Abstract
The automotive industry is one of the most demanding sectors of the global market. The response capacity and flexibility of companies represent a key factor for their success. Applying Six Sigma, it was carried out an improvement project aiming at reducing the quantity of scrap on the most critical sector of a automotive components’ manufacturer achieving a better comprehension of the flows, process characteristics and different variables associated to the scrap generation, identifying the equipment responsible for that scrap and its type. Brain- storming sessions were performed, as well as the application of 5 Why’s and 5W2H techniques in order to fulfill the Ishikawa diagrams aiming at understanding possible root-causes for the scrap generation. A definition of the improvement actions has been developed. A reduction of 15% was achieve just in the machine identified as the main generator of scrap in these processes. © 2020, Springer Nature Switzerland AG.

Abstract

Abstract
The Industry 4.0 (i4.0) paradigm was conceived bearing smart machines enabling capabilities, mostly through real-time communication both between smart equipment on a shop floor and decision-aiding software at the business level. This interoperability is achieved mostly through a reference architecture specifically designed for i4.0, which is aimed at devising the information architecture with real-time capabilities. From such architectures, the Reference Architectural Model for Industrie 4.0 (RAMI 4.0) is considered the preferred approach for implementation purposes, especially within Small and Medium Enterprises (SMEs). Nevertheless, the implementation of RAMI 4.0 is surrounded with great challenges when considering the current industrial landscape, which requires retrofitting of existing equipment and the various communication needs. Through three different case studies conducted within footwear and cork industries, this research proposes a RAMI 4.0 SME implementation methodology that considers the initial stages of equipment preparation to enable smart communications and capabilities. The result is a methodological route aimed for SMEs’ implementation of smart machines, based on RAMI 4.0, which considers both the technological aspects as well as the business requirements. © 2020, Springer Nature Switzerland AG.

Abstract
Sensors are susceptible to failure when exposed to extreme conditions over long periods of time. Besides they can be affected by noise or electrical interference. Models (Machine Learning or others) obtained from these faulty and noisy sensors may be less reliable. In this paper, we propose a data augmentation approach for making neural networks more robust to missing and faulty sensor data. This approach is shown to be effective in a real life industrial application that uses data of various sensors to predict the wear of an automotive fuel-system component. Empirical results show that the proposed approach leads to more robust neural network in this particular application than existing methods.

Abstract
New opportunities for lifelong learning, alternative curricula in pre-university education and fairly "open" policies on access to Higher Education (HE) have boosted, in recent decades, the problem of the lack of homogenization of knowledge and skills of "freshmen" students in Higher Education Institutions (HEI). This problem becomes overwhelming when it comes to "constructive" basic curricular units, such as Mathematics or Statistics, in non-mathematical degrees, in areas as Administration, Accounting or Management. This is a daily "struggle" faced by teachers of these curricular units as they try to talk about more advanced subjects to a very heterogeneous audience, with significant differences in Math background, promoting the participation of all students and avoiding the early drop out of some. In this sense, other didactic strategies, which include a set of activities that combine higher order thinking skills with math subjects and technology, for students of HE, appear as remedial but important, proactive and innovative measures in order to face and try to level up Math competences without risking the "repetition process", that unfortunately promotes other kind dropout behaviors. In this paper some of these strategies, developed in the Polytechnic of Porto (P.PORTO) and based on the creation and usefulness of the interactive teaching and learning materials, will be presented. The actual need for innovating in the teaching-learning process was felt and the search for a good support software, that enables to develop all the materials and implement real interactions, culminated with the choice of iSpring Suite 9. This software is a powerful eLearning toolkit for PowerPoint that allows to develop quality courses, video lectures, and assessments that will work on any desktop, laptop and mobile platform. Therefore, the use of the iSpring Suite 9 will be described, with a special focus on core objective when teaching statistics to students from the Management and Business degree in a HEI and facing the abovementioned issues - to improve students' basic statistics skills and enhance their motivation in learning Statistics.

Abstract
The European Union has been making efforts to increase energy efficiency within its member states, in line with most of the industrialized countries. In these efforts, the energy consumed by public lighting networks is a key target as it represents approximately 50% of the electricity consumption of European cities. In this paper we propose an approach for the autonomous management of public lighting networks in which each luminary is managed individually and that takes into account both their individual characteristics as well as ambient data. The approach is compared against a traditional management scheme, leading to a reduction in energy consumption of 28%. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020.