## 1 Introduction  
Industry 4.0 is a topic primarily debated in the literature, where many studies analyse enabling technologies and their applications by focusing on the impacts of Industry 4.0 on the specific process of manufacturing companies instead of considering all the processes in a holistic manner [1, 2]. According to the review performed by [PERSON] et al. [3], the literature has mainly been devoted to the study of smart manufacturing, although attention to the other smart dimensions, such as products and services, supply chain, and smart working, has increased in recent years. Thus, it supports the vision of Industry 4.0 as a fertile ground for exploring the smart manufacturing field, while simultaneously creating opportunities for synergies with other related smart fields by exploiting the capabilities of the most emerging technologies, such as IoT, ML, AI, big data, cloud computing, and additive manufacturing [4, 5, 6]. Several recent works adopted the Industry 4.0 paradigm and technologies in different fields with clear advantages. [PERSON] et al. [7] focused on the maritime industry's digital transformation, where security and safety are paramount in a similar multistakeholder international domain. In [8], the authors modelled, simulated, and analysed a pharmaceutical company's quality control process to achieve more effective and efficient operations. They show that modelling all these steps can lead companies to successfully identify and optimise the critical factors required to improve the whole process. A modelling process is a standard procedure for complex pharmaceutical plant design and management, which require consideration of complex interactions between phases and stakeholders [9; 10]. [PERSON] et al. [11] present practical Industry 4.0-based solutions to fill the gap between operational research and business practices in freight transportation by implementing an information system that supports the efficient delivery of goods within urban areas. In [12], the authors developed an operational framework for predictive maintenance in the service supply chain using Industry 4.0 technologies, such as IoT, ML, and cloud computing. This framework avoids replacing good-conditioned spare parts and extending the equipment life cycles with fewer unnecessary disposals. Even the adoption of the new HW platforms composed of processing cores with heterogeneous architectures is impacted by the finer automatisation of the many design and production steps between the SW code-design, the compiling, and the orchestrated execution on the different processors [13; 14]. In [15], the authors developed a conceptual model for leveraging Industry 4.0 and digitalisation to positively affect product customisation through support for enterprise integration and the value chain. All these examples clearly show the focused effort in integrating the available cutting-edge technologies to make different levels of the engineering and production processes smarter. In this context, reference architectures proposed to digitalise the industrial domain, such as RAMI 4.0, are precious tools for considering the essential dimensions characterising the design and production of goods and services, including the business aspects, the product life cycle, and the engineering process [16]. This harmonises and standardises the procedures and information flows supported by the digital technologies. However, at the moment, the digital tools and tool-chains adopted to manage a similar multidimensional space are not always market-ready, efficient, scalable, and cost-effective. Moreover, the complexity of the life cycle management of digital SoSs (e.g., IoT-based systems) typically associated with multistakeholder value and supply chains substantially increases the difficulty of identifying adequate engineering solutions. Current engineering standards, such as ISO 81346, the waterfall, and the V models, are highly focused on the design and development phases of the engineering process.