An ever-growing number of companies are moving toward the Industry 4.0 paradigm, adopting a range of advanced technologies (e.g., smart sensors, big data analytics, and cloud computing) and networking their manufacturing systems. This improves the efficiency and effectiveness of operations but also introduces new cybersecurity challenges. In this article, the impact assessment methodology is applied in the context of manufacturing systems 4.0 (also known as smart manufacturing systems, cyber manufacturing systems, or digital manufacturing systems), thus identifying the critical assets to be protected against cyber-attacks and assessing the business impacts in the case of subtractive and additive technologies. The research design of the single case study with multiple units of analysis is applied. In particular, a large company, a leader in the manufacturing of aeronautical components, is considered a representative case study, and its two main types of manufacturing cells that is, those based on networked computer numerical control machines and 3-D printers, are taken as applicative cases for the methodology. The application of the impact assessment methodology in the manufacturing context 4.0 of aeronautical components represents a useful guide for researchers in the field of cybersecurity and for companies intending to implement it in their smart manufacturing environments. In particular, based on this study, companies can define the critical manufacturing data to protect against cyber-attacks, isolate the business impacts in case of cybersecurity breaches, correlate the identified business impacts with the specific data category, and assess the level of business impacts.

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.

The construction sector is in continuous evolution due to the digitalisation and integration into daily activities of the building information modelling approach and methods that impact on the overall life cycle. This study investigates the topic of BIM/GIS integration with the adoption of ontologies and metamodels, providing a critical analysis of the existing literature. Ontologies and metamodels share several similarities and could be combined for potential solutions to address BIM/GIS integration for complex tasks, such as asset management, where heterogeneous sources of data are involved. The research adopts a systematic literature review (SLR), providing a formal approach to retrieve scientific papers from dedicated online databases. The results found are then analysed, in order to describe the state of the art and suggest future research paths, which is useful for both researchers and practitioners. From the SLR, it emerged that several studies address ontologies as a promising way to overcome the semantic barriers of the BIM/GIS integration. On the other hand, metamodels (and MDE and MDA approaches, in general) are rarely found in relation to the integration topic. Moreover, the joint application of ontologies and metamodels for BIM/GIS applications is an unexplored field. The novelty of this work is the proposal of the joint application of ontologies and metamodels to perform BIM/GIS integration, for the development of software and systems for asset management.

Product lifecycle management (PLM) has become more important in companies providing technologies and methodologies to manage data, information, and knowledge along the whole product lifecycle. In recent years, several authors have argued about PLM using a managerial or a technological view. The paper analyses these studies and integrates different author's points of view using focus groups, blogs, and face-to-face meetings in a university community of practice. Three sets of features (i.e., managerial, technological, and collaborative ones) have been used to review the existing definitions shared between academic and industrial ones and to propose an extended PLM definition describing its key concepts. The paper is a useful reference for managers and academics who want to have a clear and critical understanding of PLM using a unique source to collect lines of evidence on several PLM definitions, features, and concepts.

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.