## 1 Introduction  
Companies are attempting to stay ahead of their competitors in the current extremely competitive business environment by implementing the latest manufacturing trends. Thus, we are currently living through the Fourth Industrial Revolution, also known as \"Industry 4.0\" [1, 2]. Industry 4.0 is based on manufacturing trends such as the Internet of Things (IoT), Big Data Analytics (BDA), Cyber Physical Systems (CPS), advanced sensors and networking, intelligent algorithms, and Virtual and Augmented Reality to upgrade the manufacturing process and enable the creation of personalized products [3]. Therefore, two cornerstone principles underlying Industry 4.0 are digitalization and servitization, also known as the Internet of Services (IoS) [4]. The innovative Mass Personalization (MP) paradigm, not fully investigated under Industry 4.0, represents a high-tech manufacturing automation strategy, enabling the better integration and smoother cooperation of hardware devices and machinery (IoT), software systems, and humans in the extended manufacturing value chain. Smart Factories, the core of this new paradigm, are characterized by a high degree of digitalization and data centricity. Jointly cooperative working teams of humans and robots autonomously guided by lights out are collaborative, operating in the so-called Lights-out Factories. Extremeautomation until \"everything is connected to everything else\" creates vulnerabilities that have not been investigated extensively so far. The next industrial revolution, Industry 5.0, is expected to cope with these new challenges by bringing the efficiency of manufacturing systems and supply chains beyond limits. Industry 5.0 aims at democratizing the co-production of knowledge by employing cutting-edge digital technologies, e.g., Big Data and IoT, being supported by three main pillars: (a) human centricity; (b) resilience, and (c) sustainability. One of the main focuses relies on the development of Product Platforms aiming at improving the production efficiency and creating high added value for the customer/end-user [5]. During the last decades, manufacturing systems are undergoing a series of changes in order to transform their business models from craftsmanship towards a more customer-oriented manufacturing approach, in an attempt to tackle the volatility of modern market landscape. By extension, this transformation enables the better alignment to customer demands, as a result of the mass customization and personalization, Business-to-Business (B2B), and Business-to-Customer (B2C) paradigms. Consequently, enterprises require competitive business strategies and powerful engineering tools to enable multi-dimensional knowledge exchange throughout the supply chain [6]. Under the framework of Industry 4.0, companies are increasingly shifting their business models from manufacturing and offering only tangible products to the design and development of complete solutions, which include services. By extension, this move enables the development of more sustainable products [7], making it critical for industrialized countries to shift to the Industrial Product Service System (IPSS) paradigm [8]. The importance of servitization is widely recognized in industry as it moves toward mass customization manufacturing systems [9]. The Product Service System (PSS) [10], the dominant hybrid solution of providing services in addition to or instead of product ownership, promises competitiveness and sustainability through the identification of customer value. The academic community has long recognized the importance of evaluation in the early stages of PSS development, but as indicated by [11], PSS evaluation is still an immature field with few concrete results and approaches. Furthermore, the integration of cutting-edge digital technologies in the design and distribution of PSS can be considered as a cornerstone, based on the findings of [PERSON] et al. in [12].