Industry 4.0 is promoting the digitisation of manufacturing sectors towards smart products, machines, processes and factories. The adoption of disruptive technologies associated to this industrial revolution is re-shaping the manufacturing environment, decreasing low-skilled activities and increasing high-skill activities. These technological trends are affecting the job profiles and the skills required by the workforce, which demand proper training programs to address upskilling and reskilling needs. Having this in mind, this work proposes a model that contributes to understand how technological trends may impact the new job profiles and relevant skills, as well as how these skills may be upskilled by the workforce through available training programs according to their gaps and impact. The applicability of the proposed model was illustrated by considering two trends, the connectivity and the value of the data, and a catalogue of compiled new job profiles and training programs.

Cyber-physical systems (CPS) play an important role in the implementation of new Industry 4.0 solutions, acting as the backbone infrastructure to host distributed intelligence capabilities and promote the collective intelligence that emerges from the interactions among individuals. This collective intelligence concept provides an alternative way to design complex systems with several benefits, such as modularity, flexibility, robustness, and reconfigurability to condition changes, but it also presents several challenges to be managed (e.g., non-linearity, self-organization, and myopia). With this in mind, this paper discusses the factors that characterize collective intelligence, particularly that associated with industrial CPS, analyzing the enabling concepts, technologies, and application sectors, and providing an illustrative example of its application in an automotive assembly line. The main contribution of the paper focuses on a comprehensive review and analysis of the main aspects, challenges, and research opportunities to be considered for implementing collective intelligence in industrial CPS. The identified challenges are clustered according to five different categories, namely decentralization, emergency, intelligent machines and products, infrastructures and methods, and human integration and ethics. Although the research indicates some potential benefits of using collective intelligence to achieve the desired levels of autonomy and dynamic adaptation of industrial CPS, such approaches are still in the early stages, with perspectives to increase in the coming years. Based on that, they need to be further developed considering some main aspects, for example, related to balancing the distribution of intelligence by the vertical and horizontal dimensions and controlling the nervousness in self-organized systems.

Programmable Logic Controllers (PLCs) still are the state-of-the-art regarding the industrial automation control, but the Industry 4.0 advent is imposing new requirements, e.g., related to the capability to acquire and process data on realtime at the edge computational layer. On the other hand, the current availability of cheaper and more powerful processors opens new windows to develop low-cost and more advanced industrial controllers aligned with the Industry 4.0 principles. In this context, an important challenge is to improve the current state-of-the-art PLCs by taking into consideration the low-cost but powerful computational boards that will allow to embed IoT technologies and data analytics. This work describes the development of a low-cost but powerful industrial controller based on the use of the single-board computer Raspberry Pi, which allows executing logic control programs codified in IEC 61131–3, IEC 61499, or even in Java or Python, while maintaining the industrial requirements. The proposed platform was experimentally used to control an automation process based on a Fischertechniks platform.

In the context of Industry 4.0 (I4.0), the Asset Administration Shell (AAS) has been gaining significant attention in recent years. The AAS serves as a standardized digital representation of an asset, encapsulating all relevant information about the asset throughout its lifecycle. Since its introduction in 2015, the past decade has seen considerable progress in developing traditional AAS solutions, namely AAS Type 1 and Type 2. As this initial phase reaches maturity, it becomes essential to shift focus toward AAS Type 3 (proactive), a specific category that extends traditional AAS functionalities by incorporating higher levels of autonomy, intelligence, and collaborative capabilities. However, AAS Type 3 is still in its early stages, lacking formal specifications and comprehensive implementation guidelines. In this context, Multi-Agent Systems (MAS) have been investigated as a means to enhance traditional AAS solutions toward the realization of AAS Type 3, particularly by embedding autonomous, intelligent, and collaborative behaviors. Building on this perspective, this paper explores the role of MAS in realizing AAS Type 3 through a comprehensive analysis of existing agent-based AAS approaches in the literature. Furthermore, this paper proposes a reference model based on common patterns found in the literature to support the development of AAS Type 3 solutions, contributing to the discussion on the formalization of specifications and providing greater clarity on this emerging topic. Finally, to better demonstrate key aspects of the model, some illustrative examples are presented to guide its application and facilitate understanding.

Over the past decade, the Asset Administration Shell (AAS) has emerged as a cornerstone of digital transformation in Industry 4.0 (I4.0), providing a standardized approach to managing digital representations of industrial assets. With 2025 marking approximately ten years since its introduction, this article aims to provide a comprehensive analysis and discussion of AAS development over the past decade, potential research opportunities, and the challenges associated with its adoption. To this end, the study combines a literature survey with an examination of specifications from key organizations, such as the Plattform Industrie 4.0 and the Industrial Digital Twin Association (IDTA), which play a central role in the AAS standardization and development. A key insight from this survey is that AAS is progressing toward becoming a game-changer in realizing I4.0. Unlike a decade ago, AAS has now reached a level of maturity that enables its increasing adoption, supported by specifications and standards, dedicated development platforms for its implementation, and several examples in the literature showing a wide range of applications. Additionally, research opportunities for AAS align with emerging industrial trends and contribute to addressing them. However, several challenges must still be addressed to facilitate the widespread adoption of the AAS.