Abstract
Managing supply chain (SC) relationships to deal with challenges posed by contemporary social and business environments is a difficult task that can be facilitated with the use of digital technologies. The growing complexity of supply chains, characterized by over-dependencies on geographically dispersed partners across different regions, increases risks related to managing these relationships and highlights the importance of collaboration and balancing the power dynamics between SC partners. Previous studies have shown that small and medium enterprises (SMEs) can be considered the weakest link in terms of digitization and balance of power. This article aims to analyse how buyer-seller power relations moderate the relationship between the adoption of digital technologies in supply chain management (SCM) processes and innovation performance in the context of SMEs. Data were collected from manufacturing SMEs operating in Portugal. The results support the assumption that the use of digital technologies in processes related to SCM has a positive effect on SMEs innovation performance. The results also suggest that non-mediated power and reward-mediated positively moderate the relationship between the adoption of digital technologies and innovation performance, while the impact of coercive-mediated power was not confirmed. The article contributes to theory and practice by advancing the literature and guiding managers in the challenging task of carrying out digital transformation initiatives, considering their relationship with the power dynamics in the complex context of SMEs.

Abstract
This paper examines the concept of learning factories and their role in addressing contemporary challenges in the production sector. Learning factories integrate learning and production environments, offering hands-on experiences to develop essential competencies for modern manufacturing. Originating from initiatives like the Germany's "Lernfabriken" in the late 1980s and the National Science Foundation's funding in the 1990s, learning factories have gained global prominence. They serve as platforms for research, education, and workforce development, attracting students and workers from diverse sectors. Examples from Europe, the United States, and China illustrate various approaches to leveraging learning factories for industrial advancement and skill development. Overall, learning factories play a vital role in fostering innovation, enhancing competitiveness, and driving economic growth in the manufacturing sector. © 2024 The Authors. Published by Elsevier B.V.

Abstract
This paper emphasizes the need to broaden organizational perspectives through Open X, which promotes sharing and collaboration over selfishness and competition, instead of that industrial intellectual protection through patents can divert resources essential for the growth of organizations. Faced with new realities, organizations need different management approaches with the potential to transform the reindustrialization resulting from deindustrialization into a Neoindustrialization 2.0. It does not mean tearing down or creating new boundaries but an open culture where organizational efforts have social relevance. In the face of economic interests, Open X can make organizational outcomes more plentiful and robust. © 2024 Published by Faculty of Engineering.

Abstract
Industry 4.0 represents a turning point in the thinking of the production model since it is based on digitalized production systems with the aim of improving productivity, product quality, and delivery time to the customer. The digitalization and evolution of information technology allowed the emulation of production system virtual models, namely in the concept of Digital Twin (DT), with the ability to simulate different scenarios providing support for better decision making. This concept not only represents a virtual copy of the physical world that obtains information about the state of the value chain but also illustrates a system capable of changing the development of the production activity according to the fulfillment of the intended business goals. In literature, the concept of the Digital Twin is exhaustively treated as a stand-alone factory (one digital factory represents one physical factory) and underestimates the possibility of a DT oriented to a customized product (a project) that requires decentralized production systems. This paper brings to discussion the relevance of product customized applying DT to smart customization, and the inclusion of decentralized production systems supported by Cloud Manufacturing. © 2024 The Author(s). Published by Elsevier B.V.

Abstract
Industry 4.0 has brought modernization to the production system through the network integration of the constituent entities which, combined with the evolution of information technology, has enabled an increase in productivity, product quality, optimization of production costs, and product customization to customer needs. Despite the complexity of human thought, artificial intelligence tries to replicate it in algorithms, creating models capable of processing databases with a high volume of information, and generating valuable information for decision making. Within this area, there are subfields, such as Machine Learning and Deep Learning, which, through mathematical models, define patterns to predict output data from known input data. In addition to this type of algorithm, there are metaheuristic models capable of optimizing the parameters required in Machine Learning and Deep Learning algorithms. These intelligent systems have applications in various areas such as industry, construction, health, logistics processes, and maintenance management, among others. This paper focuses on Artificial Intelligence models addressing Industry 4.0 approach. © 2024 The Author(s). Published by Elsevier B.V.

Abstract
In a society based on data-driven, data inclusion and data access play a significant role in societal development. A called democratization of data through open access, Open Data, must be nurtured by countries to empower their citizens, entrepreneurs, companies, industries, academics, and organizations, in general. Open Data Scoring System is an evaluation system that ranks countries in 22 categories of openness in data, divided into the 3 pillars of sustainability. In this paper, we will present the importance of Industry 4.0 and its relation to sustainability and the role of Data Science in Industry 4.0 assuming an Open Design approach. Then, an analysis is made considering the Gross Domestic Product (GDP) of the most relevant countries worldwide, the USA and China, concerning the six (6) higher ranked categories of openness data of these countries, supported by the Open Data Scoring System from 2015 to 2020. Our findings reveal that in the USA and China the main categories are seven (7), five (5), and 2 (two) categories of economic, social, and environmental sustainability, respectively. Through a correlations and co-occurrences analysis of the open data scoring worldwide reveals that the most significant categories are four (4) economic, one (1) social, and two (2) environmental. © 2024 The Author(s). Published by Elsevier B.V.