Abstract
In the dynamic realm of nowadays manufacturing, integrating digital technologies has become paramount for enhancing operational efficiency and decision-making processes. This article presents a novel system architecture that integrates a Simulation-based Digital Twin (DT) with emerging trends in manufacturing to enhance decision-making, accompanied by a detailed technical approach encompassing protocols and technologies for each component. The DT leverages advanced simulation techniques to model, monitor, and optimize production processes in real time, facilitating both strategic and operational decision-making. Complementing the DT, trending technologies such as artificial intelligence, additive manufacturing, collaborative robots, autonomous vehicles, and connectivity advancements are strategically integrated to enhance operational efficiency and facilitate the adoption of the Manufacturing as a Service (MaaS) paradigm. A case study within a MaaS supplier context, deployed in an industrial laboratory with advanced robotic systems, demonstrates the practical application of optimizing dynamic job-shop configurations using Simulation-based DT, showcasing strategies to improve operational efficiency and resource utilization. The results of the industrial experiment were highly encouraging, underscoring the potential for extension to more intricate industrial systems, with particular emphasis on incorporating sustainability and remanufacturing principles.

Abstract
A new generation of manufacturing systems is emerging through the adoption of new policies to overcome future crises highlighted by constant social, environmental, and economic concerns. The rise of so-called smart manufacturing is noticeable. However, new risks to humankind are being introduced, and, more than ever, science and technology are required to guarantee the future sustainability and resilience of our manufacturing systems. This research presents a Digital Twin approach resorting to simulation models with embedded intelligence to transform efficient manufacturing systems and react to complex and unpredictable circumstances. The methodology covers production scheduling incorporating flexible robots, internal logistics supervision contemplating planning and control of mobile robots, and capacity management. The method demonstrates the potential of integrating Additive Manufacturing technologies to quickly react to production needs. The developed strategy was enforced and assessed in an industrial experiment, exhibiting its robustness and promising application. The attained results were very encouraging, highlighting its potential extension to more complex industrial systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Abstract

Abstract
Zero Defects is one of the ultimate targets for manufacturing quality control and assurance. Such systems are becoming common in advanced manufacturing industries but are at an initial stage in more traditional industrial sectors, such as wood panels, laminates production, pulp and paper processing and composite panels production. This paper proposes the PREFAB framework, applied to the wood based panels industry, to minimize rejected products using AI, machine learning and IoT devices. The framework was built through action research with a Portuguese wood-based panel manufacturing. This framework delivered an innovative decision support system that provides relevant and timely recommendations for shopfloor decision making and to support process/product engineering. Copyright (C) 2021 The Authors.

Abstract
The Portuguese footwear industry registered a strong performance over the last few years and the openness to adopt new technologies was a key factor. Among these, the implementation of innovative logistic systems for the transport and assignment of work-in-process in manufacturing processes was a key technology. By means of case research it was analyzed an internal logistic system, deployed in a Portuguese large footwear producer, which reveals some weaknesses at the physical and cyber levels. A lack of managing applications was detected at the factory and enterprise levels, which contribute to lower levels of visibility of production and productivity. This article presents the development of a Cyber-Physical Production System, in the context of the Horizon 2020 research and innovation program BEinCPPS, that comprises four application experiments. These experiments were successful and the results demonstrated a relevant increase of production efficiency and decrease of maintenance costs.