Internet, automation, digitalisering och liknande tillverkningsteknologier som associeras med Industri 4.0, eller den fjärde industriella revolutionen, håller på att förändra sättet som tillverkningsindustrier styr hela sin produktionskedja. De nya teknikerna som inkapslas av Industri 4.0-paradigmet har genererat nya smarta sätt för att effektivisera produktionen och samtidigt spara in på resurser. Men denna revolution har genererat utmaningar, liksom förmåner, inte minst för de individer som jobbar inom den industriella sektorn. I denna studie undersöks hur operatörer inom tillverkningsindustrin upplever och anpassar sig till denna typ av nya teknologi på sina arbetsplatser. Vad för typ av utmaningar och möjligheter har upplevts av de människor som står i direkt kontakt med dessa nya typer av innovationer. För att besvara denna fråga, genomfördes intervjuer med operatörer på två fabriker inom tillverkningsindustrin där ny teknologi införts och påverkat produktionskedjan. Totalt åtta operatörer, varav fem från Fabrik A och tre från Fabrik B, intervjuades genom semi-strukturerade intervjuer. Detta i syfte att generera insikt i upplevelsen av automation, internettjänster och övriga digitala hjälpmedel i arbetsuppgifter som tidigare utförts utan det. Genom metoden för grundad teori kodades och tolkades den insamlade intervjudatan och kategorier kunde bildas. Resultaten visade att det fanns ett stort behov av tydligt kommunicerad information gällande ny teknik och nya arbetssätt. Resultaten indikerar att det finns mycket utvecklingspotential när det kommer till hur operatörer underrättas om nya innovationer i deras arbete. Vidare drogs slutsatsen att det krävs en gemensam förståelse för nya systemuppdateringar och förändringar, för att dessa ska mottas och hanteras på ett bra sätt. Ur analysen framställdes en modell över de utmaningar och möjligheter som industriföretag står inför vid implementation av smarta system i sin produktion, ur de anställdas perspektiv.

Abstract Intro: Conventional training methods are intended to teach and offer the most human way of teaching but is it the most effective and can another method improve or eliminate nonvalue adding activities. This master thesis tackles the difference between traditional training versus AR supported training, studying different aspects of training to see the advantages and disadvantages of the methods. The research questions were the following:  RQ1: Is the learning process improved with or without AR supported technology?   RQ2: What are the benefits of AR use in industry?  RQ3: Will the investment of AR implementation in the learning process pay off?  Method: To answer the research questions, a case study at a case company was conducted. The case study consisted of working with two companies, an external company for a theoretical answer based on experiences and an experimental study at the internal case company. In parallel to this case study a literature review was done to answer the research questions.  Theory and Literature Review: Literature for the frame of reference was gathered to support the thesis and give the perspectives of the investigated area. The literature review sought to find answers to the research questions and the search was systematically formed to focus on data gathering to answer the questions.  Analyses: The findings from this research shows that AR supported training is beneficial and has the potential to eliminate nonvalue adding activities. AR has many capabilities and research shows that AR in general has positive impact.  Conclusion: AR-applied training has both advantages and disadvantages, but the potential of improvements is high. It is difficult to conclude if it is economical to invest in AR, with the advances the technology holds on to now since the outcome can depend on several factors from different parameters. However, AR does contribute to more effective learning and further benefits within the applied area. It has been proven to reduce the error rate and thus may increase the quality of the product.

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.

Planning and management of logistics and material flow are widely studied and two key factors contributing to company competitiveness. Automation in material flow is recognized in efficient and profitable factories in the context of today’s smart industry, however, the operators are playing a significant role as well. The purpose of the study was to identify which criteria could be used to determine the level of automation in a material flow of the industrial factory. A model was developed to practically support the decision making on the level of automation for the case company that was going to build a pilot line for battery manufacturing. The question for the case company was to decide whether the process should be fully automated, manual, or semi-automated in its trial production to avoid costly reconfigurations when a full production starts. In this study a literature review was conducted in the form of previous research to describe which criteria were important to decide the level of automation. The literature study together with site visiting, interviews, survey and document analysis was used for the formulation of research questions, establishment of methodology and model development. The study has identified different criteria whereas six of them are shared by both literature studies and the case company. Two unique criteria were identified in the case company but not found in the literature: lead time and recruitment. The lead time refers to the time from planning to finished process and the recruitment is about how difficult to recruit people with the right skills. Two theoretical contributions presented in this study: new criteria when deciding on the level of automation and a model that assists in decisions regarding the level of automation in early phases. The model is also a practical contribution to the case company. Finally, some suggestions for further research within the area are presented.

The smart factory is at the heart of Industry 4.0 and is the new paradigm for establishing advanced manufacturing systems and realizing modern manufacturing objectives such as mass customization, automation, efficiency, and self-organization all at once. Such manufacturing systems, however, are characterized by dynamic and complex environments where a large number of decisions should be made for smart components such as production machines and the material handling system in a real-time and optimal manner. AI offers key intelligent control approaches in order to realize efficiency, agility, and automation all at once. One of the most challenging problems faced in this regard is uncertainty, meaning that due to the dynamic nature of the smart manufacturing environments, sudden seen or unseen events occur that should be handled in real-time. Due to the complexity and high-dimensionality of smart factories, it is not possible to predict all the possible events or prepare appropriate scenarios to respond. Reinforcement learning is an AI technique that provides the intelligent control processes needed to deal with such uncertainties. Due to the distributed nature of smart factories and the presence of multiple decision-making components, multi-agent reinforcement learning (MARL) should be incorporated instead of single-agent reinforcement learning (SARL), which, due to the complexities involved in the development process, has attracted less attention. In this research, we will review the literature on the applications of MARL to tasks within a smart factory and then demonstrate a mapping connecting smart factory attributes to the equivalent MARL features, based on which we suggest MARL to be one of the most effective approaches for implementing the control mechanism for smart factories.

The industrial company Scania CV AB is a world leader in the manufacturing of commercial vehicles. They offer a modular systems that include heavy trucks and buses that can be configured to a range of different needs. However, this adaptability leads to a problem where each order can have a large variance of assemblers that are re-quired during the manufacturing process. In other words, variant assemblers have a workflow that can shift from high workload to low workload and vice versa in a short period of time. To solve this problem a prototype will be developed. This prototype will be used to check if it’s possible to optimize the work schedule for variant assem-blers with the help of predictive simulations. The result of the study became an implementation in form of a prototype. This prototype is built up in two layers; a data layer and a simulation layer. The data layer provides the simulation layer with two different datasets. The first dataset is based on historical data and is derived from Scania’s production in Zwolle. The second dataset is based on synthetic data which is formed with a high utilization rate in order to mimic a better production situation with less product variants to assemble. The simulation layer consists of a DES-model that is modelled after a station in the final assembly of Zwolle. After a simulation has been executed, this layer generates a simulation result in form of a graph that presents the utilization rate for a group of variant assemblers. This will happened for each dataset in the data layer, in this case two times. The simulation result that got produced shows that it’s possible to create a simulation with predictive characteristics. A long term solution for Scania’s problem statement requires more research within the possibility of combining different technologies such as DES with predictive methods such as ML and GAs. / Industriföretaget Scania CV AB är världsledande inom tillverkning av kommersiella fordon. De tillhandahåller ett modulärt system som inkluderar tunga lastbilar och bussar som kan konfigureras till en rad olika behov. Den här anpassningsförmågan leder dock till ett problem där varje order som tillverkas kan ha en stor varians av hur många montörer som krävs under produktion. I andra ord så har variantmontö-rer ett arbetsflöde som kan skifta från hög arbetsbelastning till låg arbetsbelastning och vice versa under en kort period. För att lösa dessa typer av problem så ska en prototyp med prediktiva egenskaper så som Diskrete Event Simulering (DES). Denna prototyp ska undersöka om det är möjlighet att optimera arbetsscheman för variantmontörer med hjälpa av prediktiva simuleringar. Resultatet av studien blev en implementation i form av en prototyp. Denna prototyp är uppbyggd i två lager; ett datalager samt ett simuleringslager. Datalagret tillhandahåller simuleringslagret med två dataset. Det första datasetet är baserad på historisk data och är härledd från Scania’s produktion i Zwolle. Det andra datasetet är baserat på syntetisk data som är framtagen med en högre utnyttjandegrad för att efterlikna ett bättre produktionssitation med färre produkt varianter att montera. Simuleringslagret består av en DES-model som är modulerad efter en station i slutmontering i Zwolle. Efter att en simulering har exekverats så genererar detta lager ett simuleringsresultat i form av en graf som presenterar utnyttjandegraden för en grupp med variant montörer. Detta sker för varje dataset i datalagret, i detta fall två gånger. Simuleringsresultatet som togs fram visar att det är möjligt att ha skapa simuleringar med prediktiva egenskaper. En långsiktig lösning för Scania’s problem-beskrivningen kräver mer forskning inom möjligheten att kombinera tekniker som DES med prediktiva metoder som ML och GAs.

Tillverkningsindustrin blir alltmer digitaliserad samt att nya digitala verktyg implementeras inom företagen. Som följd av detta pågår en förändring av arbetssätt. Smart Maintenance är det senaste begreppet i hur underhåll borde utföras inom tillverkningsanläggningar med hjälp av digital teknik. Detta begrepp syftar på ett arbetssätt som ämna möjliggöra en resurseffektivare produktion och underhållsverksamhet, ur såväl organisatoriskt som tekniskt perspektiv. I detta examensarbete genomfördes intervjuer med företag, vilket utgjorde den centrala undersökningsmetoden för att förstå hur den svenska tillverkningsindustrin ser på Smart Maintenance (SM), vad deras tolkning är på begreppet samt ifall de har tillämpat detta, samt tillämpat aspekter eller dimensioner från SM i deras underhållsverksamhet. En intervju med en forskare genomfördes för att utöka projektgruppens kompetens kring begreppet och dess påverkan på lönsamhet, hållbarhet och konkurrenskraft. Med information från intervjuerna och en litteraturstudie som grund, erhölls slutsatser kring vilka de främsta fördelarna och utmaningarna är i utövandet av Smart Maintenance, samt dessas samband med hållbarhet. Dessutom resulterade projektet i slutsatser kring hur företagen tolkar begreppet och hur data kan används för investeringsplaner inom de intervjuade företagen. / The manufacturing industry is becoming increasingly digital and new digital tools are being implemented within companies. As a result, there is a change in working methods. Smart Maintenance is the latest concept in how maintenance should be performed in manufacturing facilities using digital technology. This concept refers to a way of working that aims to enable a more resource-efficient production and maintenance operation, from both an organizational and technical perspective. In this thesis, interviews were conducted with companies, which constituted the central research method for understanding how the Swedish manufacturing industry views Smart Maintenance (SM), what their interpretation is of the concept and if they have applied this, and applied aspects or dimensions from SM in their maintenance operations. An interview with a researcher was conducted to expand the project group's knowledge on the concept and its impact on profitability, sustainability and competitiveness. Based on information from the interviews and a literature study, conclusions were obtained about what the main benefits and challenges are in the practice of Smart Maintenance, as well as their connection with sustainability. In addition, the project resulted in conclusions about how the companies interpret the concept and how data can be used in order to make better decisions within the interviewed companies.

Den moderna industrin har under senare år präglats av nya utvecklingar i teknikens värld, samt ett alltmer ökande behov av att effektivisera existerande processer och vara i framkant med innovationen av nya. Industri 4.0 är kulmen av detta samhällsbehov, en syn på industrin som till huvudsak drivs av cyberfysiska system, en integration av det fysiska med det virtuella i form av trådlösa uppkopplingar och molnteknologier. Internet of Things, även känd som IoT, är uppkopplingen av fysiska produkter till molnet som möjliggör datautvinning och övervakning under och efter produktionen i realtid. IoT förekommer i skiftande sammanhang och har tillämpats i varierande utsträckning inom olika branscher där det visat sig vara effektivt när det gäller inverkan på resursutnyttjandet. Inom handelsbranschen resulterade en IoT tillämpning på försörjningskedjan till en ökad kundkontakt och en förbättrad samarbetsrelation. Detta som en följd av reell-tids analyser av både behov, brister i försörjningskedjan och efterfrågan. Den har även en plats i moderna matbutiker som Electronic Shelf Labels (ESL). Detta tas reda på med hjälp av sökningar i journaler, konferensrapporter och ett teori baserad studium av tidigare tillämpningar. Målet med studien är att se hur tidigare anmärkningar förhåller sig till fordonsbranschen. Fordonsbranschen är i framkant vad gäller tillämpningar av industri 4.0 och IoT. Tillverkaren Scania är i begynnelsefasen av en eventuell storskalig övergång, och redan har tillämpningen fört med sig förbättringar. Dock återstår det mycket som måste uppklaras, bland annat sekretess angelägenheter och en omfattande kompetens kring ämnet. / In recent years, the modern industry has been characterized by new developments in the technological world. There is an ever-increasing need to streamline existing processes and a need to be at the forefront for the innovation of new ones. Industry 4.0 is the culmination of this need, a form of industry that is mainly driven by cyberphysical systems, an integration of the physical with the virtual in the form of wireless connections and cloud technologies. The Internet of Things, also known as IoT, is the connection of physical products to the cloud that enables data recovery and monitoring during and after production, in real-time. IoT exists in various contexts and has been applied to varying degrees in different industries, and has proven to be effective in terms of impacting resource efficiency. In the trading industry, an IoT application to the supply chain resulted in increased customer contact and an improved cooperative relationship. This is a result of real-time analysis of both needs, supply chain shortages and demand. It also has a place in modern grocery stores such as Electronic Shelf Labels (ESL). The information was sought after in online journals, conference reports and past applications. The goal of the study is to establish a relation between these past applications and the automotive industry, to find out how they compare. The automotive industry is at the forefront in terms of applications of Industry 4.0 and IoT. The manufacturer Scania is in the beginning phase of a possible large-scale transition, and the application has already brought improvements. However, much remains to be resolved, including confidentiality issues and extensive expertise on the subject.

The concept of industry 4.0 has long been a topic of discussion, research, and cultural impact, it has also rapidly increased effectiveness, profitability, and growth for the involved parties. At the pinnacle of industry 4.0 smart factories have emerged as a concept, describing the factories of competitive manufacturing leaders who best utilize the advanced tools of the era. The journey toward progressing conventional factories, making them smart, requires collaboration with suppliers who are well-equipped to support this transition. Therefore, this thesis aims to take on a supplier perspective through the lens of strategy to gain a deeper understanding of how suppliers should act towards original equipment manufacturers (OEMs) in the smart factory era. Specifically, investigating how suppliers can seize business opportunities in the smart factory maturity (SFM) journey of OEMs.  The study was conducted through a qualitative single case study performed with an industrial supplier towards OEMs in the motor vehicle industry (MVI). Data collection included performing semi-structured interviews with employees at the case company as well as observations such as break room discussions and visits at OEMs. Moreover, thematic analysis was carried out which resulted in three themes which deepens the understanding of the supplier’s role in the context. The discussion of the themes concludes that suppliers should: 1. Understand the state of the OEMs' smart factory journey, by identifying the OEMs SFM and what drives them forward. 2. Use the found enablers in their strategy to assist the journey, i.e. by developing strategic partnerships, expanding the software offering, establishing a smart factory vision, and changing the mindset towards solution selling. 3. Prepare for and manage the identified challenges of the smart factory era, which are factory legacy, new competency needs, cyber security, silo mindset, and change management. The research contributes to industry 4.0 maturity model literature by adding a more managerial view on the area and a supplier perspective. Furthermore, the study contributes to strategic management literature by adding onto the dynamic capabilities framework by providing a practical context on how organizations can seize business opportunities.

The term ‘Industry 4.0’ leads to many new possibilities like smart factory which is the amalgamation of manufacturing systems in a network to perform tasks more efficiently. It is becoming more and more important for the companies to develop smart factories and integrate the devices within such a facility to meet the demands of the evolving market. The next generation production systems are designed to share the data within the network, plan, and predict the solution for the future problems. One such technology under smart factory is 3D laser scanning resulting in point cloud of the production unit. The traditional way of documenting a layout is usually with the help of 2D computer aided designs which are susceptible to measurement errors and changes that are not updated regularly. With the help of point clouds, an as-is representation of the factories can be recorded which can be easily updated with changes in the real world. With advancements in virtual manufacturing, the need for visualization of the factories is increasing drastically. 3D Laser Scanning is one of the better ways of meeting this need, among many other applications. The focus of the thesis had been to create a method document for 3D laser scanning of factories and to discuss the future possibilities of it. The research approach used in this thesis was conducting observational study, interviews and testing of the method. One such future possibility is autonomous scanning and how it would be beneficial for a company like Scania which is developing smart factories. Based on the study carried out during the thesis, a document presenting the method developed is included in the report. The report also points out the applications and benefits of point cloud over traditional layout planning methods.