The fourth industrial revolution is characterized by the introduction of the Internet of things (IoT) and Internet of Services (IoS) concepts into manufacturing, which enables smart factories with vertically and horizontally integrated production systems. The main driver is technology, as Industry 4.0 is a collective term for technologies and concepts of value chain organization. Digital manufacturing platforms play an increasing role in dealing with competitive pressures and incorporating new technologies, applications, and services. Motivated by the difficulties to understand and adopt Industry 4.0 and the momentum that the topic has currently, this paper reviews the concepts and approaches related to digital manufacturing platforms from different perspectives: IoT platforms, digital manufacturing platforms, digital platforms as ecosystems, digital platforms from research and development perspective, and digital platform from industrial equipment suppliers.

Smart Product–Service Systems (S-PSS) represent an innovative business model that integrates intelligent products with advanced digital capabilities and corresponding e-services. The user experience (UX) within an S-PSS is heavily influenced by the customization of services and customer empowerment. However, conventional UX analysis primarily focuses on the design stage and may not adequately respond to the evolving user needs during the usage stage and how to exploit the data surrounding the use of S-PSS. To overcome these limitations, this article introduces a practical framework for developing Adaptive User Interfaces within S-PSS. This framework integrates ontologies and Context-aware recommendation systems, with user interactions serving as the primary data source, facilitating the development of adaptive user interfaces. One of the main contributions of this work lies on the integration of various components to achieve the creation of Adaptive User Interfaces for digital services. A case study of a smart device app is presented, to demonstrate the practical implementation of the framework, with a hands-on development approach, considering technological aspects and utilizing appropriate tools. The results of the evaluation of the recommendation engine show that using a context-aware approach improves the precision of recommendations. Furthermore, pragmatic aspects of UX, such as usefulness and system efficiency, are evaluated with participants with an overall positive impact on the use of the smart device.

The present work defines an interoperable and scalable architecture that will allow the servitization of the machine tool sector. ICT and the Internet of Things make possible to interconnect different devices and controllers, offering value-added services. One of the problems to be solved is the interoperability between the different IoT devices. Several platforms enable interoperability between devices. Our work uses Arrowhead with that purpose. In addition, with the aim of offering solutions in an agile and flexible way, we have chosen to use containers (Docker technology). Thus, the interoperability platform is managed within these containers making configuration and operation more agile and flexible. A use case is presented where a Danobat machine tool can offer value-added maintenance and efficiency services. For this purpose, data is acquired from the machine itself but data is also acquired from third-party devices such as ULMA's device. The interconnection of all this data is done in a transparent way for the user and through the interoperable Arrowhead platform. All this work has been carried out within the framework of the Productive 4.0 project. Keywords: Interoperability, Industry 4.0, Containers, Productive 4.0, Arrowhead, Servitization, IoT

Virtual commissioning has acquired a major interest with the introduction of Industry 4.0. It is demonstrated that virtual commissioning can significantly reduce the commissioning time, error rate and costs. However, industry is still experiencing difficulties with the integration of these new technologies. This paper is one of the first empirical surveys conducted in the industry that aims at understanding the challenges and current practices with respect to virtual commissioning, with special focus on the machine tool manufacturing sector. The survey contextualizes the practice of virtual commissioning and the digital twin in industry, and benchmarks the results with academia, in which main gaps are identified. Keywords: empirical survey, virtual commissioning, digital twin, machine tool manufacturing, testing