I den fluktuerande digitaliseringsvågen har den fjärde industriella revolutionen eller Industry 4.0 initierat inom tillverkningsindustrin vilket påskyndar företag att anpassa och förändra hela verksamheter för att vara fortsatt konkurrenskraftiga. Industrial Internet of Things (IIoT) har blivit en central del av denna förändring för tillverkningsföretag och kan förklaras som företag som 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 om framgången med denna förändring kan variera mellan olika företag beroende på storlek, resurser och ekonomisk stabilitet. Parallellt med möjligheterna uppstår även utmaningar för företag, särskilt små och medelstora företag, då dessa saknar ekonomiska resurser och storlek för att kunna omfördela och omvandla sin verksamhet. I denna studie har målet varit att skildra hur medelstora tillverkningsföretag hanterar implementeringen av IIoT och den smarta fabriken för att anpassa sig till det ständigt föränderliga tekniska paradigm som Industry 4.0 har introducerat. Slutsatser har dragits utifrån kombinationen av en teoretisk ram och intervjuer med sex svenska medelstora tillverkningsföretag. Digitaliseringsstrategier för tillverkningsföretag varierar beroende på bransch. Det finns emellertid enighet om att insatser för en digitaliserad produktion måste ske för att förbli konkurrenskraftig där automatisering, övervakning och kontroll av processer inom IIoT är nyckelfaktorer för att förbli konkurrenskraftiga. Tidsplanen och implementeringsnivån kan också variera beroende på digital kompetens och motståndskraft mot förändring från personalen. Viktigt att poängtera är att sambandet mellan IIoT, digitalisering och ökad konkurrenskraft inte är de enda faktorerna som krävs utan det finns fler faktorer att beakta. Studien pekar även på att konkurrensfördelar sällan 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 the manufacturing industry, has begun that has accelerated industries and companies to adapt and change their whole business to maintain competitive. Industrial Internet of Things (IIoT) has become a central part of this change for manufacturing companies and can be interpreted as companies taking advantage of units to gather real-time data and in turn, lean towards the smart 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 for companies, especially small and middle-sized enterprises, that lack the economic resources and scale to redistribute and transform their business. In this paper, the goal has been to distinguish how middle-sized manufacturing companies handle the implementation of IIoT and the smart factory in order to adapt to the ever-changing technical paradigm that Industry 4.0 has introduced. Conclusions have been drawn from the combination of a theoretical framework and interviews with six Swedish middle-sized manufacturing companies. The digitization strategy for manufacturing companies varies from industries. However, there is a consensus that efforts towards a digitized production must take place in order to stay competitive where automation-, monitoring-, and controlling processes within IIoT are main factors to stay competitive. The pace and level of implementation can also differ depending on digital qualification and resistance to change from the staff. Important to note is that the relation between IIoT, digitization and increased competitiveness is not the only factors that are significant as there are more things to consider. The study also shows that competitive advantages are rarely the main reason why companies choose to digitize and implement IIoT.

Digitalisation, and the related idea that digitalisation will cause a technological transformation that will lead to a “fourth industrial revolution” (Industry 4.0), is a global major topic with a huge impact on many parts of our life. Related changes in the labour market are to be expected, with implications for the working conditions of employees, for example due to the introduction of robots in production areas. Current research analyses how digitalisation can impact working conditions, however, there seems to be a gap in the research related to the impact of digitalisation on working conditions of women and older employees specifically. Therefore, my aim is to analyse how digitalisation changed and can change working conditions in the manufacturing sector in a way that promotes decent work and equality for women and elderly. Considering a theoretical background of the concept of decent work, gender equality and age-related issues at work, as well as a brief historical view of the impact of industrial revolutions on working conditions, I analyse the research questions for the case of Industry 4.0 in the German manufacturing sector. Methodologically, I combine expert interviews with qualitative content analysis of publicly available documents, to get a broad view of this case. My results indicate that digitalisation was, in fact, already able to improve working conditions and promoted the integration of women and older employees in a few selected areas, especially through reducing physical demands of labour by implementing assistive digital technologies. Correspondingly, digitalisation seems to provide the tools and opportunities to further change working conditions in a way that promotes decent work and equality for women and elderly. However, when it comes to key areas of inequality and discrimination in the labour market, the horizontal segregation of the labour market for women, as well as the lack of qualification of older employees, it remains uncertain whether digitalisation will actually improve these areas. The tools, however, seem to be there, and companies seem aware of them.

Eine Herausforderung aus Industrie 4.0 ist die Produktion individueller Produkte in der Losgröße 1. Dabei denken wir zuerst an Fotobuch, die Cola mit einem Etikett, das unseren Namen trägt und viele weitere der inzwischen zahlreich angebotenen individualisierten Endprodukte. Dabei wird oft vergessen, dass für deren Herstellung Maschinen und Anlagen erforderlich sind, die selbst individuelle Produkte sind und nicht mehr kosten sollen als deren Pendants aus der Großserie. Um dies zu leisten, reicht es nicht aus, Daten in einer Cloud zu sammeln und auszuwerten oder das Förderband mit einem Internetanschluss zu versehen. Vielmehr bedarf es ganzheitlicher Konzepte, mit denen Produktionssysteme nachhaltig und individuell entwickelt werden können. Ein Lösungsansatz dazu ist die funktions- und objektorientierte Modularisierung, bei der die zu realisierenden Funktionen den gesamten Entwicklungs- und Konstruktionsprozess bestimmen. [... aus dem Text]

The current industry landscape is witnessing a trend towards high-mix production, which requires a reconsideration of the existing production systems. Although high levels of automation have been achieved in the industry, the traditional automated production line, designed for mass production of homogeneous goods, is not well-suited for high-mix production. To address this situation, flexible and adaptable alternatives have been sought to replace the inflexible and rigid traditional production lines. One of the proposed solutions is the combination of digital technology and physical automation, creating a highly connected and intelligent production environment. Such an environment requires the implementation of a cyber-physical production system that integrates Industry 4.0 technologies, such as Automated Guided Vehicles (AGVs) and flexible robots. This system enables a set of robots to perform different tasks instead of being exclusively dedicated to a specific task, making it moreadaptable and flexible. The integration of advanced technologies, such as AGVs and Cyber-Physical Production Systems (CPPS), can significantly enhance workflow optimization, reduce production times, and enable flexible layout adaptations to cater to the specific requirements of different products in the production line. Furthermore, it can facilitate better control of information and enable real-time monitoring of the production process, leading to improved production efficiency and quality. To demonstrate the potential of such a system, a virtual commissioning of a fully innovative production line has been carried out, encompassing all the previously mentioned technologies and elements. The virtual commissioning of the production line serves as a proof-of-concept for the cyber-physical production system and its ability to provide a highly connected, intelligent, and adaptable production environment. / <p>Utbytesstudenter</p>

Due to Industry 4.0 (I4.0) the software ecosystem is constantly changing, and new possibilities and challenges are arising. The purpose of this Master Thesis is to map the software ecosystem in the automotive industry in order to understand the position of Atlas Copco and what gaps an industrial tool supplier can close. In addition, find out what opportunities and challenges that exist in the ecosystem today. Since Industry 4.0 and the software ecosystem are relatively new topics and alters continuously, the study was carried out in an abductive approach and a research design model was created according to that. By combining literature study with empirical study such as interviews, a better understanding was gained which laid the foundation of accumulating a deeper knowledge about the research field and made it feasible to answer the research questions. The current structure in different markets of the software ecosystem are shaped according to an old industry standard for modelling system structures. Differences and similarities between the markets can be found, which explains difficulties in providing software globally. By analysing and combining the current structures for each market, including the answers gained from the conducted interviews, a model was created. A new model for the definition of a software ecosystem in the automotive industry was the result. Additionally, a new way of structuring and visualizing the generic structure of the software ecosystem for Atlas Copco in the Automotive industry was presented as well. The latter model could be used as a tool, that enables a better understanding of the software ecosystem as well as a way to present new solutions to customers. In the ecosystem Atlas Copco takes the role as a premium industrial tool supplier and a provider of software used to configure and monitor quality and errors in the assembly processes. Hence, they broaden their market to become a software supplier. However, thanks to I4.0 technologies, there are potential areas in the production where it is possible for Atlas Copco to extend their establishment further to get more market shares of the software market as well as to provide more value to their customers. Although, the recommendation for Atlas Copco is to shape a reference architecture and focus on software close to the operations of industrial equipment. / Mjukvaruekosystemet är något som ständigt är i förändring där nya möjligheter och utmaningar uppstår i ekosystemet i anknytning till Industri 4.0 (I4.0). Syftet med denna masteruppsats är att kartlägga mjukvaruekosystemet i fordonstillverkningsindustrin för ta reda på vilken position AtlasCopco har och vilka luckor en industriverktygsleverantör kan sluta samt undersöka utmaningar och möjligheter som existerar idag. En forskningsdesignmodell skapades och eftersom projektet omfattar ämnet Industri 4.0 samt mjukvaruekosystemet som är ett relativt nytt ämne som är under kontinuerlig förändring, så utfördes studien baserat på abduktiv strategi. Genom att kombinera litteraturstudier med empiriska studier som intervjuer erhölls en bättre förståelse som lade grunden för att vidare samla på djupare kunskap om forskningsområdet vilket gjorde det möjligt att svara på forskningsfrågorna. Den nuvarande strukturen på olika marknader i mjukvaruekosystemet formas enligt en gammal branschstandard för ett modelleringssystemstrukturer. Skillnader och likheter mellan marknaderna har presenterats som förklarar svårigheterna med att tillhandahålla programvara globalt. I samband med att analysera och kombinera de nuvarande strukturerna för varje marknad, inklusive svaren från de genomförda intervjuerna, har modeller tagits fram. I resultatet presenteras en ny modell för att definiera ett mjukvaruekosystem inom bilindustrin. Dessutom presenterades också ett nytt sätt att strukturera och visualisera den generiska strukturen i mjukvaruekosystemet för Atlas Copco inom fordonsindustrin. Modellen kan användas som ett verktyg för att lättare förstå mjukvaruekosystemet, och till att presentera nya lösningar för kunderna. I ekosystemet tar AtlasCopco rollen som en premiumleverantör av industriella verktyg, inklusive att tillhandahålla programvara som används för att konfigurera, övervaka kvalitet och fel i monteringsprocesserna. Detta breddar Atlas Copcos marknad till att även bli en mjukvaruleverantör. Tack vare I4.0 teknik finns det potentiella produktionsområden där det är möjligt för Atlas Copco att etablera sig ännu mer, för att få fler marknadsandelar på mjukvarumarknaden samt ge mer värde till sina kunder. Rekommendationen är att Atlas Copco skapar en referensarkitektur samt fokusera på mjukvara nära kopplat till operationsnivån och industriverktyg.

Industry 4.0 is a collective name for several technological innovations that, when combined, among other things, provide an exponential potential for increased operational excellence in manufacturing. This thesis digs down into which technologies that are relevant in the context of predictive maintenance and how these can be integrated into existing theory in order to create value through increased e↵ectiveness. The primary findings can be condensed down into one general principal - uniformity. In order to leverage industry 4.0, and through it achieve a higher level of automatization, all data flow must be as canonical as possible. This is what allows both for bi-directional communication at scale, and higher-level decisionmaking algorithms to be deployed over a wide range of hardware. / Industri 4.0 är ett samlingsnamn för ett flertal tekniska innovationer vilka, tillsammans, möjliggör en potentiell förbättring av operational excellence som ökar exponentiellt mot antalet aopterade teknologier. Detta arbete dyker ned i vilka teknologier som skapar mest värde i kontexten predictive maintenance. Arbetet studerar även existerande orginatorisk teor och hur dessa kan slås samman. Det primära resultatet kan summeras som att fokus bör ligga på en canonical model för den data som genereras, och skickas ned till maskiner på fabriksgolvet. Uniform data spelar även en nyckelroll i att facilitera för beslutsfattande algoritmer då dessa annars enbat skulle gå att applicera på specifika maskiner.

Increased strain on incumbent wastewater distribution networks originating from population increases as well as climate change calls for enhanced resource utilization. Accurately being able to predict network interconnectivity is vital within the wastewater industry to enable operational management strategies that optimizes the performance of the wastewater system. In this thesis, an evaluation of the network interconnectivity prediction performance of two machine learning models, the multilayer perceptron (MLP) and the support vector machine (SVM), utilizing supervisory control and dataacquisition (SCADA) system data for a wastewater system is presented. Results of the thesis imply that the MLP achieves the best predictions of the network interconnectivity. The thesis concludes that the MLP is the superior model and that the highest achievable network interconnectivity accuracy is 56% which is attained by the MLP model. / Den ökade påfrestningen på nuvarande avloppsnät till följd av befolkningstillväxt och klimatförändringar medför att det finns behov för optimerad resursförbrukning. Att korrekt kunna predicera ett avloppsnät är önskvärt då det möjliggör för effektivitetshöjande operativ förvaltning av avloppssystemet. I denna avhandling evalueras hur väl två maskininlärningsmodeller kan predicera nätverketssammankoppling med data från ett system för övervakning och kontroll av data (SCADA) genererat av ett avloppsnätverk. De två modellerna som testas är en multilagersperceptron (MLP) och en stödvektormaskin (SVM). Resultaten av avhandlingen visar på att MLP modellen uppnår den bästa prediktionen av nätverketssammankoppling. Avhandlingen konkluderar att MLP modellen är den bästa modellen för att predicera nätverkets sammankoppling samt att den högsta nåbara korrektheten var 56% vilket uppnåddes av MLP modellen.

Production automation as a service (PAaaS) is seen as a new trend that enables the possibility to use production automation technologies as a service. The technologies are cloud-based which makes the implementation of production automation more effective and cost-effective. This approach is attractive to the companies that have a limited capital investment. The purpose of this thesis was to analyse and understand the implementation of Production Automation as a Service (PAaaS) in the manufacturing industry. In order to understand its implementation, it is important to know what the drivers and benefits with PAaaS implementation and what challenges there are and how to overcome them for a successful implementation. To provide with a comprehensive answer and conclusion about PAaaS implementation in the manufacturing industry the following research questions was studied:1. What are the drivers and benefits of PAaaS implementation in the manufacturing industry, and what needs does it fulfil?2. What are the challenges of implementing PAaaS in the manufacturing industry, and how can they be overcome for a successful implementation and scale-up?To be able to answer these questions, a qualitative research study based on literature review and interviews was conducted. The combination between the literature and the real-life experience in PAaaS implementation provided a greater understanding of the concept. The aim of the research questions is to provide guidance and recommendation for companies seeking for a successful implementation of PAaaS which leads to improved operational efficiency and the ability to utilise the technological advancement provided through PAaaS. The approach applied in this study has been qualitative research with an abductive research approach. By obtaining data through scientific articles and interviews it was possible to analyse it more in-depth in order to find similarities between them.In conclusion, PAaaS implementation in the manufacturing industry provides with key benefits such as cost-effectiveness, improved flexibility, scalability, productivity, efficiency, and improved product quality. These benefits fulfil several needs of manufacturing companies such as being more flexible and being able to use automated solutions at a lower cost. These needs also act as the drivers for the implementation of PAaaS. The drivers are an important aspect of PAaaS implementation, because without any drive and motivation, there won’t be any implementation of PAaaS that can fulfil a certain need of the company. The drivers and motivation for a PAaaS implementation in the manufacturing industry is the possibility for a business model transformation and the technological advancements that the manufacturer gain with the implementation.In addition, there are key challenges that makes the PAaaS implementation more complex for the manufacturing companies. These challenges are, Integration with legacy systems, internet dependency and lack of expertise and knowledge. To able to achieve a successful implementation, it is important to address the challenges, by addressing these problems it was possible to provide with strategies on how to overcome them. Which lays the foundation for future research about this topic.

Date: 4th June 2023 Level: Master thesis in Product- and Process Development, advanced level, 30 credits Institution: School of Innovation, Design and Engineering at Mälardalen University Authors: Victor Busck Julia Eriksson Title: Improving Software Development Process Through Industry 4.0 Methodologies - A focus on Railway Embedded Software Supervisor: Yuji Yamamoto - Mälardalens University, Raluca Marinescu - Alstom, Ian Bird-Radolovic - Alstom Keywords: Safety-critical software development; Software development;Industry 4.0; Artificial Intelligence Purpose: The purpose of this study is to investigate what challenges and bottlenecks may occur in the development process of safety-critical software and suggest how Industry 4.0 technologies could be applied to overcome the bottlenecks and improve the process. Research questions: 1. What bottlenecks can the railway domain encounter when developing safety-critical software? 2. How can Industry 4.0 technologies be applied to overcome thebottlenecks and improve the development process of safety-critical software? Methodology: The study is based on a qualitative research methodology following an abductive approach. This led to the theoretical framework being gradually developed in parallel with the empirical data collection. The theoretical collection was based on scientific reports and books. The empirical data collection was based on a questionnaire, of which five in-depth interviews werethen conducted based on responses. Out of the five, three were semi-structured and two unstructured. Conclusion: The study concluded that all phases except design and implementation and software evaluation contained various bottlenecks related to tools, training, processes, resources and communication. However, it can be concluded that the testing phases were the biggest bottleneck at Alstom. To overcome testing challenges and improve the development process, the analysis shows that Industry 4.0 technologies such as AI, NLP and ML could be used to automate testing activities.

Background - As a result of the latest advances in artificial intelligence (AI), the world ofbusiness is facing a major transformation where basic organizational principlesare redefined initiating a new era. It is predicted that AI in the coming decadeswill make a significant imprint and organizations aiming to stay at the forefrontcannot afford not to change. AI adoption can bring great benefits to organizationswhere a crucial factor is to establish AI readiness. However, as in any change,different perceptions are raised among employees which can either hinder orfoster organizational AI readiness, placing leaders in a crucial position. Purpose - The purpose of this study is to investigate how managers can foster organizationalAI readiness by understanding distinctive features of employee AI attitudes. Byidentifying how employees develop change attitudes towards AI, the opportunityto explore how managers should respond to these attitudes in order to achieve AIreadiness opens. Method - To gain a greater understanding of the phenomenon managing AI attitudes and tofulfil the purpose of the study, a mix of a qualitative and quantitative researchmethodology was used. The empirical data were abductively collected through asingle case study via a survey containing 80 respondents and through a focusgroup including six participants holding different roles affected by an AIimplementation. The empirical data were processed using thematic analysis andfurther analysed through systematic combining. Conclusions - The conclusions in this study confirm already existing theory. It also expands itas the phenomenon managing attitudes towards AI change was placed in a newcontext. The research results indicate that employees’ change attitudes towardsAI are affected by the organizational AI maturity, personal interest, and personaland organizational AI knowledge. They also indicate that employees develop theirchange attitudes towards AI depending on how managers handle or not handletheir attitudes. Finally, four dimensions along which leaders should manageemployee change attitudes to promote AI readiness were elaborated.