# Digital Twin-Enabled Decision Support Services in Industrial Ecosystems
## 4 Results
### Relating Business Questions to Decision Problems  
In the industrial cases where decision making is centered around operations processes, the decision chain extends from the equipment or object to the operations processes [32]. The digital twin of the equipment (see Figure 2) receives data from the real equipment and presents an indication of its performance, e.g., a health condition. For example, the data coming from the equipment may be physical sensor data, such as vibration or temperature. The digital twin model of the equipment computes the indications about the health condition based on these physical data. This indication potentially is a trigger that the current or predicted condition of the equipment is suboptimal and that it requires maintenance to be brought back to its normal condition.  
However, this is raw information and it does not support decision making until it is interpreted in the context of the ecosystem. The actor managing the asset needs to decide which actions to take by trading off various resource constraints including opportunity costs for not allocating constrained resources to other assets while servicing one specific asset. In particular, this needs to take into account the time dynamics of customer challenges. For example, two machines of two different business customers with equal service contracts may require attention in the same time window. Given limited human service resources and spare parts, the manager needs to trade off which customer to serve first. This depends  
Figure 1: Methodological procedure (based on [29]).  
not only the nature of the technical problem, the required skill and spare parts, the traveling time, and the required effort to perform the maintenance but also on the current urgency of the reduced health condition for the customer's operations (e.g., depending on the current production shift of the customers).  
The digital twin of the processes (right-hand side of Figure 2) integrates a multitude of individual digital twins representing the required information for the decisions and thus provides the required support. In real environments, this collection of digital twins cannot represent the entirety of the required information for a broad support of the decision making. Decision support based on only the available technical data would not provide the business value needed by the decision maker. Therefore, in this concept, the digital twin of the processes presents several variants of quantitatively evaluated service process actions to the decision maker, who integrates these quantitative data with with other knowledge resources (such as own management judgment) [15]--the wisdom part of the DIKW concept [28].  
An actor managing the asset makes a decision by trading off resource constraints (Figure 3). The actor can trade off between two extremes: immediate maintenance--requiring available maintenance resources; or immediate replacement--requiring available spare equipment; or any point on the continuum between these. With the help of the process twin, the decision maker may judge whether there are spare equipment or maintenance resources available and at what cost and how to trade off the available resources among several equipment instances of the installed base.  
Figure 2: Digital twin of equipment vs. processes (extending on [32], terms according to [33]).