Industry 4.0 and Industry 5.0 have introduced a lot of innovative technologies in industrial plants, transforming them into complex digital systems. On the other hand, the importance of Energy Management Systems in industrial plants is growing for both sustainability and economic reasons, but the opportunity of Industry 4.0/5.0 technologies in enhancing energy management systems is not fully understood. Thus, this paper analyzes how Industry 4.0/5.0 technologies can be applied to meet the requirements of Energy Management Systems, focusing on each aspect such as design, monitoring, control, and budget planning. It identifies additional opportunities that arise with different levels of technological implementation, suggesting organic implementation steps. The final aim is to provide a comprehensive framework for fostering a strategic and conscious implementation approach of these tools in the Energy Management Systems of industrial plants, giving clear and comprehensive suggestions.

Promoting energy efficiency is a key element of the strategic commitment of the European Community. Prominent among the binding measures established by the 2012 Energy Efficiency Directive to further this vision is the requirement for large companies to conduct energy audits every four years. After receiving the second cycle of energy audit reports in December 2019, a new description of the energy situation of Italian companies was made available. This presented the previously inaccessible possibility of comparing the two situations reported in 2015 and 2019 to assess the development of energy efficiency practices in organizations subject to the legislative obligation of energy audits in the country. To this end, in collaboration with the Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), a project was initiated with the aim of developing the tools and methodologies necessary to assess in more detail the evolution that has occurred in the four years since 2015. In this paper, the findings of the analysis conducted on a significant sample of companies in Italy are presented. Through the design of a maturity model to assess the degree of progress achieved in a company’s energy management, the results of the two situations were analyzed. The analysis was deepened by assessing the progress achieved in different aspects of Energy Management: “Strategic Approach”, “Awareness, Competence, and Knowledge”, “Methodological Approach”, “Organizational Structure”, “Energy Performance Management and Information System”, and “Best Practices”. Furthermore, the observed variations were statistically tested using a pairwise t-test to make statistical inferences about the maturity of the total population of Italian enterprises under legislative obligation. The results have shown an increase in overall energy management maturity in each maturity dimension.

Reactive maintenance management in Small and Medium-sized Enterprises (SMEs) is often hindered by technical knowledge fragmentation and reliance on senior experts. Although generative Artificial Intelligence (AI) offers new perspectives for decision support, adopting generalist models introduces operational risks linked to hallucinations and a lack of specific context. This study analyses the effectiveness of a domain-specific Retrieval-Augmented Generation (RAG) framework, designed to encode historical corporate know-how into reliable operating procedures. Through a field validation conducted on a significant sample of real assistance requests, the research compares the proposed system with a generalist Large Language Model. The methodology adopts an evaluation protocol based on the LLM-as-a-judge technique, validated through analyses conducted with a maintenance engineer, to ensure accuracy. The results highlight how targeted context integration reduces factual inaccuracies, ensuring superior technical relevance. Such accuracy emerges as a fundamental enabling factor to speed up fault diagnosis, laying the foundations for intervention time optimization and greater overall maintenance process effectiveness.

Recent advances in artificial intelligence (AI) are accelerating digitalization in manufacturingoperations, yet many solutions remain difficult to scrutinize and operate in human-centeredways, which can hinder adoption. This study presents a systematic review to map primaryresearch on AI in manufacturing operations where human-AI collaboration is explicit orexplainable artificial intelligence (XAI) and human factors are addressed. Searches in Scopus andIEEE Xplore (2020 - 2026) identified 1.240 publications. After a rigorous screening, 177 studieswere included. All studies were coded through an operational lens combining decision domains(scheduling, maintenance, inspection-quality, process control, shop floor) with AI roles andevidence strength. The primary domains are maintenance and inspection-quality, where thedecision support is the most explored role. Furthermore, most of the studies lack a specific XAImodality, while lab and simulation environments are the most included studies. The reviewhighlights that a large share of AI research in operations is still maturing in its human-centeredoperationalization. We provide structured matrices to support evidence-based design andadoption of human-centered AI in manufacturing operations.