The Industry 4.0 revolution relies heavily on data to generate value, innovation, new services, and optimize current processes [1]. Technologies such as Internet of Things (IoT), machine learning, digital twins, and much more depend directly on data to bring value and innovation to both discrete manufacturing and process industries. The origin of data may vary from sensor data to financial statements and even strictly confidential user or business data. In data-driven ecosystems, collaboration between different actors is often needed to provide services such as analytics, logistics, predictive maintenance, process improvement, and more. Data therefore cannot be considered a corporate internal asset only. Hence, data needs to be shared among organizations in a data-driven ecosystem for it to be used as a strategic resource for creating desired values, innovations, or process improvements [2]. When sharing business critical and sensitive data, the access to the data needs to be accurately controlled to prevent leakage to authorized users and organizations.  Access control is a mechanism to control actions of users over objects, e.g., to read, write, and delete files, accessing data, writing over registers, and so on. This thesis studies one of the latest access control mechanisms in Attribute Based Access Control (ABAC) for industrial data sharing. ABAC emerges as an evolution of the commonly industry-wide used Role-based Access Control. ABAC presents the idea of attributes to create access policies, rather than manually assigned roles or ownerships, enabling for expressive fine-granular access control policies. Furthermore, this thesis presents approaches to implement ABAC into industrial IoT data sharing applications, with special focus on the manageability and granularity of the attributes and policies.  The thesis also studies the implications of outsourced data storage on third party cloud servers over access control for data sharing and explores how to integrate cryptographic techniques and paradigms into data access control. In particular, the combination of ABAC and Attribute-Based Encryption (ABE) is investigated to protect privacy over not-fully trusted domains. In this, important research gaps are identified. / Arrowhead Tools

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The exploration of Smart Factories and Industry 4.0 technologies has indeed sparked curiosity and interest in the industrial world. The potential of these advancements to revolutionize manufacturing processes, enhance efficiency, and drive innovation is immense. However, there is a gap in research when it comes to the practical implementation of these advanced technologies in real-world production settings, especially in already established factories so-called Brownfield Factories. This thesis work was conducted within one such brownfield factory to comprehend the tangible challenges associated with transferring smart technologies. Within this specific company, a laboratory had already been established for testing novel smart technologies in the context of production and logistics. The aim in companies is to test smart technologies in a controlled environment without causing any disruption to the ongoing profit-generating production processes. This laboratory setup also serves the additional purpose of educating the personnel within traditional production facilities about the upcoming smart technologies in the market. The Lab showcases the potential of new and emerging technologies in addressing long-standing issues with a fresh perspective, thereby inspiring innovation. The central approach of this thesis revolves around the establishment of a standardized laboratory work process through which smart technology can be tested in a structured way. In this context, an illustrative example of a technology, namely "Virtual Training for Assembly Operators," was chosen as a case study to explore and comprehend the challenges associated with technology transfer. This case study also played a pivotal role in assessing the credibility of the standard technology transfer model formulated within the company. Notably, it was deduced that knowledge and competence are two key obstacles impeding the smooth transfer of technology. Building upon the insights garnered from the case study on virtual training technology and drawing from interviews with engineers and managers employed at the case company, a refined technology transfer process named the "Smart Factory Lab Process" was developed. This process aims to enable the effective transfer of smart technologies, informed by the lessons learned from the practical application of technology in real-world scenarios.

Industry 4.0 (I4.0) productions are complex environments driven by production data to make informed decisions affecting the events and items on the production line. This complexity can have a negative effect on the factory workers’ adoption rate of the new technology. More specifically, it can lead to the factory workers feeling passive and lacking influence over the tools used. Therefore, new UX methods and increased UX maturity are called for, to better suit the ever changing environments of I4.0 organizations. To ensure adoption, positive attitudes and intentions regarding user ownership, expertise and knowledge sharing are required. In this thesis project, participatory design (PD) methods are used to evaluate, whether PD has a positive effect on such attitudes and intentions toward new tools introduced on the production line. Five participants, employees from the I4.0 company Northvolt, were recruited to take part in a PD workshop to design a human-machine interface (HMI). The participants attitudes and intentions towards the tool were measured and explored through the PD workshop, surveys and user interviews. The outcome was also compared to the survey results on the tools already in use on the production line. The study resulted in increased positive attitudes and intentions towards user ownership, knowledge sharing and expertise concerning the HMI. Thus, the application of PD in I4.0 environments had an overall positive impact. Researchers are called to assess these effects in the long term, by allowing the participants to use the tool in a practical context overtime. / Industry 4.0 (4.0) produktioner är komplexa miljöer drivna av produktionsdata för att kunna göra informerade beslut som påverkar händelserna och produkterna på produktionslinjen. Denna komplexitet kan ha en negativ effekt på fabriksarbetarnas adoptionsfrekvens av den nya teknologin. Mer specifikt, kan det leda till att fabriksarbetarna känner passivitet och att de saknar inflytande över de digitala verktygen som används. För att bättre passa de föränderliga miljöerna i I4.0 organisationer, behövs nya User Experience (UX) metoder och ökad UX mognad. För att säkerställa adoption, positiva attityder och avsikter angående ”user ownership”, behövs expertis och kunskapsdelning. I detta examensprojekt, används ”participatory design” (PD) metoder för att evaluera om PD har en positiv effekt på sådana attityder och avsikter gentemot nya digitala verktyg introducerade på produktionslinjen. Fem deltagare, anställda från I4.0 företaget Northvolt, rekryterades för att ta del av en PD workshop för att designa ett ”human-machine interface” (HMI). Deltagarnas attityder och avsikter gentemot verktyget mättes och utforskades genom PD workshopen, enkäter och användarintervjuer. Utfallet blev jämfört med enkätresultat gällande digitala verktyg som redan används på produktionslinjen. Projektet resulterade i ökade positiva attityder och avsikter rörande user ownership, kunskapsdelning och expertis gentemot HMIt. Således, appliceringen av PD i I4.0 miljöer hade en övergripande positiv påverkan. Forskare uppmanas att bedöma dessa effekter långsiktigt, genom att tillåta deltagarna att använda det digitala verktyget i en praktiken över tid.

The advancement of technology has led to an increase in the demand for ultra-low end-to-end network latency in real-time applications with a target of below 10ms. The IEEE 802.1 Time-Sensitive Networking (TSN) is a set of standards that supports the required low-latency wired communication with ultra-low jitter for real-time applications. TSN is designed for fixed networks thus it misses the flexibility of wireless networks.To overcome this limitation and to increase its applicability in different applications, an integration of TSN with other wireless technologies is needed. The fifth generation of cellular networks (5G) supports real-time applications with its Ultra-Reliable Low Latency Communication (URLLC) service. 5G URLLC is designed to meet the stringent timing requirements of these applications, such as providing reliable communication with latencies as low as 1ms. Seamless integration of TSN and 5G is needed to fully utilize the potential of these technologies in contemporary and future industrial applications. However, to achieve the end-to-end Quality of Service (QoS) requirements of a TSN-5G network, a significant effort is required due to the large dissimilarity between these technologies. This thesis presents a comprehensive and well-structured snapshot of the existing research on TSN-5G integration that identifies gaps in the current research and highlights the opportunities for further research in the area of TSN-5G integration. In particular, the thesis identifies that the state of the art lacks an end-to-end mapping of QoS requirements and traffic forwarding mechanisms for a converged TSN-5G network. This lack of knowledge and tool support hampers the utilisation of ground-breaking technologies like TSN and 5G. Hence, the thesis develops novel techniques to support the end-to-end QoS mapping and traffic forwarding of a converged TSN-5G network for predictable communication.Furthermore, the thesis presents a translation technique between TSN and 5G with a proof-of-concept implementation in a well-known TSN network simulator. Moreover, a novel QoS mapping algorithm is proposed to support the systematic mapping of QoS characteristics and integration of traffic flows in a converged TSN-5G network. / PROVIDENT

This thesis develops a method for using machine learning in a industrial pro-cess. The implementation of this machine learning model aimed to reduce costsand increase efficiency of train wheel overhaul in partnership with the AustrianFederal Railroads, Oebb. Different machine learning models as well as categoryencodings were tested to find which performed best on the data set. In addition,differently sized training sets were used to determine whether size of the trainingset affected the results. The implementation shows that Oebb can save moneyand increase efficiency of train wheel overhaul by using machine learning andthat continuous training of prediction models is necessary because of variationsin the data set.

Объектом исследования являются cстатистические методы контроля производственных процессов. Предмет исследования – разработка ПО для статистического анализа производственных данных. В данном исследовании производится разработка программного обеспечения для контроля производственных процессов путём визуализации показателей датчиков оборудования методом контрольных карт Шухарта с использованием средства Jupyter Lab. / The object of this research are statistical production control methods. The subject of research is the development of an application for the statistical analysis of production process data. This research consists of developing software for production process control using visualization of machinery sensor data using Shewhart control charts with Jupyter Lab.

Nobel Biocare is a world-leading manufacturer of innovative dental solutions. The company has two production facilities in Karlskoga specified for producing dental implants and abutments to customers like dentists, dental technicians and their patients. Nobel Biocare has identified possibilities to improve its production processes using the concept of Industry 4.0. This thesis aims to assist Nobel Biocare by conducting a preparatory study investigating the suitability of implementing technologies from the concept of Industry 4.0 to improve company-specific processes. Suitability is based on a total of five criteria, which are: investment costs, production sustainability, monetary profit, effect on product quality and time savings in the production. The study should furthermore determine what demands are posed upon the company for this implementation to be successful. The thesis is treated as a project containing three major phases related to assessing the situation and identifying improvement areas, suggesting technologies from Industry 4.0 to improve these areas and assessing the suitability of the suggested improvements. The methods used were qualitative, consisting of informal and formal interviews with open-ended questions. This was complemented by internal and external documents and previous studies as well as observations. The thesis defined ten technologies as the core of Industry 4.0 and applied these to identified potential improvements within the company's processes. A total of twelve main areas of improvement were identified, highlighting waste, repetitive tasks, and other production related challenges. A total of 15 proposed solutions were formulated based on the identified improvement areas. Among these proposed solutions, Industry 4.0 technology automation was applied in seven of the areas, some sort of digitalisation was applied in four areas, Augmented Reality (AR) or Virtual Reality (VR) was applied in two areas and Artificial Intelligence (AI) was applied within two proposed solutions. A total of ten of the proposed solutions were found suitable based on suitability criteria and three were found somewhat suitable. Out of the suitable implementations, five were related to automation, two to digitalisation and one within VR and AR. Artificial Intelligence was found suitable in two of the proposed improvements. Some of the requirements which are to be posed upon the company in order for the implementation of Industry 4.0 technologies to be successful were furthermore determined. These requirements were in regards to network speed and capacity, education of employees, cybersecurity and data ownership. / Nobel Biocare är en världsledande tillverkare av innovativa dentala helhetslösningar. Företaget har två produktionslokaler i Karlskoga som tillverkar distanser och tandimplantat till kunder vilka utgörs av tandläkare, tandtekniker och deras patienter. Nobel Biocare har uppmärksammat möjligheten att förbättra nuvarande produktion med hjälp av konceptet industri 4.0. I detta arbete har en förstudie genomförts för att hjälpa Nobel Biocare att identifiera hur lämpliga olika tekniker från konceptet industri 4.0 är att implementera i företagets produktion utifrån ett antal lämplighetskriterier. Dessa lämplighetskriterier är: investeringskostnader, hållbarhet i produktionen, monetära vinster, effekter på produktkvalitet och tidsbesparingar i produktionen. Arbetet presenterar även ett antal krav som kommer ställas på företaget för att implementationen av teknikerna som arbetet identifierat som lämpliga ska vara framgångsrik. Arbetet behandlades som ett projekt och delades in i tre större projektfaser. Först utfördes en nulägesanalys för att identifiera förbättringsmöjligheter i den nuvarande produktionen. Därefter togs lösningar fram på de identifierade förbättringsmöjligheterna med hjälp av tekniker från konceptet industri 4.0. Slutligen utvärderades hur lämpliga de olika teknikerna var att implementera som lösningar på de identifierade förbättringsmöjligheterna. Arbetet har använt kvalitativa metoder där majoriteten av data samlats in från informella och formella intervjuer med öppna frågor. Datainsamlingen från intervjuer har kompletterats med dokumentstudier av interna och externa dokument samt observationer. I arbetet identifierades totalt tio teknologier inom konceptet industri 4.0 som sedan användes för att ta fram lösningsförslag på identifierade förbättringsmöjligheter. I nulägesanalysen identifierades tolv huvudområden med förbättringsmöjligheter kopplade till slöseri, repetitiva arbetsuppgifter och andra produktionsrelaterade utmaningar. I de framtagna lösningarna så föreslogs automation inom sju områden och digitalisering inom fyra områden. Vidare föreslogs Augmented Reality (AR) och Virtual Reality (VR) inom två områden och Artificiell Intelligens (AI) tillämpades inom två av de föreslagna lösningarna. Totalt identifierades tio teknologier som lämpliga och tre teknologier som någorlunda lämpliga att implementera. Av de lösningar som identifierats lämpliga var fem inom automation, två inom digitalisering, en inom VR och AR samt två inom AI. Arbetet kom även fram till ett antal krav som ställs på företaget för att implementeringen av teknologierna ska lyckas. Dessa krav är kopplade till nätverkets hastighet och kapacitet, utbildning av anställda, cybersäkerhet och äganderätt av data.

Digitalisation is changing the manufacturing landscape with promises to enhance industrial competitiveness with new technologies and business approaches. Various data-driven applications, enabled by digital technologies, can support process monitoring, production quality control, smart planning, and optimisation by making relevant data available and accessible to different roles in production. In this context, analytics is a relevant tool for improved decision-making for production activities since it entails extracting insights from data to create value for decision-makers. However, previous research has identified a lack of guidelines to manage the technological implementation needed for analytics. Furthermore, there are few studies in a real manufacturing setting that describe how companies are exploiting analytics. To address this gap, the purpose of this study is to investigate the implementation and use of analytics for production activities in the manufacturing industry. To fulfil the purpose of the study, the following research questions were formulated: RQ1: What does the adoption of analytics look like and what results can it bring to production activities of a manufacturing company? RQ2: What are the challenges and enablers for analytics adoption in production activities of a manufacturing company? This study was based on a literature review in addition to a single case study in a large multinational machinery manufacturing company. Data collection included observations and semi-structured interviews about three analytics use cases: for production performance follow-up, production disturbances tracking and production planning and scheduling. The first use case was based on the Design Thinking process and tools while the other two cases were narrower in scope and do not cover the development process in detail. Qualitative data analysis was the method used to examine the empirical and theoretical data. The empirical findings indicate that analytics solutions for production activities do not need to be sophisticated and characterised by high automation and complexity to bring meaningful value to manufacturing companies. The three analytics use cases investigated improved effectiveness and efficiency of production performance follow-up, production disturbances and production planning and scheduling activities. The main contributor to these benefits was a higher level of transparency of the factory manufacturing operations, which in turn aids collaboration, preventive decision-making, prioritization and better resource allocation. The identified challenges for analytics adoption were related to information system challenges and people & organization challenges. In other to address these challenges, this study suggests that manufacturing companies should focus on securing sponsorship from senior management and leadership, implementing cultural change to embrace fact-based decisions, training the existing workforce in analytics skills and empowering and recruiting people with digital skills. Moreover, it is recommended that manufacturing companies integrate information systems vertically and horizontally, link and aggregate data to deliver contextualised information to different roles and finally, invest in data-related Industry 4.0 technologies to capture, transfer, store, and process manufacturing data efficiently.

BACKGROUND - The development of Industry 4.0 increases the opportunities to both automate and digitize processes in the manufacturing industry. The steel industry has been around for many years, which means firmly anchored operations and both manual- and automated processes. To make better decisions, identify bottlenecks, and test new functions without having to stop the production, a digital model of the process can be helpful. Furthermore, with the rapid development of technology, digital models can be further developed into digital twins. A digital twin should be able to handle the communication between the physical- and digital world automatically and analyze data to make decisions in the process. RESEARCH QUESTIONS What are the challenges of developing a digital model representing a production line within a global steel manufacturing company? What opportunities could a digital model of a production line entail, and how could Industry 4.0 technologies create opportunities to further develop the digital model into a digital twin? METHODS - In this project, both a literature- and case study have been carried out. During the literature study, techniques that can be used to develop the digital model further have been investigated. During the case study, a digital model of a Quench Line was developed to gather practical experience of what it can mean to create a digital model of a manufacturing process within a steel manufacturing company. The model has been developed in MATLAB/Simulink. RESULTS - The most significant challenges when developing digital flow simulation models identified in this project were data management/access, handling variations, verifying the model, andlack of knowledge linked to digital models in general. The opportunities identified and confirmed in this project were that the model could be used to carry out new logistics planning, bottleneck analyses, and test new machine implementations. To further develop the digital model into a digital twin, Industry 4.0 technologies will be crucial. The technologies that will be useful are the Internet of Things, Artificial Intelligence, Machine Learning, Cloud Computing, and Big Data.