The powerful digitization and development of technologies is a fact. As a result, companies are facing the fourth industrial revolution in history; Industry 4.0. To remain competitive, it is important for companies to assimilate Industry 4.0 and its technologies, especially within manufacturing. Industry 4.0 offers numerous opportunities, but the implementation also comes with some challenges that should be taken into account for a successful transition. The aim of this study is to create an understanding of what Industry 4.0 can offer for companies and their manufacturing processes, and which challenges and opportunities that may be involved. This study has been based on research of the phenomenon Industry 4.0 as well as data collection from semi-structured interviews with employees of an industrial manufacturing enterprise. The result of the study can be divided in to 4 separate conclusions that should be taken into account when implementing Industry 4.0. First of all, the company must have a customized strategy to embrace the implementation into their existing structure. Secondly, it is important that all employees are informed and involved about the new procedures to increase understanding as well as motivation. Thirdly, it is important that existing techniques and digital tools within the company are adapted for a transition to Industry 4.0, and they must also have a common standard to facilitate for data management. Finally, if these three areas listed above are taken into account, the implementation of Industry 4.0, can provide real-time information and understanding that contribute to a better overview of the manufacturing, the quality, the efficiency, the work tempo, as well as any problems and downtime. Industry 4.0 also allows for businesses to create a more flexible production and give a good insight and control over the business, including better decision. On top of that, it also cuts the energy consumption as well as many other expenses. Overall, Industry 4.0 offers companies an opportunity to potentially become leading in the global market.

The Internet of Things (IoT) is the next phase in the evolution of the internet, where everyday objects are connected to the internet, and obtain the capacity to communicate with other devices and sense their environment. Especially the IIoT is one of the most talked about industrial business concepts since the recent years, companies try to focus on business models and operational efficiency. That is why this thesis focuses on researching the industrial Internet of Things (IIoT). There is already a lot of information about the common Internet of Things but still a gap in research in the business perspective, especially surrounding the concept of business models for the IIoT. The goal of this project is to investigate different kinds of business models, how they work and how feasible they are. The need to research possible business models for an IIoT framework, as traditional business models are relevant for this study, such as the Business Model Canvas which has been proposed by Alexander Osterwalder or the Business Model Navigator by Oliver Gassmann. But there is still a lack of literature covering the business models for the IIoT. Therefore, after researching the concept of IIoT from a business perspective, I identified some useful criteria and suitable business models. With a qualitative literature study, I was able to develop an IIoT business model framework, based on the dynamics and complexity of the IIoT concept, which incorporates business strategies and provides companies with a flexible approach. The business model framework can be used in any business which is working in the industrial context. To demonstrate how the business model framework works for the IIoT, I clarified how suitable business models can improve the current business model of the very prominent and successful company Tesla. The results show how the framework of IIoT business models can be used to increase profit and work efficiently as a company. The models can also be formed to only highlight single components of an already existing business model, as it offers great flexibility, which is highly valuable in the fast evolving and innovative IIoT phenomenon.

The Industrial Internet of Things (IIoT) is one of the key technologies of Industry 4.0 thatwill be an integral part of future smart and sustainable production. The current constitutedmodels for estimating Quality of Experience (QoE) are mainly targeting the multimediasystems. Present models for evaluating QoE, specifically leveraged by the expensivesubjective tests, are not applicable for IIoT applications. This work triggers the discussionon defining the QoE domain for IIoT services and applications. Industry-specific KPIs areproposed to assure QoE by linking business and technology domains. Tele-remote miningmachines are considered as a case study for developing the QoE model by taking intoaccount key challenges in QoE domain. As a result, QoE layered model is proposed, whichas an outcome predicts the QoE of IIoT services and applications in a form of pre-definedIndustrial KPIs. Moreover, software tool and analytical model is proposed to be used as anevaluation method for certain traffic types in the developed model.

Purpose: To investigate possible connections between an Industrial Internet of Things (IIoT) system, such as Predix, and data driven Lean practises. The aim is to examine if an IIoT platform can improve existing practises of Lean, and if so, which Lean tools are most likely influenced and how this is.Design/Methodology: The paper follows a phenomenon-based research approach. The methodology contains of a mix of primary and secondary data. The primary data was obtained through “almost unstructured” interviews with experts, while the secondary data comprises of a comprehensive review of existing literature. Moreover, a model was developed to investigate the connections between the concepts of IIoT and Lean.Findings: Findings derived from expert interviews at General Electric (GE) in Uppsala have led to the conclusion that Predix fulfils the necessary requirements to be considered an IIoT platform. However, the positive effects of the platform on the selected Lean tools could not be found. Only in one instance improved Predix the effectiveness of a Lean tool. Overall, data analytic efforts are performed and let to better in-process control. However, these efforts were independent from the Lean efforts carried out. There was no increase in data collection or analytics due to the Lean initiative and Predix is not utilised for data collection, storage, or analysis. It appears that the pharmaceutical industry is fairly slow in adapting new technologies. Firstly, the high regulatory requirements inherent within the pharmaceutical industry limit the application of cutting edge technology by demanding strict in-process control and process documentation. Secondly, the sheer size of GE itself slows down the adoption of new technology. Lastly, the pragmatic approach of the top management to align the digital strategies of the various industries and thereof resulting allocation of resources to other more technologically demanding businesses hinders the use of Predix at GE in Uppsala.

In this thesis, we explore the idea of using binary visualization and image analysis to detect anomalous activity on an Industrial Internet of Things (IIoT) based network. The data is gathered into a pcap file and then fed into our encoder, which uses a space-filling curve to convert the 1-dimensional stream of data into pixels with a specific red, blue, and green gradient value.  The pixels create an image which is then given to an image analysis system based on a Convolutional Neural Network, which classifies if the traffic supplied is malicious or not. The results show that using a Binary and Multiclass classifier approach to the image analysis both work well reaching an accuracy of 100% and 94% respectively. While the binary classifier is more accurate both succeed at separating Malicious from Benign traffic. The choice of space-filling curves in our binary visualization ended up having little to no impact on overall classification accuracy.

Along with the evolution of the new technologies such as industrial internet of things (IIoT), big data, cloud computing, artificial intelligence (AI), etc., the amalgamation between the cyber and physical worlds in the industrial field has become necessary to realize and achieve the smart factory and increase its productivity. The emergence of the Digital Twin (DT) concept as a technology that ties the physical and digital worlds has gained significant attention around the world during the last years. However, this concept is relatively new; the literature related to this concept is limited, and its application is still under development and requires further participation from both the industry and academia. This thesis project presented the main requirements and the steps for building a DT. Three research questions have been formulated and answered separately to fulfill the objective of this research study. The answer to the first two research questions was mainly based on surveying the scientific literature to explore this concept's background, main infrastructure, related technologies, its applications in the manufacturing domain, open issues, and some opportunities and challenges that hinder its implementation. Further, the answer to the last research question is represented in proposing a general methodology with some detailed steps for DT's building process and validating this methodology with an existing case study to show it works in practice. Further, several aspects needed for future work have also been addressed.

I den fluktuerande digitaliseringsvågen har den fjärde industriella revolutionen eller Industry4.0 initierat inom tillverkningsindustrin vilket påskyndar företag att anpassa och förändra helaverksamheter för att vara fortsatt konkurrenskraftiga. Industrial Internet of Things (IIoT) harblivit en central del av denna förändring för tillverkningsföretag och kan förklaras som företagsom utnyttjar enheter för att samla data i realtid och i sin tur gå mer mot den smarta fabriken.En rad olika möjligheter kan genomföras för industrier med uppgången av IIoT, även omframgången med denna förändring kan variera mellan olika företag beroende på storlek,resurser och ekonomisk stabilitet. Parallellt med möjligheterna uppstår även utmaningar förföretag, särskilt små och medelstora företag, då dessa saknar ekonomiska resurser och storlekför att kunna omfördela och omvandla sin verksamhet. I denna studie har målet varit att skildrahur medelstora tillverkningsföretag hanterar implementeringen av IIoT och den smartafabriken för att anpassa sig till det ständigt föränderliga tekniska paradigm som Industry 4.0har introducerat. Slutsatser har dragits utifrån kombinationen av en teoretisk ram ochintervjuer med sex svenska medelstora tillverkningsföretag. Digitaliseringsstrategier förtillverkningsföretag varierar beroende på bransch. Det finns emellertid enighet om att insatserför en digitaliserad produktion måste ske för att förbli konkurrenskraftig där automatisering,övervakning och kontroll av processer inom IIoT är nyckelfaktorer för att förblikonkurrenskraftiga. Tidsplanen och implementeringsnivån kan också variera beroende pådigital kompetens och motståndskraft mot förändring från personalen. Viktigt att poängtera äratt sambandet mellan IIoT, digitalisering och ökad konkurrenskraft inte är de enda faktorernasom krävs utan det finns fler faktorer att beakta. Studien pekar även på att konkurrensfördelarsällan är det främsta skälet till att företag väljer att digitalisera och implementera IIoT. / In the fluctuating wave of digitization, the fourth industrial revolution or Industry 4.0 in themanufacturing industry, has begun that has accelerated industries and companies to adapt andchange their whole business to maintain competitive. Industrial Internet of Things (IIoT) hasbecome a central part of this change for manufacturing companies and can be interpreted ascompanies taking advantage of units to gather real-time data and in turn, lean towards thesmart factory. A range of possibilities can be accessed by industries with the rise of IIoT,though the success of this change can differ between different companies depending on size,resources, and economic stability. Parallel to the opportunities, challenges arises forcompanies, especially small and middle-sized enterprises, that lack the economic resourcesand scale to redistribute and transform their business. In this paper, the goal has been todistinguish how middle-sized manufacturing companies handle the implementation of IIoT andthe smart factory in order to adapt to the ever-changing technical paradigm that Industry 4.0has introduced. Conclusions have been drawn from the combination of a theoretical frameworkand interviews with six Swedish middle-sized manufacturing companies. The digitizationstrategy for manufacturing companies varies from industries. However, there is a consensusthat efforts towards a digitized production must take place in order to stay competitive whereautomation-, monitoring-, and controlling processes within IIoT are main factors to staycompetitive. The pace and level of implementation can also differ depending on digitalqualification and resistance to change from the staff. Important to note is that the relationbetween IIoT, digitization and increased competitiveness is not the only factors that aresignificant as there are more things to consider. The study also shows that competitiveadvantages are rarely the main reason why companies choose to digitize and implement IIoT.

In the last few years, the popularity of Industrial IoT has grown a lot, and it is expected to have an impact of over 14 trillion USD on the global economy by 2030. One application of Industrial IoT is using data pipelining tools to move raw data from industry machines to data storage, where the data can be processed by analytical instruments to help optimize the industrial operations. This thesis analyzes and evaluates a data pipeline setup for Industrial IoT built with the tool Apache NiFi. A data flow setup was designed in NiFi, which connected an SQL database, a file system, and a Kafka topic to a distributed file system. To evaluate the NiFi data pipeline setup, some tests were conducted to see how the system performed under different workloads. The first test consisted of determining which size to merge a FlowFile into to get the lowest latency, the second test if data from the different data sources should be kept separate or be merged together. The third test was to compare the NiFi setup with an alternative setup, which had a Kafka topic as an intermediary between NiFi and the endpoint. The first test showed that the lowest latency was achieved when merging FlowFiles together into 10 kB files. In the second test, merging together FlowFiles from all three sources gave a lower latency than keeping them separate for larger merging sizes. Finally, it was shown that there was no significant difference between the two test setups.

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Manufacturing management and operations place heavy emphasis on monitoring and improving production performance. This supervision is accomplished through strategies of manufacturing performance management, a set of measurements and methods used to monitor production conditions. Over the last thirty years the most prevalent measurement of traditional performance management has been overall equipment effectiveness, a percentile summary metric of a machine's utilization. The technologies encapsulated by Industry 4.0 have expanded the ability to gather, process, and store vast quantities of data, creating opportunity to innovate on how performance is measured. A new method of managing manufacturing performance utilizing Industry 4.0 technologies has been proposed by McKinsey & Company and software tools have been developed by PTC Inc. to aid in performing what they both call digital performance management. To evaluate this new approach, the digital performance management tool was deployed on a Festo Cyber-Physical Lab, an educational mock production environment, and compared to a digitally enabled traditional performance management solution. Results from a multi-day production period displayed an increased level of detail in both the data presented to the user and the insights gained from the digital performance management solution as compared to the traditional approach. The time unit measurements presented by digital performance management paint a clear picture of what and where losses are occurring during production and the impact of those losses. This is contrasted by the single summary metric of a traditional performance management approach, which easily obfuscates the constituent data and requires further investigation to determine what and where production losses are occurring.