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.

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.

Product lifecycle management (PLM) is a strategic and holistic approach for the management of information, processes, and resources supporting the entire product lifecycle from concept to disposal. Several systems are available to manage data and information during the lifecycle. A PLM system does not have to be considered as a unique solution, but as a set of software supporting different perspectives and activities related to the lifecycle management. A full comprehensive implementation of PLM systems is rare. Since PLM reflects the peculiarities of processes and data structures, implementation differences are evidently observable among companies. The design of methods and tools supporting an assessment of PLM implementation inside a company can enable a correct definition of PLM strategies and goals. Based on these premises, the paper aims to specify a methodology for carrying out a gap analysis of PLM with the scope of discovering existing technological and methodological gaps, and planning actions for improvement. The proposed methodology implements a visual and lean reference model, and an assessment questionnaire for data collection. Lesson learned and feedback from three industrial applications are also described and discussed.