Abstract
The integration of IoT infrastructures across production systems, together with the extensive digitalisation of industrial processes, are drastically impacting manufacturing value chains and the business models built on the top of them. By exploiting these capabilities companies are evolving the nature of their businesses shifting value proposition towards models relying on product servitization and share, instead of ownership. In this paper, we describe the semantic data-model developed to support a digital platform fostering the reintroduction in the loop and optimization of unused industrial capacity. Such data-model aims to establish the main propositions of the semantic representation that constitutes the essential nature of the ecosystem to depict their interactions, the flow of resources and exchange of production services. The inference reasoning on the semantic representation of the ecosystem allows to make emerge nontrivial and previously unknown opportunities. This will apply not only to the matching of demand and supply of manufacturing services, but to possible and unpredictable relations. For instance, a particular kind of waste being produced at an ecosystem node can be linked to the requirements for an input material needed in a new product being developed on the platform, or new technologies can be suggested to enhance processes under improvement. The overall architecture and individual ontologies are presented and their usefulness is motivated via the application to use cases.

Abstract
The highly disruptive transformation that digital platforms are imposing on entire sectors of the economy, along with the broad digitalization of industrial business processes, is having an impact on supply chains around the world. To take advantage of this new aggregated market paradigm new business models with a heavy focus on servitization are changing the value proposition of businesses. In this paper, we describe a reference architectural framework designed to support a digital platform fostering the optimization of supply chains by the pairing of unused industrial capacity with production demand. This framework aims at harmonizing stakeholder requirements with specifications of different levels in order to set up a coherent reference blueprint that serves as a starting point for development activities. A four-layer approach is used to articulate between technical components, with the data and tools layers, and the ecosystem, with the business and interfaces layers. The overall architecture and component description is presented as extensions of the initial set of affordances.

Abstract
Digital platforms have, in the past decades, undergone a revolution, evolving from its technical roots so much that nowadays value is mostly generated, not by the technologies that power platforms, but by the ecosystem of applications, developers and users it is able to generate and support. In this paper, we seek to understand the importance industrial platform owners place on the community building and platform growth components of the platform development process by reviewing 50 Horizon 2020 financed projects that stand on the development of platforms. This evidence is leveraged for the case of a validation strategy definition for a platform ecosystem aiming at sharing production capacity. Key findings point to platform developing practices focused on the development of technical components to the detriment of the ecosystem generation element. We also shed light on how different business models and funding schemes impacted the steering of these platforms.

Abstract
The widespread adoption of digital technology tied with the 4th industrial revolution means the complete reinvention of how business is done. Digital Twin (DT) technologies are now a key technology trend that is already being developed and commercialized to optimize numerous manufacturing processes. In this paper, and from an Information Systems (IS) discipline viewpoint, we take stock of the different technological visions of the DT in manufacturing. We leverage this summary as a stepping stone for discussing the DT's sociotechnical design implications by pointing how this approach is essential for the design of DT software that is specific to its environment and users and co-evolves with it. Furthermore, we present our vision for a DT-based Digital Platform that can support product design and life-cycle management while generating value through an ecosystem of twin-driven product-service systems. Lastly, we show how the Transformer 4.0 project will demonstrate the main principle of our vision by placing the DT of the power transformer with the dual role of virtual counterpart of the physical product and as the architectural framework for (i) managing and processing the historical data collected from the multiple working instances of DTs, and (ii) managing and integrating design information (models, specifications, design data, among others). Copyright (C) 2021 The Authors.

Abstract
The impact of digital technologies in manufacturing organizations has been felt for decades. Servitization processes themselves have developed from the traditional Product-Service Systems (PSS) toward new business models infused with digital technologies, paving the way to smart PSS. There is, however, a lack of understanding of how digital platforms (DP) can be leveraged for the offering of smart service offerings. In this paper, we highlight how the emergence of DP traces the evolution of PSS and highlight how a platform-based modular architecture can serve as the reference infrastructure for organizations to deliver smart and highly customized products and services. The architecture of the Transformer 4.0 platform is used to demonstrate how DP can serve as orchestrators for an ecosystem of digital twin-driven smart PSS.

Abstract
Data generated throughout the product development lifecycle is often unused to its full potential, particularly for improving the engineering design process. Although Knowledge-Based Engineering (KBE) approaches are not new, the Digital Twin (DT) concept is giving new momentum to it, fostering the availability of lifecycle data with the potential to be transformed into new design knowledge. This approach creates an opportunity to research howdigital infrastructures and new knowledge-based processes can be articulated to implement more effective KBE approaches. This paper describes how combining a DT-based Digital Platform (DP) with new engineering design processes can improve Knowledge Management (KM) in product design. A case study of a company in the energy sector highlights the challenges and benefits of this approach.