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

IO-Link is a constantly growing market, bringing more interested parties who want to know if it is a market worth investing in. While deciding if the market is worth investing in is for the companies to decide, this thesis aims to shed some light on what it takes to get started with IO-Link development which could help that decision. This work presents the development of a proof-of-concept IO-Link master stack, which is then evaluated technically and financially. The proof-of-concept stack was then evaluated by testing it with a commercially available IO-Link Device (KT6101). This resulted in a functional proof-of-concept master stack with the physical, data link and application layer implemented, with ISDU support. The master stack supports cyclic Process Data exchange and acyclic On-request Data exchange, which is the minimum needed for IO-Link operations with a fully implemented IO-Link Device. The financial evaluation provides insights about the cost of developing the stack in Sweden, and the varying costs stemming from the place where the development occurs, since the host company has a global scope. Information from this work is a tool for stakeholders and decision makers in regard of the financial viability of the project, while the technical evaluation of the proof-of-concept is positive.

Försörjningskedjan består av ett komplext system med flera avgörande variabler som kostnad, tid, kvalitet, flexibilitet och produktivitet i tillverkningsindustrin. Samtidigt ställs det hårdare krav från konsumenterna och marknaden som påverkar verksamhetens konkurrenskraft. En del av dessa krav avser kedjans egna målsättningar, men även regleringar. Strävandet efter smarta flöden och system, samt övergången till industri 4.0 medför att nya tekniker tillämpas i syfte att uppfylla dessa krav och optimera flöden och processer. Informationsflödet har en avgörande roll för att kunna bemöta tillgång och resurser från ett kortsiktigt och långsiktigt perspektiv. Information och data påverkar försörjningskedjans output och processer som är alltifrån konsumentens val att köpa produkten till tillverkningsprocessens olika mekanismer. Det innefattar även andra viktiga och avgörande parametrar om kräver beslutsfattande. Bristande data från medverkande aktörer i försörjningskedjans nätverk avseende produktursprung, samt tillförlitlighet av data om produkter och processer påverkar beslutsfattanden, konkurrenskraften och effektiviteten. Emellertid finns det en bristande förståelse om vad blockkedjan innebär och dess tillämpningsområden i tillverkningsindustrin. I den nya globala ekonomin framstår blockkedjetekniken som en central fråga för effektivisering av processer och flöden. Förståelsen för blockkedjetekniken har därför visat sig vara en viktig faktor för att identifiera värden och fatta investeringsbeslut i en verksamhet. Detta arbete undersöker och studerar blockkedjetekniken i försörjningskedjan, riktat till tillverknings-industrin. Syftet med studien är att studera mekanismer, kriterier och omständigheter som möjliggör värdegenerering i försörjningskedjan. Uppdraget sker i syfte att bidra till djupgående förståelse för intressenter och aktörer inom tillverkningsindustrin. De huvudsakliga värdegenererande möjligheterna avser de vanligaste målen i försörjningskedjan som grundar sig i litteraturen. Detta i sin tur medför en förenklad förståelse om metoden för olika typer av industrier och vägledning om vilka problem det löser. Resultatet påvisade vikten av teknisk kompetens, samt att värdemöjligheterna är relativ i förhållande till syfte och behov. Blockkedjeteknikens huvudsakliga funktioner medför transparens, spårbarhet och tillförlitlighet som i sin tur leder till effektiv informationshantering. Det kan därför generera värden för verksamheter för att minska kostnader, öka kvalitet, flexibilitet, responsivitet, tillförlitlighet och andra hållbarhetskriterier. En av dem lämpligaste konsensusalgoritmerna för försörjningskedjan är därför PoA, samt en stängd och privat blockkedja. Detta medför både säkerhet och effektivitet för informationsflödet. Det kan även bestå av en egen intern algoritm och blockkedja. De identifierade värdena kan inte generaliseras för alla verksamheter och skiljer sig därför beroende på verksamhet. Beslutsfattandemodeller som Birch-Brown-Parulava modellen och Wüst - Gervais modellen kan användas för behovsidentifiering för verksamheter som överväger att tillämpa tekniken. Dessa modeller beaktar de tekniska funktionerna och hållbarhetsaspekterna vid tillämpning. Arbetet genomfördes genom en kvalitativ metod genom att studera litteratur och intervjua forskare och experter. Intervjuerna genomfördes med personer som har ingående och kontextuell förståelse för ämnet. Detta möjliggjorde undersökning av det underliggande lagret av problemet som i sin tur medförde reliabilitet och tillförlitlighet av studien. / Supply chains have evolved into complex systems with numerous factors that influence key supply chain objectives. It is largely determined by objectives such as cost, time, quality, flexibility, reliability, and productivity in the manufacturing industry. Growing market pressures along with higher customer requirements impact a company's overall competitiveness. Among the requirements are the need to meet the objectives of the supply chain, as well as compliance and sustainability criteria. Intelligent operations and systems provide opportunities for new technologies as a result of the industry 4.0 vision. This in return meets requirements and optimizes operations and processes to achieve various supply chain objectives. Thus, information flows are critical to satisfying short- and long-term supply and demand. Data and information play a key role at every point of the supply chain, from product source to final consumption. Decisions and assessments of data in different processes are affected, which also impacts the consumer. Insufficient information about the sources of materials, products, and components in the supply chain network, as well as reliability issues related to certain parameters, are adversely affecting the network. Consequently, this impacts the decision-making process, competitiveness, and efficiency of the supply chain. Information and data can be managed by facilitating the flow of information through blockchain technologies (BCT). However, there is a lack of contextual insight about blockchain as a method and the potential application areas in the manufacturing industry. This creates challenges in determining decision-making strategies for conditions facilitating value and how BCT should be evaluated before being implemented. This is one of the major issues regarding the efficiency of flows and processes in the new global economy. In order to identify organizational values and value enablers, understanding the technology is essential. This study investigates the use of blockchain technology in supply chains, aimed at the manufacturing industry. BCT mechanisms, functions and algorithms were studied to cultivate value-enabling possibilities. The secondary purpose of this study was to provide contextual and profound understanding for stakeholders and actors in the manufacturing sector. The value enablers are identified according to the objectives and ambitions outlined in the literature. Hence, it simplifies the decision-making processes. However, the study emphasized technical competence and demonstrated how potential values are dependent on purpose and need. Among the principal functions of BCT are: transparency, traceability, interoperability, compliance, and reliability resulting in efficient information management. Thus, it can contribute to reducing costs, enhancing quality, flexibility, responsiveness, reliability, and other sustainability metrics. One of the most appropriate and widely used consensus algorithms for the supply chain is PoA (Proof of Authority), which is closed and private. This algorithm contributes to security and efficiency of information flows. Another option is to build a blockchain algorithm internally for the purpose of customizing the solution. There is no generalizability to the identified values for all organizations, so they may vary between them. The Birch-Brown-Parulava and Wüst-Gervais decision-making models are suitable for identifying investment needs and creating implementation strategies. The models take technical and organizational needs into account as well as sustainability concerns. This study was carried out using a qualitative approach that included a review of the literature as well as an empirical examination. Interviews were conducted with individuals with a thorough understanding and expertise within the context of BCT and the supply chain. Studying the underlying layer of problems lead to the enhanced reliability of this study.

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

Purpose - Procurement is an integrated part of the supply chain and crucial for the success of manufacturing. Many organisations have already started the digitalisation of their manufacturing processes using Industry 4.0 technologies and consequently trying to understand how this would impact the procurement function. The research purpose is to conceptualize a procurement of 4.0 model for a truly data driven procurement. Two research questions were proposed to address the model from digital capabilities and sustainability preceptive. Design/Methodology/approach - This study is based on a systematic literature review. A method of reviewing the literature and the current research for the propose of conceptualizing a procurement 4.0 model. Findings - The findings from the literature review contributed to the development of a proposed procurement 4.0 model based on Industry 4.0 technologies, applications, mathematical algorithms and procurement processes automation. The model contributes to the research field by addressing the gap in the literature about the lack of visualization and conceptualization of procurement 4.0. Originality/Value - The current literature discusses the advantages, implementation and impact of individual or a group of industry 4.0 technologies and applications on procurement but lacks visualization of the transformation process of combining the technologies to enable a truly data driven procurement. This research supports the creation of knowledge in this area. Practical Implementation /Managerial Implications - The proposed model can support managers and digital consultants to have practical knowledge from an academic perspective in the area of procurement 4.0. The knowledge from the literature and the systematic literature review is used to create knowledge on procurement 4.0 applications and analytics taking in to consideration the importance of visibility, transparency, optimization and the automation of the procurement function and its sustainability. / Syfte - Upphandling är en integrerad del av supply chain och avgörande för tillverkningens framgång. Många organisationer har redan börjat digitalisera sina tillverkningsprocesser med hjälp av Industry 4.0-teknologier och försöker därför förstå hur detta skulle påverka upphandlingsfunktionen. Målet med studien är att konceptualisera en upphandling av 4.0-modellen för en verkligt datadriven upphandling. Två forskningsfrågor föreslogs för att ta itu med modellen från digital kapacitet och hållbarhet. Design / metod / tillvägagångssätt - Denna studie baseras på en systematisk litteraturstudie. En metod för att granska litteraturen och den aktuella forskningen för att föreslå konceptualisering av en upphandlings 4.0-modell. Resultat - Resultaten från litteraturstudien bidrog till utvecklingen av en föreslagen upphandlings 4.0-modell baserad på Industry 4.0-teknologier, applikationer, matematiska algoritmer och automatisering av upphandlingsprocesser. Modellen bidrar till forskningsområdet genom att ta itu med klyftan i litteraturen om bristen på visualisering och konceptualisering av upphandling 4.0. Originalitet / värde - Den nuvarande litteraturen diskuterar fördelarna, implementeringen och effekten av individer eller en grupp av industri 4.0-teknologier och applikationer på upphandling men saknar visualisering av transformationsprocessen för att kombinera teknologierna för att skapa en verklig datadriven upphandling. Denna forskning stöder skapandet av kunskap inom detta område. Praktisk implementering / chefsimplikationer - Den föreslagna modellen kan stödja chefer och digitala konsulter att ha praktisk kunskap ur ett akademiskt perspektiv inom området upphandling 4.0. Kunskapen från litteraturen och den systematiska litteraturstudien används för att skapa kunskap om inköp 4.0 applikationer och analyser med beaktande av vikten av synlighet, transparens, optimering och automatisering av upphandlingsfunktionen och dess hållbarhet.

Den fjärde industriella revolutionen (Industri 4.0) anses enligt forskare och internationella röster ha enorm inverkan på individens framtida karriärupplevelser, vilket fortfarande är obeforskat. Därmed syftar studien till att undersöka individens karriärupplevelse med anledning av smart teknologi inom Industriell 4.0. Studien är av kvalitativ karaktär med empiri från tio yrkesverksamma personer i olika redovisningsbyråer med AI- eller automatiseringsteknik. Resultatet visar på individuella skillnader i karriärupplevelsen samt i upplevda möjligheter och hinder på den fortsätta karriären i organisationen. Begreppet karriär har i studien bestått av erfarenheter som möjliggör professionell utveckling och som bidrar till individens anställningsbarhet. Den smarta tekniken tillför med klart professionellt avancemang – ifall organisationer haft lyckade transformationer och implementeringar – men bidrar även med minskad anställningsbarhet för vissa grupper. Dessa är nyutexaminerade och individer utan teknisk kompetens. Individer med teknisk kompetens intar därmed ett försprång i sin karriärutveckling och karriärjustering i syfte att hålla sig anställningsbara i en framtid präglad av smart teknologi.

Larger companies have adapted industry 4.0 and it’s benefits while SMEs still face significant barriers such as limited resources and knowledge gaps. By exploring AI implementation, this research aims to provide insight into the suitability and prerequisites for implementation of various AI solutions in automation on manufacturing SMEs, helping them overcome these challenges and remain competitive on the market. To answer the questions a literature review was conducted followed by two case studies at manufacturing SMEs. The case studies involved semi-instructed interviews and observations at site. The information gained was used to analyse and acquire relevant conclusions. The analysis highlights factors affecting AI implementation in manufacturing systems, such as data quality, economic resources, competence development, manager support etcetera are mentioned. Additionally, methods for AI implementation, including ML cloud computing, robotics and Decision support system are discussed. Furthermore, the case studies illustrate exemplify the challenges and aspirations of SMEs in adopting AI, showcasing their varying levels of readiness and approaches towards automation and IT integration. The result show that the lack of competence is a major barrier for SMEs in AI implementation. Both companies recognize the need to digitalize to remain competitive in the market. It is also shown that AI implementation requires the right expertise and support from management to succeed. SMEs also need to start collecting relevant data since it is an identified prerequisite for implementation. This study implies to manufacturing SMEs and how they can implement AI, identifying their needs. It also implies to AI developers that want to create SME-specific tools and helps consultants provide SMEs with accurate advice. The discussion highlights that SMEs are not yet ready for huge AI technology since they lack many of the previously mentioned prerequisites. They should however start with implementing other technology from industry 4.0 since they are highly needed to become more digitalized. The methodology is also discussed and talked about how a triangulation method was used and can help to strengthen the quality of this research. / Större företag har anpassat sig till Industri 4.0 och dess fördelar, medan små och medelstora företag (SME) fortfarande möter betydande hinder som begränsade resurser och kunskapsluckor. Genom att utforska AI-implementering syftar denna forskning till att ge insikt i lämpligheten och förutsättningarna för att implementera olika AI-lösningar inom automatisering för tillverkande SMEer, vilket hjälper dem att övervinna dessa utmaningar och förbli konkurrenskraftiga på marknaden.   För att besvara frågorna genomfördes en litteratur undersökning följt av två fallstudier vid tillverkande SMEer. Fallstudierna involverade semi-strukturerade intervjuer och observationer på plats. Informationen som erhölls användes för att analysera och dra relevanta slutsatser. Analysen belyser faktorer som påverkar AI-implementering i tillverkningssystem, såsom datakvalitet, ekonomiska resurser, kompetensutveckling, chefsstöd med mera. Dessutom diskuteras metoder för AI-implementering, inklusive maskininlärning, molntjänster, robotik och beslutsstödsystem. Vidare illustrerar fallstudierna de utmaningar och ambitioner som SMEer har vid antagandet av AI och visar deras varierande nivåer av beredskap och tillvägagångssätt för automatisering och IT-integration.   Resultaten visar att bristen på kompetens är ett stort hinder för SMEer vid AI-implementering. Båda företagen erkänner behovet av att digitalisera för att förbli konkurrenskraftiga på marknaden. Det framgår också att AI-implementering kräver rätt expertis och stöd från ledningen för att lyckas. SMEer måste även börja samla in relevant data eftersom detta är en identifierad förutsättning för implementering. Denna studie riktar sig till tillverkande SMEer och hur de kan implementera AI genom att identifiera deras behov. Den riktar sig även till AI-utvecklare som vill skapa verktyg specifika för SMEer och hjälper konsulter att ge SMEer korrekt rådgivning.   Diskussionen belyser att SMEer ännu inte är redo för storskalig AI-teknik eftersom de saknar många av de tidigare nämnda förutsättningarna. De bör dock börja med att implementera annan teknik från Industri 4.0 eftersom den är mycket nödvändig för att bli mer digitaliserade. I metodologin diskuteras också och hur en trianguleringsmetod användes, vilket bidrar till att stärka kvaliteten på denna forskning.

Yes / Advancing Industry 4.0 concepts by mapping the product of the automotive industry on the spectrum of Cyber Physical Systems, we immediately recognise the convoluted processes involved in the design of new generation vehicles. New technologies developed around the communication core (IoT) enable novel interactions with data. Our framework employs previously untapped data from vehicles in the field for intelligent vehicle health management and knowledge integration into design. Firstly, the concept of an inter-disciplinary artefact is introduced to support the dynamic alignment of disparate functions, so that cyber variables change when physical variables change. Secondly, the axiomatic categorisation (AC) framework simulates functional transformations from artefact to artefact, to monitor and control automotive systems rather than components. Herein, an artefact is defined as a triad of the physical and engineered component, the information processing entity, and communication devices at their interface. Variable changes are modelled using AC, in conjunction with the artefacts, to aggregate functional transformations within the conceptual boundary of a physical system of systems. / Jaguar Land Rover funded research “Intelligent Personalised Powertrain Healthcare” 2016-2019

This research presents a mathematical model for the transportation and distribution of COVID-19 vaccine, a simulation model for fleet optimization, and a measurement model for "Healthcare 4.0." Essay 1 examines the development of a distribution model using mixed integer programming (MIP) with the objective of maximizing the number of vaccinated individuals, minimizing transportation costs across the entire network, and ensuring widespread access. This research primarily focuses on the distribution aspect of the vaccine and accordingly devises a model for transportation and distribution that ensures swift and efficient delivery of the COVID-19 vaccine. Essay 2 provides a simulation-based model to enhance logistics performance by including drones along with vaccine trucks and air cargo in the vaccine distribution fleet. The simulation model focuses on minimization of the overall cost of distribution of medical supplies. This second study shows that the types of vehicles utilized have an impact on overall system performance. The selection of the appropriate mix for the mode of transportation impacts transportation costs and lead time. To increase the responsiveness and cost-effectiveness of the logistics system for delivery of the vaccine a proper fleet configuration is required. The model developed in this study is validated via application in Telangana, India as well as through confirmation about the applicability of the model with healthcare executives. Essay 3 introduces a measurement model and constructs for Healthcare 4.0, specifically tailored for implementation by healthcare service providers. While the concept of Healthcare 4.0 and its various components have been explored in the literature, the existing body of research primarily consists of conceptual and theoretical studies, indicating that Healthcare 4.0 is still a relatively nascent research domain. In order to facilitate practical and theoretical advancements in this field, it is imperative to refine the constructs and establish a consensus on perspectives and definitions. To address this need, the items pertaining to Healthcare 4.0 for healthcare service organizations were developed through an extensive literature review and interviews conducted with practitioners in the field. The resulting theoretical model was further validated by surveying experienced professionals from the healthcare industry, utilizing Mturk as a platform.

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.