Den globala industrin har under det senaste decenniet genomgått en enorm digital transformation, där tillämpandet av digitala och smarta verktyg inom företag aldrig har varit mer påtagligt. Under november 2011 presenterades begreppet Industrial 4.0 i en artikel skriven av den Tyska regeringen som beskriver en teknikintensiv strategi för år 2020 och omfattar vad idag betraktas som den fjärde industriella revolutionen. Industri 4.0 utgörs till stor del av integrationsprocessen mellan teknik och övrig verksamhet inom ett tillverkningsföretag, vilket i sin tur ger upphov till teknik såsom; automation, förstärkt verklighet, simuleringar, intelligenta tillverkningsprocesser samt övriga processindustriella IT-verktyg och -system. Flertal forskningsstudier hävdar att Industri 4.0-teknologier har potential att revolutionera sättet företag idag tillverkar produkter, men i och med att begreppet är relativt nytt, abstrakt samt består av väldigt komplexa tekniker och komponenter, är införandet av dessa inom en tillverkningsmiljö för närvarande en stor utmaning för tillverkande företag. Denna studie syftar alltså till att belysa de utmaningar och hinder som större tillverkande företag möter vid implementering av digital och smart teknik, samt åtgärder för att motverka dessa. Målet med studien är att leverera ett användbart resultat både för aktiva företag inom tillverkningsindustrin i form av stöd vid analys och diskussion av eventuella implementeringsstrategier och -satsningar inom Industri 4.0, men också ge övriga intressenter en uppfattning kring ämnet med tanke på att det, som sagt, är ett abstrakt system. En litteraturstudie genomfördes både för att få en överblick kring ämnet Industri 4.0 och hur det har behandlats i tidigare examensarbeten, avhandlingar samt forskningsstudier, men även för att identifiera tidigare identifierade hinder. Därefter genomfördes fältstudier på två tillverkande företag, Scania och Atlas Copco, samt teknikkonsultföretaget Knightec. Syftet med detta var framförallt att få en mer påtaglig och verklighetsförankrad uppfattning av Industri 4.0 men även verifiera att informationen i den teoretiska delen är relevant i praktiken för en tillverkande verksamhet. Studien påvisade därtill att identifierade utmaningar och hinder återfinns bland flertal organisatoriska områden inom ett tillverkande företag, varav de mest framgående aspekterna omfattade strategi, ledarskap, kunder, kultur, anställda, juridik samt teknik. Resultatet avslöjade vidare att tillverkande företag präglas av bristfälliga planer och strategier för att identifiera samt implementera nya tekniska lösningar, konflikter bland de anställda, svårigheter att integrera kundsystem enhetligt inom produktionen, avsaknad av lämplig teknisk kompetens, juridiska problem vad gäller hantering av data samt svårigheter att integrera nya och gamla teknologier. / The global industry has during the last decade undergone a considerable digital transformation, whereas the application of digital and smart technology within companies has never been more of a relevant field. During November of 2011, the term Industrial 4.0 was presented in an article written by the German government to describe a technology intensive strategy for the year 2020 and signifies what today is defined as the fourth industrial revolution. Industry 4.0 largely consists of the integration process between technology and remaining operations within a manufacturing company, which enables the development of technologies such as; automation, augmented reality, simulations, intelligent manufacturing processes and other process industrial IT-tools and systems. Several research studies has suggested that Industry 4.0 technologies has the potential to revolutionize the way companies today manufacture products, however, since the concept is relatively new, abstract and consists of various complex technologies and components, the implementation process of these within a manufacturing environment is one largest challenges that manufacturing companies are facing. This study therefore aims to highlight the challenges and difficulties that large manufacturing companies are facing when implementing digital and smart technology, as well as provide solutions regarding how they can be overcome. The overall goal is to deliver useful results both for active companies within the manufacturing industry in regards to serving as support when analyzing and discussing possible implementation strategies as well investments related to Industry 4.0, but also to provide surrounding stakeholders with a perception of the subject. At the commencement of the project, a literature study was performed to develop an overview of how Industry 4.0 has been discussed in previous theses and research studies as well as to find previously identified difficulties regarding the implementation process. Finally, a field study was performed at Scania and Atlas Copco and at the technology consulting firm Knightec. The main purpose was to gain a more realistic perspective regarding how digitalization and Industry 4.0 systems are applied and to verify that the information from our theoretical study is relevant and applicable within an actual manufacturing company. The study furthermore revealed that the identified difficulties and challenges can be found within multiple organizational areas of a manufacturing company, whereas the most distinct aspects consisted of strategy, leadership, customers, culture, employees, legal governance as well as technology. The results showed that companies were characterized by an overall lack of strategy to implement new technologies, conflicts with employees during implementation, difficulties to integrate customer orders with production, lack of technical skills in staff, legal issues regarding data storage and difficulties integrating new and old technologies.

The Advanced Manufacturing Student Conference (AMSC) represents an educational format designed to foster the acquisition and application of skills related to Research Methods in Engineering Sciences. Participating students are required to write and submit a conference paper and are given the opportunity to present their findings at the conference. The AMSC provides a tremendous opportunity for participants to practice critical skills associated with scientific publication. Conference Proceedings of the conference will benefit readers by providing updates on critical topics and recent progress in the advanced manufacturing engineering and technologies and, at the same time, will aid the transfer of valuable knowledge to the next generation of academics and practitioners. *** The first AMSC Conference Proceeding (AMSC21) addressed the following topics: Advances in “classical” Manufacturing Technologies, Technology and Application of Additive Manufacturing, Digitalization of Industrial Production (Industry 4.0), Advances in the field of Cyber-Physical Systems, Virtual and Augmented Reality Technologies throughout the entire product Life Cycle, Human-machine-environment interaction and Management and life cycle assessment.:- Advances in “classical” Manufacturing Technologies - Technology and Application of Additive Manufacturing - Digitalization of Industrial Production (Industry 4.0) - Advances in the field of Cyber-Physical Systems - Virtual and Augmented Reality Technologies throughout the entire product Life Cycle - Human-machine-environment interaction - Management and life cycle assessment

The conference proceedings contain the abstracts of the 2nd edition of the Advanced Manufacturing Student Conference (AMSC). The second edition of the conference (AMSC22) was again jointly organised by the Faculty of Mechanical Engineering at Chemnitz University of Technology and the Fraunhofer Institute for Machine Tools and Forming Technology. The AMSC is an educational format designed to promote the acquisition and application of skills related to Research Methods in Engineering Sciences. Participating students are required to write and submit a conference paper and are given the opportunity to present their findings during the conference. The conference itself is intended to serve as a platform for networking at an early career stage. Thus, the AMSC22 provides a tremendous opportunity for participants to practice critical skills associated with scientific publication. The conference proceedings provide a broad overview of the field of advanced manufacturing.:# Foreword # Scientific Committee & Board of Reviewers # Session 1 - A study of the contribution of Additive Manufacturing towards Environmental Sustainability - An analysis about the role of Inkjet devices in Additive Manufacturing - The impact of selected process parameters on dimensional accuracy of fused deposition modeling manufactured parts - Part Cooling Techniques in Additive Manufacturing Process - Effects of Cooling Techniques in WAAM - A Review on Wear Resistance of High-Entropy Alloy Coatings - Importance of Augmented Reality in Automotive Industry - Additive Manufacturing in Aerospace Industry - An Overview of 3D Printing in the Healthcare Industry # Session 2 - Comparison of Additive Manufacturing and Traditional Manufacturing - An analysis on multi-material printing using methods of Additive Manufacturing - A Review on Use of Life Cycle Assessment in Decision-Making - A review of feasibility of diffusion bonding in additive manufacturing and its applications - The Life Cycle Engineering Analysis of Additive Manufacturing Technology - Enterprise Resource Planning - An Industry 4.0 Technology for Operational Efficiency - A review of process and application limits in the field of powder based Additive Manufacturing technologies - 3D Printing processes, materials in Industry 4.0 - A review on efficiency and productivity analysis of human-machine interaction in industry 4.0 # Session 3 - A review of Ultrasonic Additive Manufacturing Technology and Application - The effectiveness of combining rolling deformation with Wire–Arc Additive Manufacturing - A Review of Quality-Related Cost Accounting or Appraisal Methods Using the Example of Additive Manufacturing - Implementing visualization techniques to show the intended motion of Automated Guided Vehicles (AGVs) on the real environment: A review - Discussion on Additive Manufacturing Technology from an Environmental Perspective - Overview of cognitive manufacturing technology in production industries - A review on high velocity oxygen fuel thermal spraying in respective applications - Recognition of Additive section through Diffusion Bonding - Review of lattice structure defects in components manufactured by powder bed fusion process # Session 4 - Biomaterials used for Prosthetic/Implant 3d Printing - Comparison of Conventional and Additive Manufacturing Techniques from an Economic Perspective - Life Cycle Assessment on Natural Fiber Composites - A Review on Design Optimization for Additive Manufacturing - Industry 4.0: Impact on Environmental Sustainability - Review of cyber physical system integrated with augmented reality in various aspects of additive manufacturing - A review of scenarios in selection of milk Packaging by environmental impacts - Review of the integration of Life Cycle Assessment, Environmental Risk Assessment, and Ecosystem Services Assessment about human activities and environmental impact - A review on how Da Vinci surgical system is changing the health care # Session 5 - Integration of Digital twin in manufacturing industries, principal concept, and comparison - An overview of Powder-bed Fusion based Manufacturing process - A review on integration of carbon fiber materials in automotive industry - Review of generating the technologies of organic composite phase change materials in energy storage - Integration of Lean Manufacturing Principles and Industry 4.0 - Pultrusion of carbon/epoxy composites for aerospace applications - new developments and modelling studies - A Review on Stitching Methods for the Inkjet Nozzles - A Review of Digital Twin and its Potential Role in Technical System Development and Operations - Customization using additive manufacturing in industry 4.0 # Session 6 - Effect of Al2O3 particles and mustard oil as lubrication on machining process and a review - A Process-Oriented Review of Supply Chain Management in Automobile Industry - Environmental Impact Assessment on Recycling of Lithium-ion Battery - Methods and Applications of Multi-Material Additive Manufacturing - Fundamental analysis of 3D printing in Dentistry - A review of production methods of Carbon Nanotubes - A review of the environmental compatibility of superconductors - Pros and Cons of applying Fourth Industrial Revolution in a Production Plant: A Review - Intelligence Amplification as a cutting-edge forecasting method: investigation of AI applications, their impacts on our society and their path toward hybrid intelligent systems # Session 7 - Quality Engineering Challenges in Additive Manufacturing - Additive Manufacturing Techniques of 3D Model Hollowing Objects - Implementing Cyber-Physical Systems in Autonomous Micro/Nano Assembly Operations - Producing edge welded bellows seals by using laser welding - An Overview of Additive Manufacturing in the Automotive Industry: study cases and viability - A Review of Digital Twin in Manufacturing

Den fjärde industriella revolutionen, även kallad Industri 4.0, drivs av ett antal teknologier som medför digitalisering och automatisering av industriella processer. Konceptet innebär en applicering av dataanalys med avancerade analytiska verktyg på stora mängder data, vilka påstås ge stora möjligheter för kvalitetsförbättringar. För att en sådan övergång ska ske är förmågan att hantera data avgörande. Trots det uppvisar många företag idag bristande användning av data för att ta beslut. Frågan är hur företag kan göra för att hantera data och utföra en transformation till Industri 4.0. För att studera det här ämnet har det här examensarbetet utförts som en fallstudie på en stansprocess hos Scania Ferruform. Genom en litteraturstudie, kvantitativ datainsamling samt observationer och intervjuer undersökte examensarbetet den nuvarande användning av data i processen. Därefter undersöktes data med statistiska verktyg för att visa på hur data kan hanteras i en process för att erhålla större kunskap om orsaker till avvikelser. Examensarbetet utredde till sist hur fortsatt arbete med datahantering kan utföras för att uppnå målet Industri 4.0.Analysverktyg har använts för att analysera över 39 000 datapunkter. Resultatet visar på att det finns utvecklingsmöjligheter vad gäller insamling, kvalitet och användning av data. Ett ramverk presenteras för hur företaget bör hantera data för att kunna utvinna ny kunskap från deras processer samt hur Ferruform fortsatt kan arbeta mot Industri 4.0.Slutligen ges rekommendationer om fortsatta studier. Resultatet av examensarbetet blir ett stöd för Ferruform i deras arbete mot mer dugliga processer och den tekniska utveckling företaget eftersträvar. / The fourth industrial revolution, also called Industry 4.0 is powered by several technologies which result in digitalization and automatization of industrial processes. The concept includes the application of big data and advanced analytics, which are said to provide great opportunities for quality improvements. For such a transition to take place, the ability to handle data is crucial. Despite this, many companies today show a lack of use of data to drive decision-making. The question is how companies can manage data and ultimately transition towards Industry 4.0. To research this topic this thesis has been carried out as a case study of a punching process at Scania Ferruform. Through a literature review, quantitative data collection, as well as observations and interviews, the thesis examined the current use of data in the process. Subsequently, data were examined with statistical tools to illustrate how data can be managed in a process to attain increased knowledge about causes of deviations. Lastly, the thesis explored future work towards Industry 4.0. Analysis tools have been used to analyse over 39 000 data points. The result of the study shows that there are opportunities for development in terms of collection, quality and use of data. A framework of how Ferruform should manage data in order to extract new knowledge from its processes is presented. Furthermore, an action plan is presented for a transition towards Industry 4.0. Finally, recommendations are given for further studies. The result of the thesis will be helpful for Ferruform in its transition towards more efficient processes and the technical development of which the company strives towards.

[ES] La presente tesis doctoral está dedicada a estudiar, desarrollar y aplicar metodologías basadas en datos, fundamentadas en modelos estadísticos multivariantes de variables latentes, para abordar el paradigma del aprendizaje científico en el entorno de la Industria 4.0. Se pone especial énfasis en los modelos causales basados en variables latentes que utilizan tanto datos provenientes de un diseño de experimentos como, principalmente, datos provenientes del proceso de producción diario, es decir, datos históricos. La tesis está estructurada en cinco partes. La primera parte discute el paradigma del aprendizaje científico en el entorno de la Industria 4.0. Se destacan los objetivos de la tesis. Además, se presenta una descripción exhaustiva de los modelos basados en variables latentes, sobre los cuales se fundamentan las metodologías novedosas propuestas en esta tesis. En la segunda parte, se presentan las novedosas aportaciones metodológicas. En primer lugar, se muestra el potencial de PLS para analizar datos del DOE, con o sin datos faltantes. Posteriormente, el potencial de los modelos causales basados en variables latentes se centra en definir el espacio de diseño de la materia prima que proporciona garantía de calidad con un cierto nivel de confianza para los atributos críticos de calidad, junto con el desarrollo de un nuevo índice de capacidad multivariante basado en el espacio latente para clasificar y seleccionar proveedores para una materia prima particular utilizada en un proceso de fabricación. La tercera parte pretende abordar aplicaciones novedosas mediante modelos causales basados en variables latentes utilizando datos históricos. En primer lugar, se trata de su aplicación en el ámbito sanitario: la Pandemia COVID-19. En este contexto, se utiliza el uso de modelos basados en variables latentes para desarrollar una alternativa a los ensayos clínicos controlados con placebo. Luego, se utilizan modelos basados en variables latentes para optimizar procesos en el marco de aplicaciones industriales. La cuarta parte presenta una interfaz gráfica de usuario desarrollada en código Python que integra los métodos desarrollados con el objetivo de ser autoexplicativa y fácil de usar. Finalmente, la última parte discute la relevancia de esta disertación, incluyendo propuestas que merecen mayor investigación. / [CA] Aquesta tesi doctoral està dedicada a estudiar, desenvolupar i aplicar metodologies basades en dades, fonamentades en models estadístics multivariants de variables latents, per abordar el paradigma de l'aprenentatge científic a l'entorn de la Indústria 4.0. Es posa un èmfasi especial en els models causals basats en variables latents que utilitzen tant; dades provinents d'un disseny d'experiments com, principalment, dades provinents del procés de producció diari, és a dir, dades històriques. La tesi està estructurada en cinc parts. A la primera part es discuteix el paradigma de l'aprenentatge científic a l'entorn de la Indústria 4.0. Es destaquen els objectius de la tesi. A més, es presenta una descripció exhaustiva dels models basats en variables latents, sobre els quals es fonamenten les noves metodologies proposades en aquesta tesi. A la segona part, es presenten les noves aportacions metodològiques. En primer lloc, es mostra el potencial de PLS per analitzar dades del DOE, amb dades faltants o sense aquestes. Posteriorment, el potencial dels models causals basats en variables latents se centra a definir l'espai de disseny de la matèria prima que proporciona garantia de qualitat amb un cert nivell de confiança per als atributs crítics de qualitat, juntament amb el desenvolupament d'un nou índex de capacitat multivariant basat en l'espai latent per a classificar i seleccionar proveïdors per a una primera matèria particular utilitzada en un procés de fabricació. La tercera part pretén abordar aplicacions noves mitjançant models causals basats en variables latents utilitzant dades històrques. En primer lloc, es tracta de la seva aplicació a l'àmbit sanitari: la Pandèmia COVID-19. En aquest context, es fa servir l'ús de models basats en variables latents per desenvolupar una alternativa als assaigs clínics controlats amb placebo. Després s'utilitzen models basats en variables latents per optimitzar processos en el marc d'aplicacions industrials. La quarta part presenta una interfície gràfica d'usuari desenvolupada en codi Python que integra els mètodes desenvolupats amb l'objectiu de ser autoexplicativa i fàcil d'usar. Finalment, l'última part discuteix la rellevància d'aquesta dissertació, incloent-hi propostes que mereixen més investigació. / [EN] The present Ph.D. thesis is devoted to studying, developing, and applying data-driven methodologies, based on multivariate statistical models of latent variables, to address the scientific learning paradigm in the Industry 4.0 environment. Particular emphasis is placed on causal latent variable-based models using both data coming from a planned design of experiments and, mainly, data coming from the daily production process, namely happenstance data. The dissertation is structured in five parts. The first part discusses the scientific learning paradigm in the Industry 4.0 environment. The objectives of the thesis are highlighted. In addition to that, a comprehensive description of latent variable-based models is presented, on which the novel methodologies proposed in this thesis are founded. In the second part, the novel methodological contributions are presented. Firstly, the potential of PLS to analyze data from DOE, with or without missing runs is illustrated. Then, the potential of causal latent variable-based models is concentrated on defining the raw material design space providing assurance of quality with a certain confidence level for the critical to quality attributes, jointly with the development of a novel latent space-based multivariate capability index to rank and select suppliers for a particular raw material used in a manufacturing process. The third part aims to address novel applications by means of causal latent variable-based models using happenstance data. First, it concerns a health application: the Pandemic COVID-19. In this context, the use of latent variable-based models is applied to develop an alternative to placebo-controlled clinical trials. Then, latent variable-based models are used to optimize processes within the framework of industrial applications. The fourth part introduces a graphical user interface developed in Python code that integrates the developed methods with the aim of being self-explanatory and user-friendly. Finally, the last part discusses the relevance of this dissertation, including proposals that deserve further research. / Borràs Ferrís, J. (2023). Causal latent space-based models for scientific learning in Industry 4.0 [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/198993

AstraZeneca är ett medicinskt företag lokaliserat i Södertälje som har cirka 900 luftfilterboxar hos sin anläggning i Gärtuna. Dessa filterboxar kan vara opraktiska att hålla koll på eftersom de är utspridda uppe på vindarna, samt att det behövs bra framförhållning vid filterbyten eftersom vissa filter innehåller farliga ämnen. Filterboxar är utrustade med analoga tryckmätare vilket innebär att det kan bli omständigt för personalen att övervaka filtrets tryckfall. Målet med det här examensarbetet är att hitta en digital lösning som underlättar övervakningen av tryckfall över filter i realtid, samt informerar via sms eller mejl när det är dags att planera in filterbyte. Visionen är att det ska bli enklare att få en bild över luftfiltrets underhållsmässiga skick. Genom att jämföra olika produkter och delta i regelbundna möten med IT kunnig personal valdes en lämplig produkt. Projektet har arbetat metodiskt och strukturerat genom att följa projektmodellen projekt case där verktygen GANTT-schema, SWOT-analys, FMEA och maxiriskmetoden har använts. Projektet resulterade i användningen av digitala differentialtryckgivare som mäter tryckfallet över filter. Differentialtryckgivaren samlar data som sedan skickas via radiofrekvens till en Ethernet gateway som är trådad in till AstraZenecas segregerade nätverk så kallat FAB-net. Därefter förs datan från ethernet gatewayen till mjukvara. I mjukvaran kommer AstraZenecas filtergrupp kunna se över tryckfall hos de olika filtrena i filterboxarna. Detta kommer underlätta personalens arbete och eventuellt spara AstraZeneca tid och pengar. / AstraZeneca is a medical company located in Södertälje that has around 900 air filter boxes at its facility in Gärtuna. These filter boxes can be impractical to keep track of as they are scattered up in the attics and good foresight is needed for multiple filter changes as some filters contain harmful substances. Filter boxes are equipped with analog pressure gauges, which means that there is a lot of walking for the staff in order to monitor the filter's pressure drop. The goal of this thesis is to find a digital solution that can facilitate the monitoring of pressure drops across filters in real time and that also informs by text message or email when it is time to schedule a filter change. The vision is that it will be easier to get a digital image of the maintenance condition of the air filter. By comparing different products and participating in regular meetings with AstraZeneca's IT personnel, a suitable product was chosen. The project has worked methodically and structured by following the project model project case where tools such as GANTT chart, SWOT analysis, FMEA and maxirisk method have been used. The project resulted in the use of digital differential pressure sensors that measure the pressure drop across filters. The differential pressure sensor collects data which is then sent by radio frequency to an ethernet gateway which is wired into AstraZeneca's segregated network called FAB-net. The data is then transferred from the ethernet gateway to the software. AstraZeneca's filter group will be able to review the pressure drop of the various filters within the software, which will facilitate their way of working and potentially save AstraZeneca time and money.

Tesis por compendio / [ES] Los sistemas tradicionales de planificación y control de la producción (PPC) se centran en producir lo que demanda el mercado, con la calidad, el calendario y los volúmenes previstos al mínimo coste, ajustándose al mismo tiempo a las disrupciones de la cadena de suministro. La exploración e implementación de nuevos avances tecnológicos en el marco de la industria 4.0 (I4.0), como sistemas ciberfísicos (CPS), fabricación en la nube (CMfg), fabricación aditiva (AM), big data, inteligencia artificial y la Internet de las cosas (IoT), podrían cambiar aspectos organizativos tales como las responsabilidades de PPC. En este contexto, no se identificaron estudios sobre un sistema para la toma de decisiones, arquitecturas y marcos conceptuales para los nuevos sistemas inteligentes de PPC e I4.0. En este contexto de nuevos cambios tecnológicos y organizativos a los que tienen que hacer frente las pequeñas y medianas empresas (PYMEs), surge el problema de diseñar herramientas de PPC que permitan la integración y colaboración de las operaciones de producción. Así, basándose en las nuevas tecnologías de producción digital y en las herramientas organizativas que darán soporte a las fábricas inteligentes conectadas del futuro, se identificó la falta de un sistema integrado de PPC e I4.0. Esta tesis doctoral es un compendio de artículos que abordan una amplia revisión bibliográfica sobre la PPC en un entorno de I4.0. También, se propone un marco conceptual y el diseño de modelos y algoritmos para la toma de decisiones y dar soporte a las funciones de PPC en un contexto digital I4.0 basado en las nuevas tecnologías de producción digital y herramientas organizativas que darán soporte a las fábricas inteligentes colaborativas y conectadas del futuro. Los modelos matemáticos y algoritmos propuestos se centran en resolver el problema del diseño y planificación de una cadena de suministro sostenible y resiliente en la que las decisiones estratégicas y tácticas se toman de forma integrada. Los modelos, algoritmos y método de resolución se han programado en Python. Los modelos han sido validados mediante un software que genera instancias de datos sintéticos y permite evaluar la complejidad computacional de los mismos. El desarrollo de este tipo de modelos y algoritmos supone una contribución al ámbito académico e investigador y, concretamente, en el área de PPC. / [CA] En l'actualitat, els sistemes tradicionals de planificació i control de la producció (PPC) se centren en produir el que demanda el mercat, amb la qualitat, el calendari i els volums previstos al mínim cost, ajustant-se al mateix temps a les pertorbacions. L'exploració i implementació de nous avanços tecnològics, com CPS, fabricació en el núvol (CMfg), fabricació additiva (AM), big data, intelligència artificial i el IoT, podrien canviar aspectes organitzatius, com les responsabilitats de PPC. En aquest context, no es van identificar estudis sobre un sistema per a la presa de decisions, arquitectures i marcs conceptuals per als nous sistemes intelligents de PPC i I4.0. En aquest context de nous canvis tecnològics i organitzatius als quals han de fer front les petites i mitjanes empreses (PIME), sorgeix el problema de dissenyar eines de PPC que permeten la integració i collaboració de les operacions de producció. Així, basant-se en les noves tecnologies de producció digital i en les eines organitzatives que donaran suport a les fàbriques intelligents connectades del futur, es va identificar la falta d'un sistema integrat de la PPC i I4.0. Aquesta investigació és un compendi d'articles que aborden una àmplia revisió bibliogràfica sobre la PPC en un entorn I4.0. També proposa un marc conceptual i el disseny de models i algorismes per a la presa de decisions i per a donar suport a les funcions de PPC en un context digital I4.0 basat en les noves tecnologies de producció digital i eines organitzatives que donaran suport a les fàbriques intelligents col·laboratives i connectades del futur. Els models matemàtics i algorismes proposats se centren en resoldre el problema del disseny d'una cadena de subministrament sostenible i resistent en la qual les decisions estratègiques i tàctiques es prenen de forma integrada. Els models, algorismes i mètode de resolució s'han programat en Python. Els models han sigut validats mitjançant un programari que genera instàncies de dades sintètiques i permet avaluar la complexitat computacional dels models. El desenvolupament d'aquesta mena de models i algorismes suposa una important contribució a l'àmbit acadèmic. / [EN] Currently, traditional production planning and control (PPC) systems focus on producing what the market demands with the expected quality, schedule and volumes at a minimum cost, while adjusting for disruption. The exploration and implementation of new technological advances, such as CPS, cloud manufacturing (CMfg), additive manufacturing (AM), big data, artificial intelligence and the Internet of Things (IoT), could change organisational aspects like PPC responsibilities. In this context, no studies on a system for decision making, architectures and conceptual frameworks for the new intelligent systems of PPC and industry 4.0 (I4.0) have been identified. In this context of new technological and organisational changes that small-and medium-sized enterprises (SMEs) have to face, the problem of designing PPC tools that enable the integration and collaboration of production operations arises. Thus, based on the new digital production technologies and organisational tools that will support the connected smart factories of the future, lack of an integrated PPC and I4.0 system was identified. The present doctoral thesis is a compendium of articles addressing a comprehensive literature review on PPC in an I4.0 environment. It also proposes a conceptual framework and the design of models and algorithms for decision making and to support PPC functions in a digital I4.0 context based on the new digital production technologies and organisational tools that will support the collaborative and connected smart factories of the future. The proposed mathematical models and algorithms focus on solving the problem of designing a sustainable and resilient supply chain where strategic and tactical decisions are made in an integrated way. The models, algorithms and resolution method have been programmed in Python. The models have been validated by means of software that generates synthetic data instances and allows the models' computational complexity to be evaluated. The development of this type of models and algorithms is a significant contribution to the academic field. / I would like to thank the following projects and universities for having financed the publications included in this doctoral thesis: • European Commission Horizon 2020 project entitled "Crop diversification and low-input farming cross Europe: From practitioners' engagement and ecosystems services to increased revenues and value chain organisation' (Diverfarming), grant agreement 728003. • Spanish Ministry of Science, Innovation and Universities project entitled 'Optimization of zero-defect production technologies enabling supply chains 4.0 (CADS4.0)' (RTI2018-101344-B-I00). • European Union H2020 program with grant agreement no. 958205 "Industrial Data Services for Quality Control in Smart Manufacturing (i4Q)". • European Union H2020 Program with grant agreement nº 825631 "Zero- Defect Manufacturing Platform (ZDMP)". / Cañas Sánchez, HE. (2023). Models, Algorithms and Digital Technologies for the Automation and Collaboration of Connected Smart Factories in an Industry 4.0 Environment [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/202312 / Compendio

The perspective of procurement and supply chain management is changing dramatically; traditionally, it was seen as a support function; however, the procurement function is receiving increased attention and investment as an essential contributor to the strategic success and a business enabler. While an end-to-end digital supply chain is an opportunity as it unleashes the next level of strategic growth and involves minimal investment in infrastructure, it is still a challenge to optimize and transform. Furthermore, the recent pandemics and geopolitical disruptions of Covid-19, the Ukraine-Russian war, Brexit and the US-China trade war; have structurally changed the global economy and revealed a new risk assessment that will result in the re-introduction of buffers, boundaries across industries and a partial return to regionalization with sort of de-globalization in which existing just-in-time getting replaced by just-in-case strategy.

Recent breakthroughs within technology and data science have initiated talks of a new emerging industrial revolution, being the fourth of its kind. This revolution, titled as Industry 4.0, implies further digitalization with AI and machine learning helping pave the way for improved robotic interconnection, decentralized decisions and linking the physical world with the virtual world. An important enabler for the transformation is 5G which will allow higher data speed, lower latency of communication, and improved network resilience, compared to its precursor 4G. That being said, a successful 5G rollout and adoption is not an easy task, especially for low-middle income countries. The 5G technology and the innovations it enables, could act as major economical catalysts for these countries and thus it is important to understand the potential barriers they are facing. To help clarify the matter, this study included a conduction of semi-structured interviews with some of the most important actors in the Malaysian 5G ecosystem. The ambition was to uncover the biggest barriers impeding the adoption of 5G technologies, as well as key enabling factors accelerating it. The results showed that low fibre infrastructure development, obscure pricing of 5G spectrum, high trait of complexity among 5G technology and associated innovations, customer unawareness, potential hampering of innovation due to a Single Wholesale Network approach (SWN), and a “Chicken or Egg”-dilemma between infrastructure providers and 5G application providers, represent the main barriers for a successful 5G implementation in Malaysia. At the same time, enabling factors such as a strong governmental backing, increased demand amongst end-users, high competitiveness of the telecommunication industry, and the SWN potentially mitigating the "Chicken or Egg"-dilemma were also identified and presented. An external validity assessment showed that most of the barriers could also be applied to neighbouring countries within the Southeast Asia region, providing practical implications for policy makers and industry actors working with the adoption of 5G technology within low-middle income countries. / De senaste genombrotten inom teknik och datavetenskap har föranlett diskussioner om närmandet av en ny industriell revolution, som blir den fjärde av sitt slag. Denna revolution som har fått tituleringen ”Industry 4.0”, väntas innebära ytterligare framsteg inom digitalisering med hjälp av AI och maskininlärning, vilket banar vägen för förbättrad robotkoppling, decentraliserade beslut och sammanlänkning av den fysiska och virtuella världen. En viktig delkomponent för denna transformation är 5G som väntas möjliggöra högre datahastighet, lägre kommunikationsfördröjning och förbättrad nätverkselasticitet jämfört mot sin föregångare 4G. En framgångsrik utrullning av 5G är dock inte en lätt uppgift, särskilt för låg- och medelinkomstländer. Tekniken bakom 5G och de innovationer den möjliggör, kan agera viktiga ekonomiska katalysatorer för dessa länder och därför blir det viktigt att förstå de potentiella hinder som de står inför. För att bättre förstå problemet genomfördes i den här studien semistrukturerade intervjuer med några av de viktigaste aktörerna i Malaysias 5G-ekosystem. Ambitionen var att avslöja de största hindren som hämmar införandet av 5G-teknik, samt viktiga möjliggörande faktorer som påskyndar denna process. Resultaten visade att låg fiberutveckling, oviss prissättning av 5G-spektrum, hög komplexitet bland 5G-teknik och tillhörande innovationer, kundomedvetenhet, potentiella innovationshämningar till följd av en ”Single Wholesale Network”-strategi (SWN) samt ett "Kyckling eller ägg"-dilemma mellan infrastrukturleverantörer och leverantörer av 5G-applikationer, utgör de främsta barriärerna för en framgångsrik 5G-utrullning i Malaysia. Samtidigt identifierades de viktigaste möjliggörande faktorerna som statligt stöd, ökad efterfrågan bland slutanvändare, den höga konkurrenskraften inom telekommunikationsindustrin samt SWN-strategins potentiellt positiva påverkan på "Kyckling eller ägg"-dilemmat. En extern validitetsbedömning visade att de flesta av barriärerna även kunde tillämpas på närliggande inom Sydostasien, vilket genererade praktiska implikationer för beslutsfattare och branschaktörer som arbetar med införandet av 5G-teknik inom låg-och medelinkomstländer.

The forth industrial revolution has risen the born of new mega trends for the improvement of the time to market and the spare of resources in the development and manufacturing of a new product. Among these trends, the Digital Twin (DT) is the one of major interests for developers and strategy analysts. The perfect transposition of a real entity into a digital environment enables the exploration and testing of the different components within the defined object, taking a further step towards a perfect correct-by-design approach. STMicroelectronics (ST) is exploring the benefits that this technology offers to the developers. The company’s primary focus revolves around the creation of SystemC models for the manufactured components so that a co-simulation between an Hardware (HW)/Software (SW) platform and a kinematic simulator is possible. This innovative approach facilitate the comprehensive validation of the designed Firmware (FW), relying on the intricate interplay with sensory aspects influenced by both device behavior and environmental circumstances. Furthermore, many applications nowadays implement an Artificial Intelligence (AI) algorithm: its performance is strictly dependent on the quality of the signals sensed and on the dataset on which the model is built. The creation of a proper DT allows to implement its development during the design phase, creating not only a valid AI for the real product, but also improving the quality and the performance of the model built. This conclusion is proven through the construction of a simple robotic arm implementing an anomaly detection algorithm based on a Machine Learning (ML) model. / Den fjärde industriella revolutionen har gett upphov till nya megatrender för förbättring av time-to-market och spara resurser vid utveckling och tillverkning av tillverkning av en ny produkt. Bland dessa trender är DT av stort intresse för utvecklare och strategianalytiker. Den perfekta överföringen av en verklig enhet till en digital miljö gör det möjligt att utforska och testa de olika komponenter inom det definierade objektet, vilket tar ytterligare ett steg mot en perfekt korrekt-från-design-metod. ST utforskar fördelarna som denna teknologi erbjuder utvecklare. Företagets huvudsakliga fokus kretsar kring skapandet av SystemC-modeller för tillverkade komponenter så att en samkörning mellan en HW/SW och en kinematisk simulator blir möjlig. Denna innovativa metod underlättar den omfattande valideringen av utformad FW och bygger på den intrikata interaktionen med sensoriska aspekter som påverkas av både enhetens beteende och miljöförhållanden. Dessutom implementerar många applikationer nuförtiden en algoritm för AI: dess prestanda är strikt beroende av kvaliteten på de uppfångade signalerna och den dataset på vilken modellen bygger. Skapandet av en korrekt DT möjliggör genomförandet av detta steg under designfasen, vilket inte bara resulterar i en giltig AI för den verkliga produkten utan också förbättrar kvaliteten och prestandan hos den skapade modellen. Denna slutsats bevisas genom konstruktionen av en enkel robotarm som implementerar en algoritm för avvikelsedetektering baserad på en ML model.