Abstract
The digital transformation in the manufacturing world raised an additional interest in data collection and connectivity, preferably to the one performed in real time. Internet of Things (IoT) and Machine to Machine (M2M) technologies allow the collection of the huge amount of generated data in the shop floor, at the desired rates and without any human intervention. This paper describes the application of IoT technologies to create an automatic data collection solution for an industrial metal stamping unit, supporting its posterior processing, aiming to develop monitoring, prediction and optimization in an industrial intelligent and predictive maintenance system.

Abstract
Digitalization is transforming the worldwide economy. In such digital transformation the human integration in CPS assumes a crucial importance, complemented with some key enabling technologies like IoT, cloud computing, big data and machine learning. In this context, user interfaces are used to collect data and to monitor and control the systems, being required to use design and ergonomics guidelines, as well as user expirience, for their deployment in industrial enviroments. This paper describes the use of friendly and ergonomic user interfaces to support the human integration at operational and strategic level, aligned with the Industry 4.0 context, for an industrial metal stamping unit.

Abstract
In the Industry 4.0 context, humans are taking up new roles in Cyber-Physical Systems (CPS) since it was realized that they constitute the most flexible part of an automated system. With this new reality, humans are becoming integral parts of CPS, entering in the realm of the Human-in-the-Loop of Cyber-Physical Systems (HiLCPS). In this paper, the challenges and technologies for the integration of humans in CPS environments are briefly discussed, and a practical application of the HiLCPS concept is presented. For this purpose, a human operation workbench is empowered with intelligent decision and assistant systems that use, among others, reasoning algorithms, image processing and speech recognition, to improve the productivity and quality of the tasks performed by humans, particularly focusing the assembly of highly customized products, as well as the training of operators to perform complex assembly, repairing or maintenance tasks.

Abstract
In the era of the 4th industrial revolution, manufacturing companies are digitally transforming their businesses, processes and factories to address the current market demands. Among several opportunities, the dynamic monitoring of strategical indicators assume a crucial importance for the rapid awareness of the adaptation and optimization of a manufacturing process. In particular, the analysis of the effect that the variation of business factors will provoke in the associated performance indicators will allow to take strategical decisions to mitigate the occurrence of condition changes, as well as to optimize the system operation to face business opportunities. This paper describes the deployment of a KPI (Key Performance Indicators) monitoring tool in an industrial factory producing micro-waves, allowing the fast production reconfiguration facing the continuously and rapidly changing market conditions.

Abstract
Human integration in cyber-physical systems (CPS) is playing a crucial role in the era of the digital transformation, notably because humans are seen as the most flexible driver in an automated system. Two main reference models for human activities in production systems are usually considered, namely Human-in-the-Loop (HitL) and Human-in-the-Mesh (HitM), which present different requirements and challenges. This paper aims to overview the different activities related to the human integration in CPS, particularly discussing the requirements that can be found in HitL and HitM models for the different phases of the decision-making process, namely detect, determine, develop and describe; and analyzing the technologies and computational tools to support these human activities. The human integration in CPS is illustrated through three examples, where humans playing the operator and manager roles are integrated in the PERFoRM and FAR-EDGE ecosystems, covering different phases of the decision-making process. Copyright

Abstract
Scheduling assumes a crucial importance in manufacturing systems, optimizing the allocation of operations to the right resources at the most appropriate time. Particularly in the Flexible Manufacturing System (FMS) topology, where the combination of possibilities for this association exponential increases, the scheduling task is even more critical. This paper presents a heuristic scheduling method based on genetic algorithm for a robotic-centric FMS. Real experiments show the effectiveness of the proposed algorithm, ensuring a reliable and optimized scheduling process. © 2018 by World Scientific Publishing Co. Pte. Ltd.