As the awareness of the impact of human activities on the environment, economy, and society has been brought to spotlight during the last decades, sustainable development has gained ever-increasing attention around the world. While industrial revolutions are critical for the efficiency of meeting customer needs, it is also crucial to address and reverse the depletion of Earth’s resources. That is why the concept of circular economy has been introduced and implemented in the manufacturing sector, which aims for a shift in paradigm towards closed-loop “systems that are restorative or regenerative by intention and design”. However, while bringing forward an amplitude of opportunities, there are challenges and barriers in the implementation of the principles of circular economy in manufacturing systems. This is where another area of science can contribute: Industry 4.0. Industry 4.0 refers to the incorporation of technological advancements in all stages of the life cycle to provide meaningful and real-time information, in order to aid decision-making regarding different functions of the manufacturing systems, i.e., business model, product design, and supply chain. It has been proven in literature that there are major opportunities in merging Industry 4.0 with circularity principles. However, there is a research gap on what the prerequisites are for a successful transition towards a more circular manufacturing system with the support of digital technologies. This research attempts to fill in this gap by conducting a qualitative study, interviewing practitioners and academics involved in real-life cases of such transitions. The study contributes to the existing body of knowledge by suggesting a list of seventeen prerequisites, categorised into three themes of cultural, organisational and business-related, and technical prerequisites. The main audience of this study is the manufacturing companies who want to increase their chances of success when embarking on a transition towards more circular manufacturing systems. / Under de senaste decennierna så har människans effekter på miljön, ekonomin och samhället kommit att få en allt större roll, och begreppet hållbar utveckling har kommit att få stort genomslag runt om i världen. Teknisk utveckling inom produktion, som historiskt sett mest bidragit till ökad effektivitet och bättre erbjudanden till kunderna, har i det här sammanhanget även en viktig roll i att vända utarmningen av jordens resurser. Begreppet cirkulär ekonomi, som introducerats och implementeras inom produktion, beskriver denna vändning. Begreppet syftar till ett paradigmskifte mot slutna "system som är återställande eller regenerativa genom avsikt och design". Förutom möjligheterna med cirkulär ekonomi inom produktion finns även en rad utmaningar och hinder med att införa cirkulära produktionssystem. Ett teknikområde som kan bidra till att åtgärda dessa utmaningar och hinder är Industri 4.0. Industri 4.0 förespråkar införandet av ny teknologi i alla stadier av en produkts livscykel, för att ge meningsfull information i realtid och hjälpa beslutsfattandet inom alla faktorer i ett produktionssystem: affärsmodell, produktutveckling och värdekedjor. Tidigare litteratur har rapporterat om stora möjligheter med att använda Industri 4.0 för ökad cirkulär ekonomi. Det finns dock en lucka i forskningen kring vilka förutsättningar som krävs för en lyckad övergång till mer cirkulära tillverkningssystem med stöd av digitala tekniker. Detta examensarbete försöker fylla denna lucka med hjälp av en kvalitativ studie, baserad på intervjuer med industrirepresentanter och akademiker som deltagit i verkliga fall av sådana övergångar. Studien bidrar till tidigare litteratur genom att föreslå en lista med sjutton förutsättningar, kategoriserade i tre teman: kulturella-, organisatoriska och affärsmässiga-, och tekniska förutsättningar. Den huvudsakliga målgruppen för studien är tillverkande företagen som vill öka sina chanser att lyckas när man påbörjar en övergång till mer cirkulära tillverkningssystem.

AI has revolutionized the world and begun to influence an increasing number of industries worldwide. The pressure to implement AI has been evident at both EU and national levels. AI has demonstrated significant benefits in many companies, such as improved financial performance and efficiency. However, despite this, there are still many companies,particularly manufacturing companies, that have not implemented AI. Interestingly, manufacturing companies are among those that stand to benefit the most from AI. Therefore, exploring the intentions to adopt AI in manufacturing companies becomes relevant. The concept of adopting innovation and to some extent AI adoption, is theoretically explored. However, there is a lack of empirical evidence of what hindersmanufacturing companies from adopting and implementing AI. Furthermore, Swedish companies are less positive towards AI compared to global leaders. To address the identified research gap, the following research question has been formulated:  “What are the barriers and determinants that manufacturing companies in the northern part of Sweden face during the adoption and implementation of AI? “ The aim of this thesis is to investigate the factors influencing the adoption and implementation of AI solutions in manufacturing companies located in the northern part of Sweden, based on previous research and the empirical findings. To fulfill the purpose, we designed an exploratory qualitative study and conducted seven semi-structured interviews based on themes derived from TOE and TAM along with Rogers InnovationDiffusion. The key findings from the study revealed that the combined TOE-TAM model along with Rogers theory and other factors like leadership and external actors can explain the intention to adopt AI technology. Further, the significant barriers to AI adoption arethe lack of sufficient competence both internally and externally, another barrier was the costs of the implementation. Although we identified barriers to AI adoption, a lot of opportunities emerged as well. The determinants to adopt AI or not was the internal attitude and understanding of how the company can utilize AI. This thesis contributes with knowledge concerning the factors affecting AI adoption in manufacturing companies in the northern part of Sweden. It resulted in a revised conceptual framework that provides valuable insights for managers to consider when adopting AI. Finally, the thesis contributes with social implications that can assist in the rising concerns regarding AIs role in society.

Many industries are moving to adapt to the competitive market and customer requirements to increase productivity, flexibility of their production systems, and achieve optimal improvement. This made the simulation attract attention by enabling its users to test several scenarios with different options and concepts before applying them on the ground. The purpose of this thesis is to test the role of simulation in making decisions about the optimal layout design within the available cost and to present a concept for simulating the production system after redesigning the smart factory, which includes the number of machines, tiles and movers as an assumption and the demand for products with a variable dynamic flow. The simulation model addresses the concept of the production process by implementing modern technologies with their effective characteristics to ensure a smooth flow of products between all the machines that present as components of the smart factory. The developed optimal model includes the components of the smart factory in addition to all the requirements that must be considered to ensure increased flexibility and efficiency of the production system. In addition, the optimal design with the lowest cost and the concept of the production process were presented through the results obtained regarding productivity and performance. This thesis presents the challenges might be arise when employing the simulation as a tool to redesign and suggestions mechanisms to reduce them in the discussion chapter. The design model presented in this thesis serves as a connecting point for continuing future initiatives and developing the production concept.

This thesis investigates the impact of digitalisation on sustainability performance within manufacturing firms in Northern Europe from 2010 to 2023 at the age of Industry 4.0. It explores how digital technologies like IoT, cyber-physical systems, and big data have transformed production processes, enhancing sustainability across environmental, social, and governance (ESG) criteria. The research employs a quantitative approach, utilising regression analyses of data sourced from the Refinitiv database to assess the influence of digitalisation on ESG performance in manufacturing firms across Northern Europe.  The theoretical underpinnings of this study are grounded in an integration of shareholder and stakeholder theories, which offer a detailed examination of how digital transformations are reshaping corporate governance dynamics. It also encompasses a discussion on agency theory and legitimacy theory, highlighting how differences in corporate governance outlooks impact sustainability initiatives. Moreover, the research evaluates the potential benefits and challenges associated with digitalisation through the lens of the resource-based view and the dynamic capabilities framework. This analysis elucidates how digitalisation provides a competitive advantage, for example, by promoting efficiency and decreasing energy consumption while acknowledging the possibility of increased resource use, a phenomenon referred to as the Jevon Paradox.  Findings indicate that digitalisation facilitates improved sustainability outcomes to enhance financial performance by optimising resource utilisation, reducing waste, and improving operational efficiencies. However, variations exist depending on the type and intensity of digitalisation. The results highlight that while digital technologies drive significant improvements in environmental management and operational efficiency, the benefits are not uniformly experienced across all firms, suggesting a differentiated impact based on company size and digital maturity.  This research significantly contributes to the academic literature by providing empirical evidence of digitalisation’s role in enhancing sustainability within the manufacturing sector. Importantly, it offers practical insights for policymakers and business leaders, equipping them with the knowledge to leverage digital transformation to meet sustainability goals effectively. Future research must delve deeper into the long-term effects of digital technologies on sustainability, investigate the impacts across different industrial contexts, and examine the role of regulatory frameworks in shaping digital transformation strategies. This thesis underscores the urgent need for manufacturing firms to harness digital innovations responsibly to foster sustainable development in the digital age.

Den moderna tillverkningsindustrin är i ständig förändring för att möta kraven från samhället och marknaden. Industri 4.0 har introducerat en ökad digitalisering och automatisering, vilket kräver smidiga och effektiva system för inrapportering för att hantera den ökande komplexiteten. Integreringen av Manufacturing Execution Systems (MES) och Enterprise Resource Planning (ERP) möjliggör insamling och visualisering av realtidsdata för att optimera produktionsprocesserna. Trots detta möter industrier hinder vid implementering av ERP-system, vilket minskar potentialen för optimal användning. Dessa utmaningar inkluderar brist på noggrann analys och planering, otillräcklig användarutbildning samt systemutvecklares tendens att prioritera teknik- eller produktanpassning framför användaranpassning. Denna studie fokuserar på att identifiera problem vid implementering av ERP-system samt utforska strategier för att optimera systemets användning. Forskningsfrågan som denna studie bevarar är: Vilka faktorer är väsentliga vid implementering av ERP-system inom tillverkande industrier för att uppå en mer optimal användning och uppföljning? Studien genomförs på Scanias nya gjuteri och företagets störningsuppföljningssystem, EBBA. Med hjälp av empiriska data, i form av semistrukturerade intervjuer och observationer på gjuteriet, samt stödjande litteraturstudier har ett resultat framställts. En framgångsrik implementering av ERP-system kräver användarvänliga system, kontinuerlig utbildning, kommunikation, ständigt förbättringsarbete, engagerat ledarskap samt resurstillgänglighet. För ledande tillverkningsföretag som Scania AB, Volvo AB, Saab AB och Volvo Cars AB utgör ett effektivt inrapporteringssystem en central del för att förbättra produktionskapaciteten och stärka konkurrenskraften. Genom att möjliggöra optimal användning och störningsidentifiering kan företagen effektivisera tillverkningsprocesserna och förbättra kvaliteten på sina produkter. Detta bidrar även till den hållbara utvecklingen och de globala målen, till exempel SDG 8 “Anständiga arbetsvillkor och ekonomisk tillväxt”, SDG 9 “Hållbar industri, innovationer och infrastruktur" och SDG 12 “Hållbar konsumtion och produktion”. / The modern manufacturing industry is constantly changing to meet the demands of society and the market. Industry 4.0 has introduced increased digitization and automation, which requires smooth and efficient reporting systems to manage the growing complexity. The integration of Manufacturing Execution Systems (MES) and Enterprise Resource Planning (ERP) enables the collection and visualization of real-time data to optimize production processes. However, industries face obstacles in the implementation of ERP systems, which reduces the potential for optimal use. These challenges include a lack of thorough analysis and planning, insufficient user training, and a tendency among system developers to prioritize technological or product adaptation over user-centric design. This study focuses on identifying problems in ERP system implementation and exploring strategies to optimize system usage. The research question addressed in this study is: What factors are essential in the implementation of ERP systems in manufacturing industries to achieve a more optimal usage and follow-up? The study is conducted at Scania's new foundry and the company's disruption monitoring system, EBBA. Using empirical data from semi-structured interviews and observations at the foundry, along with supportive literature studies, a conclusion has been drawn. Successful ERP system implementation requires user-friendly systems, continuous training, communication, continuous improvement efforts, committed leadership, and resource availability. For leading manufacturing companies such as Scania AB, Volvo AB, Saab AB, and Volvo Cars AB, an efficient reporting system is essential to improve production capacity and enhance competitiveness. By enabling optimal usage and disruption identification, companies can streamline manufacturing processes and improve the quality of their products. This also contributes to sustainable development and global goals, such as SDG 8 "Decent Work and Economic Growth", SDG 9 "Industry, Innovation, and Infrastructure" and SDG 12 "Responsible Consumption and Production".

This study project looks at how metrology and inspection methods have advanced inside machining processes to improve production efficiency, accuracy, and precision. The study includes an extensive examination of the literature, observational research, and critical analysis to provide insights into the possibilities, difficulties, and practices that exist in the sector today. The literature review charts the development of metrology from manual measurement techniques to highly automated systems, emphasizing the crucial roles that optical inspection systems and coordinate measuring machines (CMMs) play in contemporary production. Modern inspection technologies, including as non-contact techniques and laser scanning, are also discussed, along with the difficulties in combining metrology data with digital production systems. Observational research offers direct knowledge of how metrology and inspection techniques are used in real-world industrial settings. Alongside difficulties including operator skill levels and equipment calibration, the usage of portable measuring tools, CMMs, and optical inspection systems for assessing surface finish and dimensional correctness is noted. Findings study points to growing problems such the requirement for experienced operators and operational unpredictability in machining processes, as well as the increasing integration of metrology and inspection methods into machining processes. But there are also prospects for development, with an emphasis on the ability to increase efficiency and quality control, via Industry 4.0 projects and additive manufacturing technology. All things considered, this study project clarifies the crucial function metrology and inspection methods play in contemporary manufacturing and provides guidance for enhancing quality assurance and machining process efficiency.

This thesis investigated the possibility of using machine learning algorithms for classifying items in a queuing system to optimize a production line. The evaluated algorithms are Artificial Neural Network (ANN) and Support Vector Machine (SVM), selected based on other research projects. A quasi-experiment evaluates the two machine learning algorithms trained on the same data. The dataset used in the experiment was complex and contained 47,212 rows of samples with features of items from a production setting. Both models performed better than the current system, with ANN reaching 97,5% and SVM 98% on all measurements. The ANN and SVM models differed in training time where ANN took almost 205 seconds and SVM took 1.97 seconds, ANN was however 20 times faster to classify. We conclude that ANN and SVM are feasible options for using Artificial Intelligence (AI) to classify items in an industrial environment with similar scenarios.

Large Scale Additive Manufacturing (LSAM) technology has emerged as a transformative force in the manufacturing industry. Using robots and advanced material deposition systems, LSAM facilitates the efficient creation of large-scale components with exceptional precision and reduced production times. Beyond its capacity to manufacture complex structures, this technology drives innovation by fostering the exploration of new materials and designs, opening new frontiers in the aerospace, automotive, and construction sectors. The synergy between robotics and additive manufacturing in LSAM represents a significant advancement toward the future of manufacturing, where customization, efficiency, and sustainability are paramount.  Within the framework of this research, conducted in collaboration with ABB and RISE, a series of tests have been developed to optimize printing parameters when transitioning from one material to another. This study showcases how simple adjustments in the workflow of robotic stations can lead to significant improvements in print quality and increased resource efficiency, paving the way for more precise and sustainable manufacturing.  This research has not only generated valuable insights into the behavior of LSAM under different operating conditions but also provided practical solutions for its continuous improvement. These findings are relevant for the continuous improvement of large-scale additive manufacturing processes and provide important insights for future research in the fields of robotics and advanced manufacturing. Additionally, a systematic methodology has been developed to evaluate and validate results, ensuring the reliability and reproducibility of findings. These achievements solidify the role of LSAM as a fundamental technology in the evolution of the manufacturing industry toward a more efficient and sustainable future.

Date:    2nd June 2024 Level:    Master Thesis in Product- and Process Development, Advanced     level, 30 credits                                                                                  Institution:   School of Innovation, Design and Engineering, Mälardalens University Authors:   Farin Hasan & Soumik Karim Title:    Role of digitalization and digital capabilities to implement second-life business models for electric vehicle batteries Keywords:   Second-life EV batteries, Digitalization in circular economy, Industry 4.0 technologies, EV battery life cycle, Circular economy models, Battery recycling/repurposing Purpose:   The aim of this thesis study is to develop and evaluate a digitalization framework with the goal of improving the second life management of EV batteries.  Research questions:  “What type of Industry 4.0 technologies and digital capabilities are key for the implementation of second-life business models for EV batteries and how do they support ecosystem actors?”  , “How should ecosystem actors prepare and integrate these     technologies and capabilities to implement second-life business models for EV batteries? Methodology:   The study was conducted using an abductive research approach,   employing qualitative methods. The theoretical framework was developed through a systematic literature review of existing literature within the research scope. Data collection involved an  extensive literature review and archival study. Thematic analysis was applied to the data to identify patterns and themes, which were further analyzed to construct a framework.                                                     Conclusion:   The study concludes that integrating industry 4.0 technologies enhances the management and repurposing of EV batteries for second-life applications. These technologies enable effective monitoring, predictive maintenance, and secure data management, which supports ecosystem actors in developing sustainable and economically viable second-life business models.

Tesis por compendio / [ES] La innovación en los métodos de trabajo con nuevos sistemas de producción ajustados, sostenibles y resilientes, así como la gestión tecnológica eficaz son las tendencias actuales para mejorar el rendimiento en las organizaciones. Así, esta tesis doctoral investiga las contribuciones teóricas y formula un modelo conceptual con el uso de herramientas de fabricación ajustada o lean manufacturing (LM) y de la industria 4.0 (I4.0) para procesos de planificación de la producción en las cadenas de suministro. Además, se utiliza la optimización como tecnología facilitadora de la I4.0 para desarrollar la propuesta de solución. La metodología que se utiliza es bibliográfica, exploratoria y experimental; se aplican técnicas de investigación operativa, normalización de datos y casos de estudio en empresas industriales. Como punto de partida se revisa la literatura existente relacionada con la LM y las tecnologías de la I4.0 en el contexto de la cadena de suministro. Seguidamente, se diseña un modelo conceptual denominado LSCP 4.0 para facilitar la toma de decisiones en los niveles de decisión estratégico, táctico y operativo. Se trata de una relación estructurada entre los paradigmas lean, ágil, sostenible, resiliente y flexible para mejorar el rendimiento de las cadenas de suministro mediante la aplicación de las tecnologías facilitadoras de I4.0. Basado en esto, se propone un modelo matemático de optimización lineal entera-mixta, denominado LSCP 4.0, para maximizar los beneficios y planificar simultáneamente la producción, inventario de materiales y productos terminados satisfaciendo la demanda proveniente de previsiones y pedidos en firme en una cadena de suministro de cinco niveles. Novedosamente, se combinan enfoques de producción de just in time (JIT) y planificación de requerimientos de materiales (MRP). Ambos modelos para LSCP 4.0, i.e., conceptual y matemático, se validan en casos de estudio reales de la industria de calzado. Adicionalmente, se considera el uso de otra tecnología facilitadora de la I4.0 como la computación en nube para abordar el problema del intercambio de información entre los nodos de la cadena de suministro. Así, se propone un modelo de datos normalizado para la fabricación colaborativa en la nube aplicado a la industria del calzado. / [CA] La innovació en els mètodes de treball amb nous sistemes de producció ajustats, sostenibles i resilients, així com la gestió tecnològica eficaç són les tendències actuals per a millorar el rendiment en les organitzacions. Així, aquesta tesi doctoral investiga les contribucions teòriques i formula un model conceptual amb l'ús d'eines de fabricació ajustada o lean manufacturing (LM) i de la indústria 4.0 (I4.0) per a processos de planificació de la producció en les cadenes de subministrament. A més, s'utilitza l'optimització com a tecnologia facilitadora de la I4.0 per a desenvolupar la proposta de solució. La metodologia que s'utilitza és bibliogràfica, exploratòria i experimental; s'apliquen tècniques d'investigació operativa, normalització de dades i casos d'estudi en empreses industrials. Com a punt de partida es revisa la literatura existent relacionada amb laLM i les tecnologies de la I4.0 en el context de la cadena de subministrament. Seguidament, es dissenya un model conceptual denominat LSCP 4.0 per a facilitar la presa de decisions en els nivells de decisió estratègic, tàctic i operatiu. Es tracta d'una relació estructurada entre els paradigmes lean, àgil, sostenible, resilient i flexible per a millorar el rendiment de les cadenes de subministrament mitjançant l'aplicació de les tecnologies facilitadores d'I4.0. Basat en això, es proposa un model matemàtic d'optimització lineal entera-mixta, denominat LSCP 4.0, per a maximitzar els beneficis i planificar simultàniament la producció, inventari de materials i productes acabats satisfent la demanda provinent de previsions i comandes en ferma en una cadena de subministrament de cinc nivells. Novament, es combinen enfocaments de producció de just in time (JIT) i planificació de requeriments de materials (MRP). Tots dos models per a LSCP 4.0, i.e., conceptual i matemàtic, es validen en casos d'estudi reals de la indústria de calçat. Addicionalment, es considera l'ús d'una altra tecnologia facilitadora de la I4.0 com la computació en núvol per a abordar el problema de l'intercanvi d'informació entre els nodes de la cadena de subministrament. Així, es proposa un model de dades normalitzat per a la fabricació col·laborativa en el núvol aplicat a la indústria del calçat. / [EN] Innovation in working methods with new lean, sustainable and resilient production systems, as well as effective technology management, are current trends to improve performance in organisations. This PhD thesis investigates the theoretical contributions and formulates a conceptual model with the use of lean manufacturing (LM) and Industry 4.0 (I4.0) tools for production planning processes in supply chains (SCs). In addition, optimisation is employed as an enabling technology of I4.0 to develop the proposed solution. The applied methodology is bibliographic, exploratory and experimental; operational research techniques, data standardisation and case studies in industrial companies are applied. As a starting point, the existing literature related to LM and I4.0 technologies in the SC context is reviewed. Then a conceptual model, known as LSCP 4.0, is designed to facilitate decision making at the strategic, tactical and operational decision levels. It is a structured relation among lean, agile, sustainable, resilient and flexible paradigms to improve SCs' performance via the application of I4.0 enabling technologies. Based on this, an integer-mixed linear optimisation mathematical model, termed LSCP 4.0, is proposed to maximise profits and to simultaneously plan production, material inventory and finished goods by satisfying the demand from forecasts and firm orders in a five-tier SC. Novel just-in-time (JIT) production and material requirements planning (MRP) approaches are combined. Both models for LSCP 4.0, i.e., conceptual and mathematical, are validated in real case studies from the footwear industry. The use of another I4.0 enabling technology, such as cloud computing, is considered to address the problem of information exchange between SC nodes. Thus a standardised data model for collaborative manufacturing in the cloud applied to the footwear industry is proposed. / This thesis has been developed at the Research Centre on Production Management and Engineering (CIGIP) of the Universitat Politècnica de Valencia, within the framework of the projects: "Industrial Data Services for Quality Control in Smart Manufacturing (i4Q)" funded by the European Union H2020 Programme with grant agreement No. 958205; the MCIN/AEI/10.13039/501100011033 and by European Union Next GenerationEU/PRTR with grant agreement PDC2022-133957-100); "Industrial production and loginics optimization in industry 4.0 (i40PT)" funded by the Generalitat Valenciana within the framework of the research groups of excellence project PROMETEO/2021/065; "Optimisation of zero-defects production technologies enabling supply chains 4.0 (CADS4.0)" funded by the Spanish Ministry of Science, Innovation and Universities with grant agreement RTI2018-101344-B-100; "Operational Programme of the European Regional Development Fund (ERDF) of the Autonomous Valencian Community 2014-2020" (Ref. IDIFEDER/2018/025); "Resilient, Sustainable and People-Oriented Supply Chain 5.0 Optimisation Using Hybrid Intelligence" (RESPECT) (Ref. CIGE/2021/159); and a PhD grant from the Technical University of Ambato. / Reyes Vásquez, JP. (2024). Conceptual and optimisation modelling for lean supply chain planning in Industry 4.0 [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/203818 / Compendio