The emergence of IoT has propelled the traditionally known Product-Service System (PSS) to be characterized by smarter technologies, enabling them to collect and process data from the operational stage and facilitate communication between the customer and the provider. Commonly referred to as Smart Product-Service Systems (Smart PSS), these systems promise to create value at a personal level by collecting and effectively utilizing the operational data. However, one of the fundamental challenges is the lack of awareness as to what kind of data can be collected from the operational stage and what can be achieved from this data. This paper systematically reviews scientific literature to underline the kind of data being collected from the operational stage, the purposes being achieved from that data, and how they lead to value creation. The systematic review of 60 representative studies enabled the definition of the operational scenario that comprises 4 dimensions of data and 10 classes of data within these dimensions to generically identify what kind of data is being collected. The intend presented by various authors led to the generalization of 5 themes that target different purposes of collecting data. Further, the papers were classified with regards to functional or non-functional requirements to see how data in different approaches are leveraged for value creation. Finally, the discussion highlights the current gaps in the literature and raises several opportunities for future contributions.

With the advances of telemetries and computational power, the question for firms is no longer what can be collected but what shall be collected. It is easy to add sensors and gather a vast amount of data but without knowing their purpose, the gains are marginal. In the field of Value-Driven Design, value is quantified and measured to ensure an overall higher output, something crucial for complex and ambiguous domains such as Product-Service Systems. This paper aims at finding the connection between PSS value and operational data from an industrial perspective. By using Participatory Action Research in a manufacturing firm, a value-data framework and method were developed that succeed in connecting these two extremities as well as visualize them with the support of network graph theory.

As manufacturing firms move from pure product-based business models toward Product-Service Systems offerings, value creation becomes more complex and requires more active cross-disciplinary collaboration. Especially non-functional values, those connected to the overall PSS behavior, depend on the successful alignment and coordination between perspectives. However, understanding the value creation itself, particularly among different departments or stakeholders, can be challenging. This paper conceptualizes a value model and a method based on Group Model Building to support value exploration of non-functional design objectives in early-stage Product-Service System design. The value model is constructed based on three layers of granularity to visualize how value is created holistically. Further, the proposed method allows participants to jointly explore and identify design objectives and put these in the context of value. The method and model were tested at two companies, showing that the proposed method and model successfully supported a collaborative value exploration in the non-functional domain. © 2023, IFIP International Federation for Information Processing.

The manufacturing sector is witnessing a paradigm shift toward servitization, where companies are transitioning from selling products to offering product-service systems. This shift creates additional challenges, where the providers must ensure the expected value throughout the operational phase of the solution. Especially when dealing with a system-of-systems (SoS), evaluating performance across diverse contexts and business models while understanding the interconnectedness between systems becomes critical. To address these challenges during the design phase, this article presents a novel integrated simulation framework that supports the development team in exploring value from a SoS perspective. This framework utilizes agent-based simulation and offers three key features: multifidelity, modular and multidisciplinary. The applicability of the proposed framework is further demonstrated in a quarry industry case.

Today, Manufacturing companies are adopting a servitization strategy and Product-Service System model to enhance value and remain competitive. Often, this transition also means to embrace a System-of-Systems (SoS) perspective. Concurrently, companies face challenges with volatile, uncertain, complex, and ambiguous (VUCA) environments. One way to tackle VUCA is to utilize simulation modeling. However, developing SoS simulations can be complex and cumbersome. This paper extracts lessons learned from six case studies to identify effective and ineffective practices in developing simulation models. The analysis has led to nine design principles for more effective simulation modeling. Furthermore, the paper explores simulation techniques for modeling SoS and discusses effective VUCA management. Finally, the paper proposes four future research directions to advance SoS simulation research.

Climate change is one of the most important challenges for society to solve. However, it cannot be fixed solely through greenhouse gas reductions; it requires proactive efforts. This paper investigates how Digital Twins (DT) can serve as a support for Climate Change Adaptation (CCA), which is required to achieve climate resilience. Data was collected from a workshop targeting the role of DT in CCA, which consisted of 92 participants from the public sector in various European countries. The data was analyzed and disseminated to establish guiding principles on how DT can be deployed and designed to best fit the characteristics of CCA work. Firstly, use cases were identified based on cross-examination between CCA activities and stakeholders. Secondly, a list of needs for developing DTs to serve as CCA support was developed. Together, these aspects provide a solid base for more effective DT design in supporting the transition to a more resilient society.