The digital twin is currently recognized as a key technology allowing the digital representation of a real-world system. In smart manufacturing, the digital twin enables the management and analysis of physical and digital processes, products, and people in order to foster the sustainability of their lifecycles. Although past research addressed this topic, fragmented studies, a lack of a holistic view, and a lack of in-depth knowledge about digital twin concepts and structures are still evident in the domain of the shop floor digital twin. Manufacturing companies need an integrated reference framework that fits the main components of both physical and digital space. On the basis of a systematic literature review, this research aims to investigate the characteristics of the digital twin for shop floor purposes in the context of smart manufacturing. The “hexadimensional shop floor digital twin” (HexaSFDT) is proposed as a comprehensive framework that integrates all the main components and describes their relationships. In this way, manufacturing organizations can rely on an inclusive framework for supporting their journey in understanding the shop floor digital twin from a methodological and technological viewpoint. Furthermore, the research strengthens the reference literature by collecting and integrating relevant contributions in a unique framework.

In industry 4.0 and digital transformation scenarios, manufacturing companies face the challenges of the exponential growth in the volume of data. A considerable part of the data that enterprises generate in large quantities, at a high speed, and in different forms can be defined as dark data. Companies are not able to extract their potential value for data management, storage, and maintenance issues, and, due to their unstructured form, they become unknown together with their informative value. Some researchers and professionals have addressed the issue of dark data but none of them have focused on the specificity of the manufacturing industry or on the data generated in it. Based on a lack in the literature, in this article, we explore the dark data through a systematic literature review and three focus groups with manufacturing companies. This article fills the gap and provides a valuable support for manufacturing companies to be aware of the presence of dark data in their scenarios, to identify them, and to stimulate initiatives for dark data exploitation. Furthermore, the study provides for the academic audience a reference paper for addressing future exploration in the field of data management in the companies for improving their innovative and operative capabilities.

In the current era of digital transformation, among the plethora of technologies, blockchain (BC) technology has attracted attention, carrying the weight of enormous expectations in terms of its applicability and benefits. BC technology promises immutability, reliability, transparency, and security of transactions, using decentralized models to scale up existing Internet of Things (IoT) solutions while guaranteeing privacy. In the energy industry, BC technology is mainly used to secure distributed power grids, which have proven to be easily hackable by malicious users. Recognizing the need for a preliminary analysis of the literature investigating the role of BC technology for sustainability and secure data management in the energy industry, this study conducts a bibliometric analysis, identifying the implications and research directions in the field. Specifically, a performance analysis and scientific mapping are performed on 943 documents using the Scopus database and the VOSviewer software version 1.6.20. The result is the identification of seven thematic clusters and the most relevant implications as well as future research actions at the strategic, technical, regulatory, and social levels. This study extends the literature by suggesting potential sustainability opportunities regarding BC technology adoption in the energy industry; it also supports managers in identifying strategies to strengthen business sustainability by leveraging the development of new knowledge for secure asset management.

Innovations in product development and process technologies represent a promising strategy to face the increasing competitiveness of modern markets in the global economy. Also, customer requirements become more and more specific and the complexity of products is still increasing. Industries need to adopt effective solutions during the product development process and to support, for sustainable purposes, all the phases of the product lifecycle. Advanced model-based solutions emerge for digitally supporting these industrial needs. In this context, a Model-Based Enterprise (MBE) represents an organization that adopts modeling technologies, such as Model-Based Definition (MBD) solutions, to integrate and manage both technical and business processes related to product design, production, support, and retirement. Past research discusses the model-based approaches focusing on technical product development, mainly referring to the design and the manufacturing phases. A wide perspective from the other phases of lifecycle seems to lack in the literature. By carrying out a systematic literature review, this research aims to investigate the relationships between the MBE approaches and the product lifecycle phases and to identify potential benefits and challenges. It enhances the academic knowledge domain by also shedding light on potential future research directions.