It is known that digitalization will be the greatest challenge for companies and an essential condition for their competitiveness in the coming years. It is also known that, although the current COVID-19 pandemic has accelerated the need for digitalization in different business areas with its greatest impact being noticed at the level of e-commerce platforms, the lack of a digital vision for the different areas may leave companies without the skills to act competitively in the global market. In line with this, the authors adopted a Design Science methodology. A solution is proposed for obtaining a picture of the Digital Maturity level of the manufacturing industry in the Tâmega e Sousa region - Digital Industry Survey. Therefore, it is expected to create knowledge about the reality of this industry, supporting companies in the challenges posed by the paradigm of Industry 4.0.

This thesis explores the challenges presented by an ageing workforce in modern production systems, focusing on the evolving context from Industry 4.0 to the human-centred paradigm of Industry 5.0. The study investigates the physical, cognitive, and well-being conditions of operators through four key perspectives: demographic ageing, technological developments, individual operator characteristics, and workplace ergonomic considerations. Using a mixed-methods design, including qualitative and quantitative methodologies, systematic literature reviews, and case studies in two Portuguese metal polishing industries, the research collects sociodemographic, anthropometric, and musculoskeletal symptom data via the Nordic Musculoskeletal Questionnaire (NMQ), and assesses ergonomic risks through sensorless, AI-driven Rapid Entire Body Assessment (REBA) applications. The research reveals a high prevalence of work-related musculoskeletal disorders (WRMSDs), particularly affecting the spine and upper limbs, with significant variation across gender and age groups. Particularly, female operators and ageing workers exhibit an increased risk. Significant correlations were found between age, company seniority, health perception, and WRMSD symptoms, with perceived health declining and symptoms increasing with age and seniority. The methodologies applied were validated in the second company, an independent industrial setting, confirming the reliability, reproducibility, and transferability of findings across different polishing companies. In addition, the study introduces AgErgoVSM, an integrative Lean tool embedding ergonomic principles for distinct age clusters and applies it systematically to map and improve current work practices. The outcomes of this study underscore the need to integrate digital and supportive technologies, such as AI-based ergonomic analysis, to sustainably adapt workplaces to diverse and ageing workforces.

Industry 4.0 promises to revolutionize industrial production with increased operational efficiency, the development of new business models, services and products. It enables real-time production planning and dynamic optimization in contrast to conventional forecast. The tools and technological advances developed by Industry 4.0 will have a significant role to improving the quality of life in society in order to enable a happier, motivated, and satisfied and with more time for leisure. As a consequence, it will increase productivity and the mankind will choose the direction and the kind of society we want to create in the future to promote equal wealth distribution. As consequence of Industry 4.0 emerges Society 5.0, beginning in Japan, due to the concern of the ageing population. Society 5.0 focuses on the use of tools and technologies developed by Industry 4.0 to benefit the humankind. Intelligent systems, developed by Industry 4.0, could be seen by society as a beneficial rather than as adversaries. Future society could benefit from advanced technology in solving problems and economically. Society 5.0 has a special focus to position the human being at the centre of innovation, technological transformation and industrial automation, stimulated by Industry 4.0. This new Society 5.0 paradigm will play a predominant role in creating a happier, satisfied, fulfilled and consequently more productive society. Society 5.0, also called the super intelligent society, intends to use advanced technology of Industry 4.0 for enjoyment of humankind, in order to promote an interconnection between people and systems in cyberspace with optimization of results by artificial intelligence.

Conveyor belts (CBs) are widely used for the continuous transport of bulk materials. CBs must be extremely reliable due to the cost associated with their failure in continuous production systems. Thus, it is highly relevant in terms of maintenance and planning to find solutions to reduce the existing stoppages from these assets. In this sense, it is essential to monitor and collect real-time data from this piece of equipment. This work presents a case study, where a model that combines the Lean Philosophy, Total Productive Maintenance (TPM), and the enabling technologies of Industry 4.0 is applied to a CB. The proposed model monitors the CB and provides data on its operation, which, using the calculation of indicators, allows a more accurate and thorough view and evaluation, contributing to improving and supporting decision making by those responsible for maintenance. The data collected by the sensor help those responsible for maintenance and production, in the readjustment of more accurate and optimized planning, programming, and execution, supporting decision making in these areas. During the field test of a two-hour monitoring period (10 a.m. to 12 p.m.), the model identified six stoppages, resulting in approximately 88.6% of operational time for the conveyor. The field test showed that this model can result in more accurate maintenance decision making than conventional approaches. This research also contributes to the advancement of electronics and industrial automation sectors by empowering companies to transform maintenance methodologies. The potential of this approach and its implications for enhanced productivity and overall performance are therefore highlighted.

In order to create high-quality products, quality engineering must be integrated across the entire product development process. To accomplish the ultimate goal, innovative approaches are required, and a Quality Management System-QMS is imperative to standardize all processes. All business areas depend on people and processes, but quality is especially dependent on them. A QMS can benefit from the application of Quality 4.0—Q4.0 and Cognitive Engineering—CE aspects to reduce the workload and cognitive capacity required from QMS specialists, using these technologies to tackle long-standing quality concerns and to re-optimize to deliver creative solutions. The decision to implement a QMS based on Q4.0 technologies is difficult to take due to the challenge that is to automatize dispersed activities. The purpose of this paper is to develop a framework that aids in the application of a Q4.0 QMS. The relationship between quality management practices and Industry 4.0 technologies that improve quality are deeply studied and connected with CE practices to develop an advanced framework, that makes it easier to overview all the dispersed activities within the manufacturing environment gathered as one, and simplify the application of new technologies to the QMS activities. The proposed framework was developed as result of this study.

This paper addresses the design of a system to facilitate individual environmental risk assessment and long-term risk management in the Industry 4.0 work context. The solution is based on IoT to provide data related to workers’ exposure to hazardous agents (dust, noise, ultraviolet radiation, poor lighting, and inappropriate temperature and humidity) through a simple interface for employees and employers. The system includes a monitoring device and a server and performs employee registration, receives secure messages from the monitoring devices, shows information about the workers’ exposure, and triggers alarms when the measures reach or exceed the limits established by applicable legislation and/or standards. The system was tested in a controlled environment, and the results are presented in this work. Our solution is still under development, and in future stages, it will include a smartphone app for employees to check their exposures and the use of artificial intelligence on the server, which is expected to enable long-term planning by companies based on the analysis of employees’ health history and their exposures to hazardous agents.

Industry 4.0 promises to make manufacturing processes more efficient using modern technologies like cyber-physical systems, internet of things, cloud computing and big data analytics. Lean Management (LM) is one of the most widely applied business strategies in recent decades. Thus, implementing Industry 4.0 mostly means integrating technologies in companies that already operate according to LM. However, due to the novelty of the topic, research on how LM and Industry 4.0 can be integrated is still under development. This paper explores the synergic relationship between these two domains by identifying six examples of real cases that address LM-Industry 4.0 integration in the extant literature. The goal is to make explicit the best practices that are being implemented by six distinct industrial sectors such as automotive, paper, furniture, healthcare, apparel, and machine manufacturing.

In recent decades, concern with workers’ health has become a priority in several countries, but statistics still show that it is urgent to perform more actions to prevent accidents and illnesses related to work. Industry 4.0 is a new production paradigm that has brought significant advances in the relationship between man and machine, driving a series of advances in the production process and new challenges in occupational safety and health (OSH). This paper addresses occupational risks, diseases, opportunities, and challenges in Industry 4.0. It also covers Internet-of-Things-related technologies that, by the real-time measurement and analysis of occupational conditions, can be used to create smart solutions to contribute to reducing the number of workplace accidents and for the promotion of healthier and safer workplaces. Proposals involving smart personal protective equipment (smart PPE) and monitoring systems are analyzed, and aspects regarding the use of artificial intelligence and the data privacy concerns are also discussed.

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.

In recent years, radio-frequency identification (RFID) has aroused significant interest from industry and academia. This demand comes from the technology’s evolution, marked by a reduction in size, cost, and enhanced efficiency, making it increasingly accessible for diverse applications. This manuscript presents a case study of the implementation of an RFID traceability system in the packaging section of an industrial company that produces test equipment for the automotive wiring industries. The study presents the proposal and execution of a prototype asset-tracking system utilising RFID technology, designed to be adaptable and beneficial for various industrial settings. The experiments were carried out within the company’s shop-floor environment, alongside the existing barcode system, with the primary objective of evaluating and comparing the proposed solution. The test results demonstrate a significant enhancement in production efficiency, with substantial optimization achieved. The time required for asset identification and tracking was significantly reduced, resulting in an average time of approximately 43.62 s and an approximate 3.627% improvement in the time required to read the test sample of assets when compared to the barcode system. This successful implementation highlights the potential of RFID technology in improving operations, reducing working time, and enhancing traceability within industrial production processes.