# Digital Twin-Enabled Decision Support Services in Industrial Ecosystems
## 2 Literature Review  
West [3] described the need to identify business questions for different situations as a critical aspect of defining the use cases. The use case needs to consider the actors, processes, and the machines (e.g., people, processes, and things) to understand the problem and build, via the digital twin, different options that can support the decision-making processes. The digital twin provides a way to integrate the explicit and the tacit knowledge within a system [4]. This is important from the perspective of knowledge management, as information is only truly valuable when relevant to the individual actor. Therefore, a distinction is made between commoditized information and high-value information. High-value information offers the potential to solve individual or organizational issues and is a result of customization [5]. [PERSON] represents an engineering perspective towards information and semantics: \"semantic aspects of communication are irrelevant to the engineering problem\" [6]. Visualization of the decision support process, rather than just offering a number, provides the basis for a common understanding of the problem space and, in turn, supports value co-reaction between actors with different perspectives. This is achieved in the case of the digital twin by putting the data into context [7]. This is in line with [PERSON] [8], who identified a knowledge base, a decision context, and points and interface as critical aspects of any decision support process. The digital twin is defined as a virtual representation of a connected physical equipment that represents in realtime its static and dynamic characteristics [9]. It has the potential to add value to many applications and, in particular, to industrial processes, and thus attracts increasing interest among practitioners and scholars [10]. There is a wide range of understandings and definitions of the digital twin and its applications [11]. The research streams of industrial services, data-driven services, and digital twins converge in the field of decision support services. The digital twin can be considered as an implementation of data-driven services that enable the exploration of scenarios and alternatives and thus support decision making in business environments [12]. Therefore, changing the perspective and conceptualizing the digital twin as an approach for value creation from a service perspective is a promising new research direction. Decision Support Systems (DSS) are information systems that help users in their decision activities, which can extend to highly automated decision making. The steps of the decision making process are described in [13; 14; 15; 16; 17] and can be simplified to: 1. Describe the set of possible actions or alternatives. 2. Evaluate these actions. 3. Select the preferred action. Step 2 of the simplified decision process points to the application of the digital twin, in particular digital twin-based simulation, which allows users to explore the variants described in step 1 and evaluate their consequences. This is supported by [17], which states that simulation is a very common decision support instrument, in particular for decisions in maintenance or capacity planning in product-service-systems, for instance. [PERSON] talks about knowledge building being a creative cognitive process, which relates concepts far apart in an individual's memory accessible through metaphors [18]. They claim that tacit knowledge can be \"transformed\" into communicative knowledge by recognizing contradictions through metaphor and resolving them through analogy [19]. In addition, others emphasize that transformation is not a reproduction but rather a dynamic process of building a relationship between the individual and the state of \"knowing\" [20; 21]. For this reason, knowledge is agile and a changing object, and using the correct information at the right time enables the development of knowledge. Therefore, decision support services must support the different perspectives of the business, technology, and operations.