen und Potenziale[END_REF]. There is general agreement, however, about some properties of the Blockchain technology, which will be explained below. A Blockchain is a decentralized database (often called "distributed database"). The included data is usually not available at a central or a small number of distributed locations to which authorized participants can access (see Clouds on central servers or on distributed server networks), but completely for each participant in a network of distributed, decentralized units. The data is stored in the form of transactions in blocks of data arranged chronologically. [START_REF] Schlatt | Blockchain: Grundlagen, Anwendungen und Potenziale[END_REF][START_REF] Tschorsch | Bitcoin and Beyond: A Technical Survey on Decentralized Digital Currencies[END_REF] This decentralized structure prevents a loss of one or more network nodes from permanently losing the stored data -as long as at least one network node remains intact [START_REF] Schlatt | Blockchain: Grundlagen, Anwendungen und Potenziale[END_REF]. With a sufficient number of participants, the loss-security of the data is sufficiently high. The involved network nodes continuously synchronize their data status, new information is sent to all nodes in the network [START_REF] Badev | Bitcoin. Technical Background and Data Analysis[END_REF].

A block consists of a block header and its associated transactions. The transactions are combined via a hash tree to a root, which is part of the block header. In addition to the "hash value of the predecessor block" explained below, a timestamp is usually stored in the block header which indicates the date of origin of the block. Depending on the Blockchain also the storage of further data is provided. [START_REF] Tschorsch | Bitcoin and Beyond: A Technical Survey on Decentralized Digital Currencies[END_REF] A key feature in the data structure of Blockchain technology is that the individual data blocks are linked using hash functions. For the subsequent blocks, these are linked in chronological order by means of pointers. The replacement of the simple reference with a cryptographic hash function yields an authenticated data structure which, with sufficient probability, ensures that the previous data is not compromised. This anti-counterfeiting security benefits those elements that are included in the calculation of the linking hash value. In the case of a Blockchain, a cryptographic hash function is usually used for linking the blocks, for which the preceding block header is used as data input. Since the block header contains the root of the hash tree of the associated transactions, the security against forgery thus extends to the entire previous block and ultimately to the entire data structure. [START_REF] Tschorsch | Bitcoin and Beyond: A Technical Survey on Decentralized Digital Currencies[END_REF][START_REF] Böhme | Technische Grundlagen und datenschutzrechtliche Fragen der Blockchain-Technologie[END_REF] Consciously, a more detailed description of the Blockchain technology and specific sub-technologies (hash functions, consensus algorithms, cryptography, etc.) should be omitted here -the reader should refer to the relevant specialist literature.

Blockchain in the Internet of Production

It becomes clear that the Blockchain technology is transaction-based. As a redundant database, it can provide data forgery-proof and high availability of Data. As part of the reference architecture of the IoP, the Blockchain could therefore serve as Mid-dleware+. This application would solve the problem that in use cases of crosscompany data usage, a comprehensive data storage or accessibility solution can not be easily installed. Which of the two mentioned would be the Blockchain, incidentally, would depend on the configuration. It would be possible to use the Blockchain for storing access authorizations to proprietary systems as well as the use for storing the data itself, which would then be redundant with storage in the company's own databases. Since the first case has been little explored so far it will be assumed from the second in the following.

In order to clarify the mode of operation, a few potential use cases are briefly outlined, in which the Blockchain technology is used as Middleware+ in the crosscompany Internet of Production.

In the BDE system of an aluminum supplier, the process parameters from the production process are stored unambiguously per component. These process parameters have a significant influence on the material properties and thus on the machine parameters to be set during further processing. The process parameters can be written from the supplier's BDE to a Blockchain, with the summary of the process parameters corresponding to part of a transaction. During further processing of the parts, the parameters can be retrieved by the appropriate operational application systems. It would be conceivable in addition to the transmission of the process parameters and the transmission of desired-dimension deviations that are then used in further processingeg. in the assembly robot offset these deviations. As a result, the processing company could allow higher deviations, which in turn would save costs.

In the ERP / ME-System, especially for convergent and divergent material flows, the relationships between the predecessor and successor parts are maintained and relevant data is copied to a Blockchain. The transactions in the Blockchain contain the information about which components were merged. Through the documentation along the entire value chain, it is thus possible to ensure the traceability of a product or all of its components across the entire value chain. If several faulty products are identified, it can be determined by a simple analysis, where similarities lie -for example, in the fact that with all faulty products a component was manufactured on the same day. Thus, callbacks can be carried out in a particularly targeted manner.

The BDE of a company documents how many working hours a particular machine has been used. The distributor of the machine offers additional industrial services, such as maintenance contracts or subscription models. The BDE transfers the data necessary for the implementation and / or billing of the specific services into a Blockchain. Thus, for example, a maintenance contract, which provides for maintenance after a certain period of use, or a pay-per-use billing can be created.

Summary, outlook and research needs

In this paper, it was presented which current developments force companies to operate Supply-Chain-Data-Management, ie to share data along the value chain. One of the challenges of this cross-company data management is the technical feasibility. It was also shown that the reference architecture of the Internet of Production is suitable for enabling use cases of cross-domain networking -however, cross-company networking could not yet be represented in the IoP. Consequently, the architecture has been extended to a cross-company Internet of Production.

Furthermore, it was explained how the Blockchain technology -which is often described as an enabler for cross-company data exchange -can be classified in the cross-company IoP: it assumes the role of Middleware+. Also how these solutions addressed the problems that are to be expected in the Supply-Chain-Data-Management was executed.

It becomes clear that the described use cases of cross-company data exchange can be implemented with a Blockchain as Middleware+, but the demands on this Blockchain are clearly different per use case. These claims and the use-case-specific design of the respective Blockchain are not trivial. In the beginning it was already stated that the technology is not described in a uniform way. There is a lack of a comprehensive description of the technology and its various features and feature types to explain how to design the specific Blockchain, depending on the use case. The description of features such as the type of data to be stored, the information release, etc. could provide a remedy.

Also it remains an open technical problem that data in Blockchains are indeed stored counterfeit-proof, but this does not guarantee that they were correct when they were created. The consensus algorithm is expressly only to ensure the immutability of the data and not to verify their accuracy.

A non-technical problem, which is best described under the name "Supply-Chain-Project-Management", also weighs heavily: Even if it is technically possible to execute the mentioned use cases, this does not mean that they can be implemented. The lack of a superior authority, the necessary economic advantage per company and the lack of initiative by an involved company can mean that digital connection across corporate boundaries is never implemented. This is similar to a problem of component design: There are components that can be drawn and in this form perform certain functions very well -but can not be produced after all because, for example, screw points would not be reached.

[ 9 ]Fig. 1 .

 91 Fig. 1. Internet of Production[START_REF]Internet of Production für agile Unternehmen[END_REF]


Maria Stoettrup

Schioenning Larsen

Astrid Heidemann Lassen

Kjeld Nielsen

Process Innovation in Learning Factories: Towards a Reference Model

Keywords: Learning factory, process innovation, Industry 4.0, literature review, reference model

The fourth industrial revolution, also known as Industry 4.0, implies significant new technological opportunities for today's manufacturing industry. However, manufacturing companies still lack knowledge and skills on how to fully utilize these innovative technologies. This is in particular the case as regards development of process innovation. In order to advance this knowledge, the concept of learning factories is useful to support the manufacturer's learning and movement towards innovation. In short, a learning factory, that supports process innovation, can be described as a learning process for industry participants, which utilizes a learning factory to create rapid and innovative process solutions in industry based on the paradigm of Industry 4.0. Research on this topic is however still relatively scarce and scattered, meaning that no common conceptual frame of reference exists to support the research field of process innovation. Consequently, the theory building on innovation in the context of i4.0 is still fragmented and in its infancy, in spite of the rapidly increasing interest in this empirical phenomenon. To advance this domain of research, the current study unites and synthesizes existing research on process innovation in learning factories. Based on the findings of the literature review, the initial work of a reference model for process innovation in learning factories is presented.

Introduction

Industrial revolutions refer to paradigm shifts in industrial production, often caused by technology leaps. Thus far, three paradigm shifts have occurred and thereby also three industrial revolutions. Currently, the manufacturing industry is moving towards a fourth paradigm shift, which is generally recognized as the fourth industrial revolution or Industry 4.0 (i4.0). [START_REF] Lasi | Industry 4.0[END_REF] The overarching vision of i4.0 is to have intelligent end-toend processes enabled by autonomous decision-making and cyber-physical systems throughout the supply chain [START_REF] Colli | Contextualizing the Outcome of a Maturity Assessment for Industry 4.0[END_REF][START_REF] Qin | A Categorical Framework of Manufacturing for Industry 4.0 and Beyond[END_REF]. The implementation of i4.0 is expected to be enabled by a combination of Internet technologies, and "smart" machine and product technologies [START_REF] Lasi | Industry 4.0[END_REF]. Despite agreement on the potential for using i4.0 technologies to make technological advancements in the production [START_REF] Erol | Tangible Industry 4.0: A Scenario-Based Approach to Learning for the Future of Production[END_REF] and improve the competitiveness of the company [START_REF] Un | Types of R&D Collaborations and Process Innovation: The Benefit of Collaborating Upstream in the Knowledge Chain[END_REF], the implementation of innovative and valuable i4.0 solutions in industry is highly complicated, and requires a dedicated focus on developing new skills and competences [START_REF] Erol | Tangible Industry 4.0: A Scenario-Based Approach to Learning for the Future of Production[END_REF][START_REF] Lassen | Labour 4.0: Developing Competences for Smart Production[END_REF]. Creating process innovation by the use of i4.0 technologies is complex and abstract for companies [START_REF] Erol | Tangible Industry 4.0: A Scenario-Based Approach to Learning for the Future of Production[END_REF] among other things due to lack of understanding of the potential caused by radical changes as a result of a shift in paradigm [START_REF] Abele | Learning Factories for Future Oriented Research and Education in Manufacturing[END_REF]. Furthermore, the speed of the technological development is increasing, which means that companies need to both learn about and implement new technologies faster than ever before to stay competitive. Therefore, existing learning methods related to i4.0 technologies may be too time consuming while they, at the same time, may restrict the participants' creativity and thereby the potential for achieving innovative solutions. In order to rapidly develop new knowledge on i4.0 technologies and construct innovative solutions on the basis of these technologies, companies may benefit from being more experimental in their learning approaches. At the same time, companies need to enter into new collaborations with e.g. supply chain partners to fully exploit the potential of i4.0. Consequently, new and faster learning methods which support the development of i4.0 manufacturing systems are required [START_REF] Lasi | Industry 4.0[END_REF]. Several examples exist from both academia and industry where learning factories are used to train i4.0 solutions and thus development of i4.0 manufacturing systems [START_REF] Wagner | The State-of-the-Art and Prospects of Learning Factories[END_REF][START_REF] Andersen | Engineering Education in Changeable and Reconfigurable Manufacturing: Using Problem-Based Learning in a Learning Factory Environment[END_REF]. However, we argue that the concept of learning factories has a broader usefulness for conceptualizing the development of generic innovation methodologies for i4.0 manufacturing systems. Since research on this topic is scarce and fragmented, a common frame of reference is needed in order to create a common conceptual understanding of the research field. Reference models seek to create common understandings of e.g. a research field [START_REF] Fettke | Referenzmodellierungsforschung[END_REF]. Therefore, the objective of this research is to present the initial work of a reference model, which can provide a common understanding of the use of learning factories to support process innovation in relation to i4.0. This is achieved by conducting a literature review, which seeks to answer the following research question: How do learning factories support process innovation in i4.0?

The paper is structured as follows: The applied research methodology is presented in section 2. Afterwards, the findings of the literature review are presented in section 3 and significant conclusions for establishing a reference model are emphasized. Lastly, findings and conclusion on the research are presented in section 4.

Methodology

A structured literature review was made which addresses the research question: How do learning factories support process innovation in i4.0?

The initial applied search string was: "learning factory" OR "learning factories". Additionally, an initial search on innovation in learning factories was made. The chosen database was Web of Science, which ensures the academic standards of chosen publications. The search provided 192 hits. Upon first round of revision of the material, it became clear that most of the retrieved research focused on learning factories in education environments and that only limited research was related to industry participation. This led to initiation of a second round of literature search, which focused on capturing research on the topic, which could potentially have applied an adjacent terminology to learning factories. Combinations of several terms related to the research objective were tested. The terms were found in papers related to the research topic and through inputs from experienced researchers in the fields of robotics and automation, manufacturing systems, and operations development. It was chosen to broaden the scope of the literature topics to be included in this research. Leavitt's Diamond model from the 1960's was used as inspiration for structuring both the literature search and the subsequent analysis. The model was originally used for describing and analyzing organizations based on four parameters: Structure, technology, people, and task [START_REF] Jensen | Leavitt-Ry -Modellen Med De Mange Muligheder. Decade[END_REF]. Through the four parameters, process innovation in learning factories was investigated in relation to the technologies in center of the innovation (technology), the people who learn by using the learning factory (people), the tasks these people do in relation to the innovative technology (task), and the applied learning process (structure) to answer the research question. Three search strings for each of the four parameters were constructed to search for literature. Only papers published after 2008, written in English language, and with a subject related to i4.0 innovation in learning factories in the manufacturing industry were included. This means that e.g. research on learning factories in education environments was excluded. Based on these criteria, the three most cited articles for each search string were selected for analysis, thus 36 articles have been selected in total.

Literature Review

This section presents the analysis of the papers retrieved through the literature search. Each analysis is rounded off with a summary of those findings which are of particular relevance to process innovation learning factories.

Structure

Technological process innovation is important for manufacturers' competitiveness in the market [START_REF] Un | Types of R&D Collaborations and Process Innovation: The Benefit of Collaborating Upstream in the Knowledge Chain[END_REF][START_REF] Hollen | The Role of Management Innovation in Enabling Technological Process Innovation[END_REF]. Consequently, learning approaches which boost innovation in manufacturing are crucial [START_REF] Abele | Learning Factories for Research, Education, and Training[END_REF]. Learning factories are applicable of providing the format and methodologies for this, since learning factories improve the learning productivity [START_REF] Abele | Learning Factories for Future Oriented Research and Education in Manufacturing[END_REF]. Several learning factories have been installed at universities and research institutions for research, education and/or industrial training [START_REF] Wagner | The State-of-the-Art and Prospects of Learning Factories[END_REF][START_REF] Wagner | Learning Factory for Management, Organization and Workers' Participation[END_REF][START_REF] Schallock | Learning Factory for Industry 4.0 to Provide Future Skills Beyond Technical Training[END_REF]. However, the learning processes in learning factories and their objectives differ. Wagner et al. [START_REF] Wagner | The State-of-the-Art and Prospects of Learning Factories[END_REF] for example studied learning factories focusing on changeable and reconfigurable manufacturing systems and concluded that only few learning factories have changeability characteristics and thus may be used to develop innovative solutions in regard to manufacturing system changeability and reconfigurability. Schallock et al. [START_REF] Schallock | Learning Factory for Industry 4.0 to Provide Future Skills Beyond Technical Training[END_REF] found that more learning factories are being oriented towards i4.0 topics. The learning factory at Ruhr-Universität Bochum focuses on resource efficiency, management and organization, and process optimization to improve the workers' ability to accommodate changes in the production [START_REF] Wagner | Learning Factory for Management, Organization and Workers' Participation[END_REF]. This corresponds to the findings of Abele et al. [START_REF] Abele | Learning Factories for Research, Education, and Training[END_REF] who discovered that learning factories are often used to develop industry participants' ability to manage complex and unfamiliar situations. An example of a learning process related to i4.0 is described by Schallock et al. [START_REF] Schallock | Learning Factory for Industry 4.0 to Provide Future Skills Beyond Technical Training[END_REF]. The training in the learning factory consists of two parts: One for the high-level managers and one for the line managers. The high-level managers are first coached and prepared for leading the change process of implementing i4.0. Afterwards, the line managers have two trainings. The first is a basic training, which introduces general principles needed for the second part. The second training focuses on i4.0 technologies and the implementation of these in production processes.

After completing the training in the learning factory, the organization receives guidance and coaching on-site in the following six months. Another example is the learning process at the learning factory at Ruhr-Universität Bochum, which is centered around business games and simulations of real production processes [START_REF] Wagner | Learning Factory for Management, Organization and Workers' Participation[END_REF]. Tisch and Metternich [START_REF] Tisch | Potentials and Limits of Learning Factories in Research[END_REF] present seven success factors for methodical modelling of learning processes and make suggestions for how each of the seven factors can be addressed through learning factories to build successful learning factories.

The literature furthermore indicates that different types of collaboration have a positive influence on learning. According to Macher and Mowery [START_REF] Macher | Measuring Dynamic Capabilities: Practices and Performance in Semiconductor Manufacturing[END_REF] manufacturers in the semiconductor industry that use diverse teams for problem-solving and co-locations, which strengthens the relations between employees in manufacturing and development, learn faster. Additionally, Un and Asakawa [START_REF] Un | Types of R&D Collaborations and Process Innovation: The Benefit of Collaborating Upstream in the Knowledge Chain[END_REF] found that a R&D collaboration between a manufacturing company and a university positively affects process innovation at the manufacturing site, which also supports the findings of Abele et al. [START_REF] Abele | Learning Factories for Future Oriented Research and Education in Manufacturing[END_REF] who found that complexity and costs of translating research results into operations can be costly for especially smaller companies.

The main goal of learning factories differs based on their application. For research purposes the main goals are often technological and/or organizational innovation, whereas for education and training of industry purposes, the main goal is to effectively develop competences. [START_REF] Abele | Learning Factories for Research, Education, and Training[END_REF] Though, according to Abele et al. [START_REF] Abele | Learning Factories for Future Oriented Research and Education in Manufacturing[END_REF] learning factories would benefit from having a closer relation to innovation.

Based on this, it can be concluded that the use of learning factories differs depending on whether the purpose is related to industry or research. Currently, the use of learning factories to support innovation is related to the research domain, which however indicates that learning factories may be used to create process innovation in industry. Furthermore, there are indications that collaborations between industry and universities have a positive impact on process innovation.

Technology

New, innovative technologies are introduced with ever-increasing frequency, which gives manufacturing companies new possibilities for improving production processes. However, as mentioned before this also sets requirements for rapid knowledge acquisition and solution design for future manufacturing systems. [START_REF] Madsen | The AAU Smart Production Laboratory for Teaching and Research in Emerging Digital Manufacturing Technologies[END_REF] Learning factories support this process by focusing on new technologies and methods in their learning goals. Some learning factories use a holistic view by exploring topics such as i4.0 technologies and methods [START_REF] Küsters | Textile Learning Factory 4.0-Preparing Germany's Textile Industry for the Digital Future[END_REF][START_REF] Karre | Transition Towards an Industry 4.0 State of the LeanLab at Graz University of Technology[END_REF], digital solutions [START_REF] Küsters | Textile Learning Factory 4.0-Preparing Germany's Textile Industry for the Digital Future[END_REF], cyber physical production systems [START_REF] Karre | Transition Towards an Industry 4.0 State of the LeanLab at Graz University of Technology[END_REF] and Internet of Things technologies [START_REF] Lensing | Designing a Curriculum for the Internet-of-Things-Laboratory to Foster Creativity and a Maker Mindset within Varying Target Groups[END_REF][START_REF] Gronau | Development of the Industrial IoT Competences in the Areas of Organization, Process, and Interaction Based on the Learning Factory Concept[END_REF] whereas other learning factories focus on specific technologies and methods. Examples of these are industrial robots [START_REF] Madsen | The AAU Smart Production Laboratory for Teaching and Research in Emerging Digital Manufacturing Technologies[END_REF], logistics systems [START_REF] Blöchl | Simulation Game for Intelligent Production logistics-The PuLL® Learning Factory[END_REF][START_REF] Matt | Mini-Factory-a Learning Factory Concept for Students and Small and Medium Sized Enterprises[END_REF], digital twins [START_REF] Uhlemann | The Digital Twin: Demonstrating the Potential of Real Time Data Acquisition in Production Systems[END_REF], and agility in operations [START_REF] Karre | Learn how to Cope with Volatility in Operations at Graz University of Technology's LEAD Factory[END_REF]. The main focus in current use of learning factories is related to specific technologies and methods and only little attention is given to develop conceptual understandings of i4.0 in spite of it being a new manufacturing paradigm which may therefore require development of an overall understanding of i4.0 manufacturing systems.

People

Competence development is a recognized target of learning factories [START_REF] Tisch | Learning Factory Design: A Competency-Oriented Approach Integrating Three Design Levels[END_REF], which for instance should prepare industry participants for successful implementation of innovative i4.0 technologies [START_REF] Prinz | Learning Factory Modules for Smart Factories in Industrie 4.0[END_REF]. The employees in focus are e.g. shop-floor workers [START_REF] Cachay | Developing Competencies for Continuous Improvement Processes on the Shop Floor through Learning Factories-Conceptual Design and Empirical Validation[END_REF], engineers from areas of expertise related to production and employees from small and medium sized enterprises since they need information and innovative solutions which fit their corporate size and needs [START_REF] Hummel | Competence Development for the Holistic Design of Collaborative Work Systems in the Logistics Learning Factory[END_REF][START_REF] Issa | Mobilizing SMEs Towards Industrie 4.0-Enabled Smart Products[END_REF]. According to Tisch et al. [START_REF] Tisch | A Systematic Approach on Developing Action-Oriented, Competency-Based Learning Factories[END_REF] most existing learning factories' industrial target is engineers. However, possible target groups may also be i4.0-specific roles, which have not yet been created [START_REF] Enke | Industrie 4.0 -Competencies for a Modern Production System[END_REF]. Participants are primarily trained in homogeneous groups [START_REF] Tisch | A Systematic Approach on Developing Action-Oriented, Competency-Based Learning Factories[END_REF], though to reflect real workplace situations, Tisch et al. [START_REF] Tisch | A Systematic Approach on Developing Action-Oriented, Competency-Based Learning Factories[END_REF] propose to design learning processes which also focus on heterogeneous target groups. According to the findings of Macher and Mowery [START_REF] Macher | Measuring Dynamic Capabilities: Practices and Performance in Semiconductor Manufacturing[END_REF] presented in section 3.1, using heterogeneous groups may also imply faster learning. Participation in learning factory learning processes does not necessarily imply improved competences for the participants [START_REF] Tisch | Learning Factory Design: A Competency-Oriented Approach Integrating Three Design Levels[END_REF]. Therefore, in an attempt to avoid this problem, Enke et al. [START_REF] Enke | Competency-Oriented Design of Learning Modules[END_REF] suggest to compare the companies' actual state to the wished target state and based on this, design competence-oriented learning processes.

Based on this, it can be concluded that current research focuses on developing shopfloor employees' competences so that they are capable of implementing and operating i4.0 solutions and only limited attention is given to the development of management.

Task

Through learning factories, employees from industry should get hands-on experience with innovative i4.0 technologies. However, to achieve this, suitable learning methods and tasks must be applied. [START_REF] Wank | Using a Learning Factory Approach to Transfer Industrie 4.0 Approaches to Small-and Medium-Sized Enterprises[END_REF][START_REF] Schuh | Promoting Work-Based Learning through Industry 4.0[END_REF] Therefore, Schuh et al. [START_REF] Schuh | Promoting Work-Based Learning through Industry 4.0[END_REF] studied factors relevant for creating a learning process, which increases the efficiency for participants to learn innovative i4.0 characteristics. Several learning processes put emphasis on training employees in the implementation of innovative technologies (see e.g. [START_REF] Faller | Industry 4.0 Learning Factory for Regional SMEs[END_REF][START_REF] Kreimeier | Holistic Learning Factories -A Concept to Train Lean Management, Resource Efficiency as Well as Management and Organization Improvement Skills[END_REF][START_REF] Kreitlein | Green Factories Bavaria: A Network of Distributed Learning Factories for Energy Efficient Production[END_REF][START_REF] Merkel | Teaching Smart Production: An Insight into the Learning Factory for Cyber-Physical Production Systems (LVP)[END_REF]) through practical, hands-on application, which is often combined with theoretical lectures. For instance, in the learning process described by Prinz et al. [START_REF] Prinz | Implementation of a Learning Environment for an Industrie 4.0 Assistance System to Improve the overall Equipment Effectiveness[END_REF], the participants start with a theoretical introduction to i4.0 and assistance systems. This is followed by several hands-on simulations starting with a complex production with no assistance system and ending with a fully integrated assistance system to show the participants the differences. The learning process ends with a best-practice tour. [START_REF] Prinz | Implementation of a Learning Environment for an Industrie 4.0 Assistance System to Improve the overall Equipment Effectiveness[END_REF] Several other learning factory concepts have also used simulation tasks (see e.g. [START_REF] Erol | Tangible Industry 4.0: A Scenario-Based Approach to Learning for the Future of Production[END_REF][START_REF] Seitz | Cyber-Physical Production Systems Combined with Logistic Models -A Learning Factory Concept for an Improved Production Planning and Control[END_REF]) where the main objective is to transfer knowledge on innovative technologies from research institutions to industry.

Consequently, it can be concluded that there is a tendency to use predefined simulation tasks for participants to learn about i4.0 and not using tasks which support innovation.

Findings and Conclusion

As this research shows, research on process innovation in learning factories is scarce. Consequently, to support future research in this area, a common frame of reference needs to be established. The findings of the literature review are used to construct a discussion on using learning factories to support the creation of process innovation, and thereby introduce the initial work of a reference model in this section. The vision of an i4.0 factory consists of integrated seamless processes, which are able to efficiently control the increased complexity resulting from accommodating customers' unique requirements. Modularity, flexibility, complexity management, data exchange, and i4.0 technologies are examples of tools and methods, which support the development of the factory of the future. However, i4.0 is not limited to the implementation of isolated innovative methods and technologies, but is a new paradigm that requires manufacturers to see i4.0 from the perspective of the whole manufacturing system and its supply chain. Consequently, to avoid sub-optimizing individual production processes, the company's management needs to acquire a conceptual understanding of the i4.0 paradigm to use as guiding principle for individual i4.0 development projects. i4.0 learning factories could be used to develop this conceptual understanding. However, from this research it is evident that the current research focus in learning factories is to transfer knowledge on innovative i4.0 technologies to implement these in the production and not to develop a conceptual understanding of the i4.0 paradigm and support process innovation. Current focus is on employees who implement and operate the technologies and as a result of this a managerial perspective is lacking. Therefore, compared to existing learning factories, learning processes need to also support the generation of the conceptual understanding of i4.0 by using a management perspective. Additionally, the findings indicate that simulation is a frequently used method in learning factories which means that focus is on making knowledge transfer from research institutions to industry participants on i4.0 technologies and methods. However, if learning factories should support process innovation, this would require tasks and learning processes which encourage the participants to experiment and be creative.

A learning factory for process innovation distinguishes itself from existing learning factories because of its main focus being to create innovation in industry. Consequently, with an offset in the findings of this research we can describe the characteristics of a process innovation learning factory which stand out from existing use of learning factories. A process innovation learning factory can, therefore, be described as a learning factory which supports process innovation in industry through tasks that may be experimental and encourage creativity compared to existing learning factory approaches. Furthermore, to succeed, these learning factories should support the long-term and visionary development of i4.0 manufacturing systems, and thus focus on development of a conceptual understanding of i4.0 besides focus on individual i4.0 technologies and methods which are already used in learning factories today.


Adalberto Polenghi
email: adalberto.polenghi@polimi.it*

Irene Roda
email: irene.roda@polimi.it

Marco Macchi
email: marco.macchi@polimi.it

Paolo Trucco
email: paolo.trucco@polimi.it

Risk Sources Affecting the Asset Management Decision-Making Process in Manufacturing: A Systematic Review of the Literature

Keywords: Risk management, Asset risk management, Risk sources, Asset Management, Manufacturing

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Asset Management (AM) as discipline and business process is recently at the centre of the scientific and industrial debate. In fact, AM has been climbing top management's priority list, having a special concern in physical assets [START_REF] Frolov | Identifying Core Functions of Asset Management[END_REF], i.e. those assets that exist independently from any contract, as opposed to financial assets [START_REF] Amadi-Echendu | What is engineering asset management[END_REF]. During its development, the AM system view promoted a holistic approach, leading to more attention to strategic, risk, safety and environment, as well as human factors [START_REF] Amadi-Echendu | What is engineering asset management[END_REF][START_REF] Schuman | Asset life cycle management: towards improving physical asset performance in the process industry[END_REF][START_REF] Mitchell | Physical asset management handbook[END_REF]. Four founding principles are also remarkedi.e., life cycle, system, risk, and assetcentric orientationas levers to set an AM system within an industrial organisation [START_REF] Roda | A framework to embed Asset Management in production companies[END_REF].

While remarking the holistic approach, value creation has recently emerged as another essential concept to the purpose of AM. Indeed, AM embraces different kinds of actors that together aim at realising value by managing assets through coordination and in alignment with the organisational strategy [START_REF]Asset management -Overview, principles and terminology[END_REF]; accordingly, "effective control and governance of assets … is essential to realize value … to achieve the desired balance of cost, risk and performance" [START_REF]Asset management -Overview, principles and terminology[END_REF]. In order to achieve such a balanced cost, risk and performance, the focus on value in asset-related decisions is remarked by the most recent discussion on value-based AM [START_REF] El-Akruti | The strategic role of Engineering Asset Management[END_REF][START_REF] Hastings | Physical Asset Management[END_REF][START_REF] Roda | A Framework for Implementing Value-Based Approach in Asset Management[END_REF]. Different application fields/sectors advocated the adoption of AM and, specifically, of an AM system. The establishment of AM as a business process was initially evident in the mid-90s in the oil and gas sector; later on, infrastructure and distributed network systems have progressively aimed to introduce AM in their core processes; manufacturing is, nowadays, still lagging behind the achievements in the other sectors, especially discrete manufacturing.

Notwithstanding the application sector, and the relative AM maturity within it, a common understanding is that a proper and efficient AM requires to set up an effective decision-making process, capable of supporting asset-related decisions through all the lifecycle phases of an asset [START_REF] Ouertani | Towards an approach to select an asset information management strategy[END_REF]. In this perspective, risk management plays an important role in improving the decision-making process within AM [START_REF]Asset management -Overview, principles and terminology[END_REF]. Risk management has, in fact, a huge impact on the correct setup of a well-performing decisionmaking process [START_REF] Lu | Risk Management in Decision Making[END_REF]. However, within the AM field, asset-related decisions and decision-making are not particularly supported by practical guidelines [START_REF] Schuman | Asset life cycle management: towards improving physical asset performance in the process industry[END_REF][START_REF] Mitchell | Physical asset management handbook[END_REF] in accordance with risk management. For this reason, the present work focuses on the relationships between the decision-making process within AM and the related risks. More specifically, the aim is the identification of possible risk sources affecting the decision-making process within AM, while particular attention is put on manufacturing companies, considering how the general concepts and principles of AM are currently implemented in this business context.

From a research perspective, this work fosters the importance of risk management, with the aim of enhancing its role in AM decision-making; from a practical perspective, the paper gives hints to asset managers about what risk sources should be considered for implementing a risk-informed decision-making process.

The paper is so structured: Section 2 describes the reference framework for AM risk sources, proposed as a background, and built by integrating the risk-orientation principle to the lifecycle-orientation; Section 3 deals with the systematic literature review methodology, and it describes how the framework is fulfilled based on the literature findings; Section 4 proposes the results of the literature review, then Section 5 states some conclusions and work limitations for future improvements.

Framework for Asset Management risk sources

This work assumes a reference framework for the analysis of the most impactful risk sources for the AM decision-making process. The framework is composed of two dimensions: asset-related decisions and operational risk sources (see Fig. 1).

The first dimension includes the asset-related decisions mapped against each lifecycle phase, see the bottom part of the scheme in Fig 1 . This dimension adopts an asset user perspective, so it does no include decisions as pricing, or maintenance service provision offering. The currently included decisions are related to evaluation of alternatives and suppliers, maintenance strategy definition, and budgeting for the BoL phase of the asset. The MoL phase is related to the operation of the asset, thus including operations and maintenance management decisions, and reconfiguration decisions to adapt the asset to everchanging production conditions. At the end of the asset lifecycle (EoL), the main decision is related to the selection of decommissioning strategies. For more insights into the asset-related decisions model, refer to [START_REF] Roda | TCO Evaluation in Physical Asset Management: Benefits and Limitations for Industrial Adoption[END_REF].

The second dimension considers the operational risk model adopted to classify the risk sources and the respective risk categories, having as major concern the physical assets and their management process. The operational risk model adopted for the analysis is the one proposed by [START_REF] Silvestri | On the anatomy of operational risk[END_REF]: the risk model considers as a basic concept the definition of a risk source as "any entity or circumstance with the potential to generate uncertain conditions"; it is used as a reference to map risk sources found out from the systematic literature review.

Correspondingly, at the top of Fig. 1, the scheme of the operational risk model, as adopted in this research, is presented. Therein, the AM process is heading towards the AM objectives; however, the achievement of such objectives is affected by different risk sources that impact on the AM process. In particular, the impact of the different risk sources is identified as the variation in reaching the AM objectives. Four different risk source categories are identified, which represent different types of risk sources, both internal and external to the company:

• Core risk sources belong to the internal risk category and are associated with the core processes of the company, which is a process that directly contributes to the value creation according to the company's objectives. In the context of the present study, the AM process is the core process of interest.

• No-core risk sources belong to the internal risk sources and are associated with the no-core processes of the company. A no-core process is a process that is considered ancillary with respect to the core process and supports it in value creation. For example, spare parts management is an ancillary process, relevant to support the AM process towards the generation of value from physical assets. • Supply chain risk sources belong to external risk sources with respect to the AM decision-making process and are associated with the organisation in a broader view with respect to the core and no-core risk sources, which are internal. These risks stem from the interaction with suppliers. This may occur with the Original Equipment Manufacturers (OEMs) providing new assets, the OEMs/service providers supporting maintenance services, and the MRO (Maintenance Repair Operations) material suppliers. • Environment risk sources belong to external risk sources and are associated with the environment surrounding the company and influencing the core process as well. Environment does not mean only natural events, but all the set of geographical, political, social, and cultural factors that could influence, as contextual factors, the core process. For example, new legal requirements or the lack of skilled workforce may influence the AM process.

The focus of this research work is on the second dimension of the framework (i.e. operational risk sources, the top part of the scheme of Fig. 1). A systematic literature review is applied to analyse scientific literature dealing with risk and AM in manufacturing, as described in Section 3. Then, Section 4 presents the mapping of risk sources against risk source categories.

Literature review methodology

A systematic literature review is carried out to explore risk within AM in manufacturing industries; in particular, the literature search is performed to look for the risk sources affecting the decision-making process.

The literature review is pursued looking at works present in databases as Web of Science (WoS), Scopus and Google Scholar. In particular, it is done considering the following features:

• adopting a comprehensive search in title, abstract and keywords;

• keywords used are Asset Management AND Risk AND Manufacturing;

• English documents are the only ones considered.

The literature search finds out 985 documents (16 in WoS, 189 in Scopus, and 780 in Google Scholar). After applying the elimination of all non-English written documents and filtering according to title and abstract, the final list is composed of 27 works. The bottleneck criterion is the one related to the screening phase because, even though manufacturing is introduced in the keywords, most of the documents, especially in Google Scholar, deal with risk and/or assets in the financial sector. Then, the final list of 27 documents is further screened through full paper readings to understand if each paper analyses some risk sources in the manufacturing sector.

Considering the above findings, it has to be noticed that the main limiting criterion of the literature review adopted so far is the confinement within the manufacturing sector: most of the documents treat risk within AM in infrastructure and distributed networks. Thus, even though the systematic literature review methodology is adopted to look in depth at the scientific documentation, the results are not completely satisfactory, since the number of papers after the last screening phase is small. To overcome this problem, additional literature is introduced to better feed the proposed framework: it comes from the background of AM in the scientific community, and it also considers the ISO 5500x body of standards, which gives some hints about risk in AM.

Thus, the risk sources found out in the scientific documents are classified according to the risk source categories therein discussed (Section 4). More specifically, the risk sources are derived by the analysis of the literature: the eligible papers clearly state the authors are addressing specific risks, even though usually they do not refer to specific decisions. As a consequence, with this information it is possible to complete one dimension of the framework, the one dealing with risk sources (top part of Fig. 1), mapping them against risk source categories, defined while going through the literature analysis.

Risk sources against risk source categories

The framework of risk sources versus risk sources categories is fed as a result of the step of full paper reading using the 27 works selected from the systematic literature review. The analysis of the articles allows either to understand which kinds of risk sources are highlighted in the scientific literature, either to associate them with the relative risk source categories. The analysis is firstly done considering the eligible papers; then, this set is enlarged thanks to additional literature (highlight by an * in Table 1) that has grounded the basics of AM, such as the ISO 5500x body of standards. For the sake of transparency, if the risk source is not derived from the eligible documents, a * is put next to the risk source; instead, if the risk source is identified in both eligible and not eligible documents, a (*) is used. Thus, Table 1 proposes the results of this analysis.

The analysis of the risk identified in literature allows to classify them according to the framework proposed in Fig. 1 (column of Table 1), and to group them into risk sources (rows of Table 1), which could be:

• Equipment: risk source related to machines, components or systems that could fail or, somehow, affect the possibility to achieve the desired AM objectives; • Information management: risk source associated with the way information is gathered and managed; • Human factor: risk source connected with leadership, culture, motivation, behaviour and competence within the organisation; • Organisational architecture: risk source related to the organisational structure; • Supplier: risk source associated with the suppliers of the organisation that could, in some way, affect reaching AM objectives.

The main finding of this analysis is the importance of considering information management as the most impactful risk source on the AM process. However, the second place is taken by the human factor, underlining the need for knowing and understanding AM principles to correctly manage systems of assets in the long-term perspective.

Conclusions

The present study focused on the analysis of risk sources to be taken into account within the decision-making processes supporting the implementation of AM in the manufacturing sector. The aim is to promote a risk-informed decision-making process tackling the lifecycle perspective of an asset, thus being aligned to the AM principles of risk-and lifecycle-orientation [START_REF] Roda | A framework to embed Asset Management in production companies[END_REF]. The main findings of the work are expected to bring a contribution both to researchers and to practitioners.

From a scientific perspective, the work fosters the importance of risk management in asset-related decisions. The main finding of the analysis is that information management covers a primary role as the most critical risk source in asset-related decisions. Furthermore, the culture of AM, identified by the knowledge ecosystem, seems to play an important role as a risk source, since the knowledge and capabilities not merely related to the technical aspects of AM, but also to the managerial ones, could promote and make effective and successful the decisions.

From a practical standpoint, the proposed framework (Table 1) will enhance the possibility of asset managers to correctly reflect on the decision drivers and the associated risks. This will allow them to take preventive actions in this regard.

Clearly, the present work is a starting point to bring forward new research work. In particular, the proposed classification should be completed, with the aim to map the risk sources against asset-related decisions in the lifecycle, as defined in the reference framework (Fig . 1). This additional analysis could make the proposed framework more valuable for both academia and industry since it creates a direct relationship between each risk source and each asset-related decision. In so doing, a more risk-informed AM decision-making process could be established, and the asset user will be aware of possible misbehaviour of this process.

Moreover, the research developed in this work and its results are limited to the needs and peculiarities of the AM in manufacturing. Another limitation of the review regards the explicit exclusion of maintenance as the scope of the literature review, even if it could be used as an exploratory field to enlarge the collection of research works that address the risk orientation as a precursor of AM [29]. These limitations may stimulate a wider scope of the systematic literature review in future works.

Fig. 1 .

 1 Fig. 1. Framework for Asset Management risk sources.

Table 1 .

 1 Classification of risk sources against risk source categories

		Core risk sources	No-core risk sources	Supply chain risk sources	Environment risk sources
		[15]	[15,16]
	Equipment	Security	Security Failure
		[17][6,26-28]*	[17,18,20][7,27,28]*	[20,21]	[22,23]
		Information track-	Information tracking
	Information management	ing (*) Information availa-bility* Data collection*	(*) Data tracking (*) Data security Data collection*	Data security Information se-curity	Information security Data security External hacking
		Data management*	Data management*
		[16,24][6,29]*	[19,24][29]*
	Human factor	AM knowledge lacking (*) Leadership* Motivation* Culture*	AM knowledge lacking Maintenance knowledge lacking
		[25][7]*
		Framework
	Organisational	guideline stating
	architecture	AM objectives
		misalignment*
		Responsibility allo-
		cation*
		[6]*		[28]*
	Supplier	Responsibility allo-cation*		Maintenance ac-tivities outsourc-ing*


Marlène Hildebrand
email: marlene.hildebrand@epfl.ch

Ioannis Tourkogiorgis

Foivos Psarommatis

Damiano Arena

Dimitris Kiritsis

A method for converting current data to RDF in the era of Industry 4.0

Keywords: Ontology, zero-defect manufacturing, data integration, RDF, semantics, JSON

In the past two decades, the use of ontologies has been proven to be an effective tool for enriching existing information systems in the digital data modelling domain and exploiting those assets for semantic interoperability. With the rise of Industry 4.0, the data produced on assembly lines within factories is becoming particularly interesting to leverage precious information. However, adding semantics to data that already exists remains a challenging process. Most manufacturing assembly lines predate the outbreak of graph data, or have adopted other data format standards, and the data they produce is therefore difficult to automatically map to RDF. This has been a topic of research an ongoing technical issue for almost a decade, and if certain mapping approaches and mapping languages have been developed, they are difficult to use for an automatic, largescale data conversion and are not standardized. In this research, a technical approach for converting existing data to semantics has been developed. This paper presents an overview of this approach, as well as two concrete tools that we have built based on it. The results of these tools are discussed as well as recommendations for future research.

Introduction

What might make Industry 4.0 a unique industrial revolution, is its capacity to rely on already existing infrastructures, just by interconnecting technologies. In that regard, the data produced by machines on assembly lines make for a particularly interesting case to investigate. Assembly lines in factories currently produce very large amounts of data, most of which is under-exploited, often simply stored to never be used again. At the same time, factories want to insure better interoperability between their services and tools. To do so, a more and more widespread approach is to add meaning to the data [START_REF] Verscheure | The Meaning of Data[END_REF] through the use of semantics, or to be more precise, through the use of ontologies, allowing for data to be machine-understandable, and understood the same way across diverse systems of information [START_REF] Martinez Lastra | Semantic Web Services in Factory Automation: Fundamental Insights and Research Roadmap[END_REF][3] [START_REF] Kádár | Semantic Virtual Factory supporting interoperable modelling and evaluation of production systems[END_REF]. The purpose of ontologies is to help mark-up data with meaningful metadata that will make it more easily interpretable. To formalize this new manner to structure data, a language named RDF (Resource Description Framework) [START_REF] Klyne | RDF 1.1 Concepts and Abstract Syntax[END_REF] has been developed, which expresses data in the form of triples subject-predicate-object, and where URIs (Uniform Resource Identifier) symbolize the instances. URIs help identify a specific instance, and link it to its attributes, or to other instances. What RDF allows us to do, is to formalize our data as a graph. This is a very powerful way of storing unstructured data, and quickly retrieve the connections between data, unlike SQL (Structured Query Language) joints that are very difficult to express and expensive.

But this raises one big issue. Graph data is fairly new and has only been widely adopted in certain niches, such as the market of social networks. In the meanwhile, relational data has remained the bread and butter of most data management systems for the past 20 years. To exchange and communicate data between different systems, formats such as JSON (JavaScript Object Notation), which relies on a key-value pair paradigm, have become widely popular. Therefore, the data produced by assembly lines is mostly structured as tables, key-value pairs or trees, and comes in formats such as SQL databases, XML (Extensible Markup Language) files, Excel sheets or JSON streams. Some of these formats are difficult to convert into graph data because of their very structure, such as SQL, and others, like JSON, may only be easy to translate in certain cases, depending on how well the schema of the data matches with the ontology.

Therefore, some pre-processing is required to be able to leverage this data, which involves mapping the existing schema of the data to the ontologies we have designed, and then implement this mapping in a computer program that will achieve an ETL (Extract-Transform-Load) procedure to in order to handle the data accordingly. This raises several questions: how do we map an existing data schema to an ontology, and how to we formalize it? And once we have formalized this mapping, how do we implement it in a tool in order to automatically convert the data?

As we will see in the next section, methodologies to map non-graph data schemas to graph data schemas, and more specifically to RDF, is a very active field of research, and several recommendations, languages and tools have already been created to achieve this mapping. However, no standard has been issued yet, and some of these tools have limitations. This brings us to the second issue, which is the one we would like to address in this paper: How do we then use these methodologies to implement a tool that can automatically convert data? How can we "generalize" this tool so that it can be applied to different data formats, different data schemas, and different ontologies? Such a tool, or at least a general conversion algorithm, would make the life of software developers easier, which is a crucial step for the more widespread adoption of RDF and ontologies.

Therefore, the current research aims to present a general, easy to implement algorithm, that can automatically convert data to RDF without being dependent on the original data schema or the targeted ontology. Section 2 describes the state of the art in data mapping. Section 3 describes the approach that was proposed for this research, and the tools that have been used. Section 4 describes two applications of this approach and Section 5 discusses the findings and their implications.

State of the art

Methodologies to convert non-graph data to graph data are still an ongoing field of research in the world of linked data and semantics, since the solutions to this issue could significantly increase the adoption of RDF. Mapping SQL data schemas to RDF has been a particular topic of focus, since most databases are relational. Even though the World Wide Web Consortium has issued no standard yet in that regard, it has issued a few recommendations. The most notable one deals with how to directly map SQL to RDF [START_REF] Sequeda | On Directly Mapping Relational Databases to RDF and OWL (Extended Version)[END_REF]. R2RML, a language for mapping SQL to RDF has also been issued and recommended by the Consortium [START_REF] Das | R2RML: RDB to RDF Mapping Language[END_REF].

Regarding on how to map JSON schemas with RDF, another language, JSON-LD (JavaScript Object Notation for Linked Data), has been created by members of the W3C [START_REF] Sporny | JSON-LD 1.1: A JSON-based Serialization for Linked Data[END_REF] and has gained quite a lot of attention due to its simplicity of use and its practicality. RML, an extension of R2RML, can also be used to map JSON and other formats to RDF [START_REF] Dimou | RML: A Generic Language for Integrated RDF Mappings of Heterogeneous Data[END_REF]. A language named GRDDL [START_REF] Connolly | Gleaning Resource Descriptions from Dialects of Languages (GRDDL)[END_REF] has been developed quite early on during the development of the Semantic Web in order to map XML to RDF, but it also possible to use it for the mapping of JSON to RDF. A language named XSPARQL has also been developed to query XML and RDF and to transform data from one format to the other [START_REF] Bischof | Mapping between RDF and XML with XSPARQL[END_REF].

As we can see, there are many methodologies and languages to map existing data schemas of all kinds of formats to ontologies. The questions then arise: how do we provide this mapping to a software tool, and how do we implement the conversion of the data? One way would be to simply use these methodologies as they are, and to explicitly implement the mapping in the code. There are several problems with this approach. First, explicitly hard-coding values in a program is bad practice in the world of software development, because every evolution of the data schemas will have to be implemented in the code as well. Also, the code wouldn't be reusable for different data schemas or different ontologies. Another issue is that in the case of assembly lines, the amounts of data to process and integrate are huge, and so are the ontologies we have to map them to. Explicitly coding the mapping therefore implies that each data will go through one test for each different field of the data schema, which is doable for small mappings, but would be a tedious work and come at a great cost of performance for really big ontologies and data schemas.

What we would like to achieve instead is to come up with a general conversion algorithm, one that would be able to automatically process the data based on a mapping we would have provided it, but that wouldn't need to be aware of the details of the ontology or of the original data schema, so that we would be able to feed it any mapping, or to adapt it to any data format in input. Something lightweight that could be easily implemented in different programming languages, which would have good performances regarding time and memory consumption, and that would be easy to understand and maintain for software developers.

Proposed approach

As stated previously, the intent behind this paper is to present a general implementation to automatically convert non-graph data to RDF through a software tool, without having to know the data schema or the ontology in advance, so that it could work with any mapping. We have tried to develop tools based on this implementation to convert data coming from XML files and JSON streams into RDF. Our idea was to develop a technique for mapping that would rely on an approach similar to what is currently done with configuration parameters for software tools. Instead of hard-coding the values of the configuration parameters in the software, they are usually stored in a separate file, under the form of key-value pairs. When the code is running, the value is retrieved through the key called in the code. This allows for an easy way to modify the parameters of a desktop application or a web service (e.g. the access path to a database) when necessary, without going through the expensive path of a code evolution.

Our approach regarding the formalization of the mapping was very similar in that we decided to provide it as a JSON file that the code could parse to retrieve the corresponding ontology concept for each data schema field (Fig. 1). One file would be dedicated to classes and another to the properties, since the process for creating a new class instance is not the same as the one for creating the property of an instance. In the JSON files, the fields of the XML or JSON data schema would be handled as the keys, would paired to the URIs of the classes or properties relationships they correspond to in the ontology (Fig. 2).

In the code of the software tool, two functions will need to be implemented: one for the creation of a new instance of a class, and another for the creation of a property of this instance. The first function will create a new URI and create an RDF triple expressing of which class this URI is an instance of. This triple will consist of this new URI as a subject, "rdf:type" as a predicate to indicate the "is an instance of" relationship, and the URI of a class as an object. The second function will create a triple that will assign a new property of a certain value to a certain class instance. To do so, it will create a triple that consist of the URI of the instance, the URI of the relationship to the property, and the value of said property. "Process": "http://.../ontology/class_000501" "AssemblyLine": "http://.../ontology/class_000502" ... "has_id_process": "http://.../ontology/prpt_000701" "has_ProcessName": "http://.../ontology/prpt_000702" ... "hasAssemblyLine": "http://.../ontology/prpt_000801" "hasStation": "http://.../ontology/prpt_000802" … ... of the object in the JSON file and retrieve the corresponding class URI, and would then create a new line in the Turtle file consisting of a triple for a new instance of a class. It would then read the attributes of this object, and for each attribute, lookup the corresponding property relationship URI in the JSON file to create a triple in the Turtle file comprised of the abovementioned class instance URI, the property relationship URI, and the value of the attribute in the XML sheet. The only issue that needed to be solved was how to link instances together, when a property was referring to an instance and not just a literal. As stated earlier, the solution was easy due to the structure of the XML file. Whenever the algorithm was creating a new instance, it would save this instance URI, and when reading a new object and creating its new URI, it would link these two URIs together by looking up the property relationship corresponding to the new object name in the property mapping file. Then the algorithm would be able to create a new triple with the previous instance URI as the subject, the relationship URI as the predicate, and the new instance URI as the object.

Zero-Defect Manufacturing

Another ETL tool has been developed in the context of the Z-Fact0r project. This project aims at improving the quality and the productivity of the European manufacturing industries through five multi-stage production-based strategies. These include: the early detection of the defect, the prediction of the defect generation, the prevention of defect generation as well as its propagation in later stages of the production, the reworking/remanufacturing of the product and finally the management of these strategies through event modelling, KPI monitoring and real-time decision support.

In this case, the original format was a JSON stream, therefore consisting of keyvalue pairs, and the created RDF triples would have to be stored in a triple store powered by Apache Fuseki 3 . In order to save the triples to the triple store, we used a library named DotNetRDF 4 . Each JSON object corresponded to the instance of a class, and certain key-value pairs corresponded to a property of this object. Others could be ignored.

If the overall algorithm remained the same, this time, attention had to be given to the fact that different JSON objects within the stream could refer to the same instance, and therefore it would be necessary to be able to retrieve the URI of a previously created instance. Our solution to this issue was to use the ID of the object as the URI to the corresponding instance. This way, when reading a new object, we would not need to know if it already existed or look for its ID: just by concatenating the base URI of the ontology and the value of the "id" key-value pair, we would be sure to refer to the correct instance.

Another issue was that the key-value pairs could refer to literal properties but also to other class instances. One way to handle that would be to test beforehand if the key does exist in the class mapping instead of the properties mapping, or to create a separate file for properties that refer to an instance of another class rather than just a literal value. Then the algorithm could distinguish this case and create an instance of the adequate class and link it to the object instance. But this raises a few questions as well. In our case, this approach worked because the value stored in these key-value pairs was the id of the instance, which we could use to create a new URI. But what if several values in the JSON object are related to this instance and not the overall object? Then it will not be possible anymore to apply a general treatment to the key-value pairs, and it will be necessary to create specific treatments for specific fields.

Conclusion

In this paper, a generic algorithm for the automatic conversion of different types of data into RDF has been presented, and it was demonstrated that it can contribute to quickly and easily building ETL processes for data that has to be transformed and described with RDF. The main contribution of this research is to propose a solution that doesn't require to hard-code the mapping, and that can be reusable for different data schemas and different ontologies, and that can be easily adapted to other data formats.

Nonetheless, as we have seen, this methodology does not function with every data schema in input. Ideally, for this generic treatment to be efficient, the data structure in entrance should follow the logic of the ontology, class instances and property value should be easily distinguishable. Also, the id of an object should be easily retrievable when referring to an already existing instance, in order to facilitate the work of developers. Otherwise, they will have to look for the instance in the already saved RDF triples, which would kill performance. These issues need to be addressed through future research and tools development.

  different formats : Excel sheets, XML files, relational databases, JSON streams... Operator creates a JSON file to map the data format to the ontology and feeds it to the application Generic application that transforms the data according to the mapping fileThe data produced is stored as RDF

Fig. 1 .

 1 Fig. 1. General architecture of the proposed solution

Fig. 3 .

 3 Fig. 3. Flowchart of the proposed algorithm

A syntax and file format to express data in RDF. https://www.w3.org/TR/turtle/

Free, open-source framework from Microsoft. https://dotnet.microsoft.com/download

HTTP interface to RDF data, developed as a servlet. https://jena.apache.org/documentation/fuseki2/

[START_REF] Kádár | Semantic Virtual Factory supporting interoperable modelling and evaluation of production systems[END_REF] .Net library for parsing, managing, querying and writing RDF. http://www.dotnetrdf.org/

Acknowledgments

The work presented in this paper is partially supported by the project Z-Fact0r which is funded by the European Union's Horizon 2020 program under grant agreement No 723906.

The next step is to create an algorithm using these functions. The first thing this algorithm will have to do is to parse the file or the stream it will receive in input. For each new object in the file, it will retrieve the JSON key or XML field that corresponds to this object, it will check if it corresponds to a class in the class mapping file, and if it is the case, create a new class instance. If the key or field corresponds instead to a property, the algorithm will retrieve the URI of the relationship in the property mapping file and link the URI of the object to the value in the file (Fig. 3). As we will see later in this paper, this approach works well granted that, in the case we are creating a property, we have a way to know which class instance it has to be linked to. This information is easy to retrieve depending on the original data schema, but it is also the only part of the algorithm that cannot be generalized.

4

Application cases

Semantic enhancement to ease aircraft design and lifecycle cost data integration

The first software tool we have developed based on this approach was for a project aimed at digitalizing the life cycle ground testing of aircrafts. The purpose of the tool was to be able to feed it an XML file containing the data to be processed to output a Turtle 1 file containing the original data but descried using RDF instead of XML. Our approach was particularly well adapted to this case, since the original XML data schema consisted of objects matching the ontology classes, and attributes matching the ontology properties. Also, objects that had to be linked together were imbricated in each other, which made it easier to link one instance to the other algorithmically. The tool we created for this use-case was developed in C# with the framework .Net Core 2.1 2 . We created two JSON files: one that mapped the names of the XML objects to the corresponding ontology class URIs, and another that mapped the XML attributes to the corresponding property URIs. The structure of the algorithm is the following: while reading the XML file, for each new XML object, the algorithm would look-up the name