An investigation upon Industry 4.0 implementation: the case of small and medium enterprises and Lean organizations

In recent years, industries have undergone several shifts in their operating and management systems. Alongside to the technological innovation, rapid market changes and high competitiveness; growing customer needs are driving industries to focus on producing highly customized products with even less time to market. In this context, Industry 4.0 is a manufacturing paradigm that promises to have a great impact not only on improving productivity but also on developing new products, services and business models. However, the literature review has shown that research on Industry 4.0 implementation is still characterized by some weaknesses and gaps (e.g., topics such as the implementation of Industry 4.0 in SMEs and its integration with Lean Management approach). Motivated by so, this thesis sought to answer four key questions: (RQ1) What are the challenges and opportunities for SMEs in the Industry 4.0 field? (RQ2) What are the resources and capabilities for Industry 4.0 implementation in SMEs? (RQ3) How can these resources and capabilities be acquired and/or developed and (RQ4) How to integrate Industry 4.0 and Lean Management? To deal with the first research question, a semi-systematic literature review in the Industry 4.0 field was conducted. The main goal is to explore the implementation of Industry 4.0 in SMEs in order to identify common challenges and opportunities for SMEs in the Industry 4.0 era. To face with the second and third research questions, a multiple case study research was conducted to pursue two main aims: (1) to identify the resources and capabilities required to implement Industry 4.0 in Portuguese SMEs. Furthermore, based on mainstream theories such as resource-based view (RBV) and dynamic capability theory, it sought empirical evidence on how SMEs use resources and capabilities to gain sustainable competitive advantage; (2) to shed light on how those SMEs acquire and/or develop the Industry 4.0 resources and capabilities. Finally, this thesis employed a semi-systematic literature review methodology to deal with the fourth research question. As such, it explored the synergistic relationship between Industry 4.0 and Lean Management to identify the main trends in this field of research and, ultimately, the best practices. The analysis and discussion of the best practices revealed a set of potential relationships which provided a more clear understanding of the outcomes of an Industry 4.0-LM integration.Nos últimos anos, as indústrias têm passado por várias mudanças tanto nos seus sistemas operacionais, como de gestão. Juntamente com a inovação tecnológica e alta competitividade; as mudanças nas necessidades dos clientes levaram as indústrias a se concentrarem na produção de produtos altamente personalizados e com tempo de lançamento no mercado cade vez menores. Nesse contexto, a Indústria 4.0 é um paradigma de manufatura que promete ter um grande impacto não só na melhoria da produtividade, mas também no desenvolvimento de novos produtos, serviços e modelos de negócios. No entanto, a revisão da literatura mostrou que a investigação sobre a implementação da Indústria 4.0 ainda é caracterizada por algumas lacunas (por exemplo em tópicos como a implementação da Indústria 4.0 em pequenas e médias empresas (PMEs) e sua integração com a filosofia de gestão Lean Management). Diante disso, esta tese procura responder à quatro questões-chave: (RQ1) Quais são os desafios e oportunidades para as PMEs no campo da Indústria 4.0? (RQ2) Quais são os recursos e capacidades necessários para a implementação da Indústria 4.0 nas PMEs? (RQ3) Como esses recursos e capacidades podem ser adquiridos e/ou desenvolvidos e (RQ4) Como integrar os paradigmas de manufatura, Indústria 4.0 e Lean Management? Para responder à primeira questão de investigação, este trabalho empregou uma revisão semi-sistemática da literatura. O objetivo principal foi explorar a implementação da Indústria 4.0 nas PMEs, a fim de identificar quais são os desafios e oportunidades para as PMEs na era da Indústria 4.0. Para fazer face à segunda e terceira questões de investigação, foi realizado um estudo de caso em 5 PMEs localizadas em Portugal a fim de atingir os seguintes objetivos: (1) identificar os recursos e capacidades necessários para implementar a Indústria 4.0 nas PME portuguesas; (2) esclarecer como essas PMEs adquirem e/ou desenvolvem esses recursos e capacidades. Além disso, com base nas teorias resourcebased view (RBV) e dynamic capabilities, buscar evidências empíricas sobre como as PMEs usam recursos e capacidades para obter vantagem competitiva sustentável. Finalmente, para lidar com a quarta questão de investigação, este estudo explorou a relação sinérgica entre a Indústria 4.0 e a filosofia de gestão Lean Management (LM) para identificar as principais tendências neste campo de investigação e promover as melhores práticas. A análise e discussão das melhores práticas revelaram um conjunto de potenciais relações, o que contribuiu para um entendimento mais claro sobre a integração da Indústria 4.0 com LM

Trusted Artificial Intelligence in Manufacturing; Trusted Artificial Intelligence in Manufacturing

The successful deployment of AI solutions in manufacturing environments hinges on their security, safety and reliability which becomes more challenging in settings where multiple AI systems (e.g., industrial robots, robotic cells, Deep Neural Networks (DNNs)) interact as atomic systems and with humans. To guarantee the safe and reliable operation of AI systems in the shopfloor, there is a need to address many challenges in the scope of complex, heterogeneous, dynamic and unpredictable environments. Specifically, data reliability, human machine interaction, security, transparency and explainability challenges need to be addressed at the same time. Recent advances in AI research (e.g., in deep neural networks security and explainable AI (XAI) systems), coupled with novel research outcomes in the formal specification and verification of AI systems provide a sound basis for safe and reliable AI deployments in production lines. Moreover, the legal and regulatory dimension of safe and reliable AI solutions in production lines must be considered as well. To address some of the above listed challenges, fifteen European Organizations collaborate in the scope of the STAR project, a research initiative funded by the European Commission in the scope of its H2020 program (Grant Agreement Number: 956573). STAR researches, develops, and validates novel technologies that enable AI systems to acquire knowledge in order to take timely and safe decisions in dynamic and unpredictable environments. Moreover, the project researches and delivers approaches that enable AI systems to confront sophisticated adversaries and to remain robust against security attacks. This book is co-authored by the STAR consortium members and provides a review of technologies, techniques and systems for trusted, ethical, and secure AI in manufacturing. The different chapters of the book cover systems and technologies for industrial data reliability, responsible and transparent artificial intelligence systems, human centered manufacturing systems such as human-centred digital twins, cyber-defence in AI systems, simulated reality systems, human robot collaboration systems, as well as automated mobile robots for manufacturing environments. A variety of cutting-edge AI technologies are employed by these systems including deep neural networks, reinforcement learning systems, and explainable artificial intelligence systems. Furthermore, relevant standards and applicable regulations are discussed. Beyond reviewing state of the art standards and technologies, the book illustrates how the STAR research goes beyond the state of the art, towards enabling and showcasing human-centred technologies in production lines. Emphasis is put on dynamic human in the loop scenarios, where ethical, transparent, and trusted AI systems co-exist with human workers. The book is made available as an open access publication, which could make it broadly and freely available to the AI and smart manufacturing communities