The food industry has recently faced rapid and constant changes due to the current industrial revolution, Industry 4.0, which has also profoundly altered the dynamics of the industry overall. Due to the emerging digitalisation, manufacturing models are changing through the use of smart technologies, such as robotics, Artificial Intelligence (AI), Internet of Things (IoT), machine learning, etc. They are experiencing a new phase of automation that enables innovative and more efficient processes, products and services. The introduction of these novel business models demands new professional skills requirements in the workforce of the food industry. In this work, we introduce an industry-driven proactive strategy to achieve a successful digital transformation in the food sector. For that purpose, we focus on defining the current and near-future key skills and competencies demanded by each of the professional profiles related to the food industry. To achieve this, we generated an automated database of current and future professions and competencies and skills. This database can be used as a fundamental roadmap guiding the sector through future changes caused by Industry 4.0. The interest shown by the local sectorial cluster and related entities reinforce the idea. This research will be a key tool for both academics and policy-makers to provide well-developed and better-oriented continuous training programs in order to reduce the skill mismatch between the workforce and the jobs.

The growth of the renewable energy industry is happening at a swift pace pushed, by the emergence of Industry 4.0. Smart technologies like artificial intelligence (AI), Big Data, the Internet of Things (IoT), Digital Twin (DT), etc. enable companies within the sector of renewable energies to drastically improve their operations. In this sectoral context, where upgraded sustainability standards also play a vital role, it is necessary to fulfil the human capital requirements of the imminent technological advances. This article aims to determine the current skills of the renewable energy industry workforce and to predict the upcoming skill requirements linked to a digital transition by creating a unified database that contains both types of skills. This will serve as a tool for renewable energy businesses, education centers, and policymakers to plan the training itinerary necessary to close the skills gap, as part of the sectoral strategy to achieve a competent future workforce.

The construction sector has always occupied a strategic place in the European economy. The European construction industry suffered during the 2007–2008 global financial crisis, and today the sector is undergoing a recovery process. Among all the construction subsectors, civil engineering has the highest growth rate. Currently, the sector has to face profound industrial changes emerging with digital transformations (Industry 4.0), sustainability, climate change and energy efficiency. To promote the growth of the civil engineering sector and accelerate the recovery, we need to create a highly qualified and competent workforce that can handle the challenges coming up with the technological progress and global competitiveness. The main condition to achieve this capable workforce is to define the expected evolution of skills requirements. For that purpose, our work focuses on identifying current and near-future key skills required by the civil engineering occupations. To achieve this, we developed an automated sectoral database for the current and near-future skills requirements of the selected professional profiles. It is our belief that this sectoral database is a fundamental framework that will guide the sector through the future changes. We also believe that our research can be used as a key tool for construction companies, policy-makers, academics and training centers to develop well-designed and efficient training programs for upskilling and reskilling the workforce.

This paper is focused on preventive maintenance optimisation in manufacturing environments, with the objective of determining the optimal preventive maintenance frequencies for multi-equipment systems under cost and profit criteria. The initiative considers the interaction of production, work in process material, quality and maintenance aspects. In this work the suitability of discrete event simulation to model or modify complex system models is combined with the aptitude that multi-objective evolutionary algorithms have shown to deal with multi-objective problems to develop a maintenance management and optimisation approach. An application case is performed where the activities applied on a system that produces hubcaps for the car making industry, showing the quantitative benefits of adopting the detailed approach.

The machine tool industry, which is the starting point of all the metal producing activities, is presently undergoing rapid and continuous changes as a result of the fourth industrial revolution Industry 4.0. Manufacturing models are profoundly transforming with emerging digitalization. Smart technologies like artificial intelligence (AI), big data, the Internet of Things (IoT), digital twin, allow the machine tool companies to optimize processes, increase efficiency and reduce waste through a new phase of automation. These technologies, as well, enable the machine tool producers to reach the aim of creating products with improved performance, extended life, high reliability that are eco-efficient. Therefore, Industry 4.0 could be perceived as an invaluable opportunity for the machine tool sector, only if the sector has a competent workforce capable of handling the implementation of new business models and technological developments. The main condition to create this highly qualified workforce is reskilling and upskilling of the current workforce. Once we define the expected evolution of skills requirements, we can clarify the skills mismatch between the workers and job profiles. Only then, we can reduce them by delivering well-developed trainings. For this purpose, this article identifies the current and foreseen skills requirements demanded by the machine tool industry workforce. To this end, we generated an integrated database for the sector with the present and prospective skills needs of the metal processing sector professionals. The presented sectoral database is a fundamental structure that will make the sector acquire targeted industrial reforms. It can also be an essential instrument for machine tool companies, policymakers, academics and education or training centers to build well-designed and effective training programs to enhance the skills of the labor force.