The application of digital twins provides value creation within the fields of operations and service management; existing research around decision-making and value co-creation is limited at this point. Prior studies have provided insights into the benefits of digital twins that combined both data and simulation approaches; however, there remains a managerial gap. The purpose of this paper is to explore this research gap using input from a multiple case study research design from both manufacturing environments and non-manufacturing environments. The authors use ten cases to explore how digital twins support value co-creation through decision-making. The authors were all involved in the development of the ten cases. Individual biases were removed by using the literature to provide the assessment dimensions and allowing a convergence of the results. Drawing on the lessons from the ten cases, this study empirically identified eight managerial issues that need to be considered when developing digital twins to support multi-stakeholder decision-making that leads to value co-creation. The application of digital twins in value co-creation and decision-making is a topic that has developed from practice and is an area where a research gap exists between theory and practice. A cross-case analysis was developed based on the literature and the ten cases (eight industrial and two pilot-scale cases) providing the empirical findings. The findings describe how firms can design, develop, and commercialize digital-twin-enabled value propositions and will initiate future research.

The goal of this paper is to further elaborate a new concept for value creation by decision support services in industrial service ecosystems using digital twins and to apply it to an extended case study. The aim of the original model was to design and integrate an architecture of digital twins derived from business needs that leveraged the potential of the synergies in the ecosystem. The conceptual framework presented in this paper extends the semantic ontology model for integrating the digital twins. For the original model, technical modeling approaches were developed and integrated into an ecosystem perspective based on a modeling of the ecosystem and the actors’ decision jobs. In a service ecosystem comprising several enterprises and a multitude of actors, decision making is based on the interlinkage of the digital twins of the equipment and the processes, which is achieved by the semantic ontology model further elaborated in this paper. The implementation of the digital twin architecture is shown in the example of a manufacturing SME (small and medium-sized enterprise) case that was introduced in. The mixed semantic modeling and model-based systems engineering for this implementation is discussed in further detail in this paper. The findings of this detailed study provide a theoretical concept for implementing digital twins on the level of service ecosystems and integrating digital twins based on a unified ontology. This provides a practical blueprint to companies for developing digital twin based services in their own operations and beyond in their ecosystem.

The provision of advanced services becomes a relevant differentiation for manufacturing companies, in particular for SMEs (small and medium-sized enterprises). These services, also referred to as smart services, require the collection and processing of data from equipment, customers, and processes, as well as the development of analytics models and the interpretation of their results for improved service value propositions. These steps require significant engagement of the firms in terms of technical and human resources, skills, and new types of value creation processes, which is a major hurdle especially for SMEs. As the value that can be achieved when leveraging the information inherent in the data is not known a priori, the enterprises are not sufficiently informed for taking the decision to engage. Consequently, they are missing out on relevant business opportunities due to a lack of quantitative models for assessing the value of data. In this paper, we discuss the existing literature on data valuation models and explore the state of practice through an interview-based field study. We develop a model for the utility-based valuation of data that helps companies expand their fund of knowledge and skills about the value of their data and thus make better-informed investment decisions. A simulation-based model is developed to support companies in this assessment by providing quantitative insights in the value potential of the data in various use cases. This model opens a series of new research questions for the further elaboration of the data valuation models.