Our thinking in systems is influenced by the academic discipline of Service Science. In this approach, we define systems as social entities that transform resources into valuable, rare, inimitable and non-substituable resources to develop increased capabilities. System interaction and service value co-creation – the cooperation and collaboration of independent system stakeholders – within and among heterogenous systems and ecosystems are core to launch innovation. Additionally, service interaction and value co-creation enable gaining and sharing of mutual knowledge and expertise to benefit all system stakeholders. In our approach we differentiate between service systems, ecosystems and – as a special form – autonomous systems.
Service Science: Service Systems & Ecosystems
Service Science is an interdisciplinary field of research of service, service systems and service ecosystems as well as how service (eco-) systems interact and co-create value (service). Considered from an engineering perspective, major emphasis is on service (eco-) system innovation – the (1) dynamically configuration (transformation) of people, technology, organizations and shared information, (2) computation and calculation of value from multiple stakeholder perspective, (3) reconfiguration of access rights to resources by mutually agreed-on value propositions (resp. the access rights associated with entity resources are reconfigured by mutually agreed-on value propositions) and (4) computation and coordination of actions with others through symbolic processes of valuing and symbolic processes of communicating (Spohrer et al., 2012; Maglio & Spohrer, 2013).
Digital twin technology as a means for decision making – a practical example:
Autonomous Systems
A special form of systems are autonomous systems. Autonomous systems are capable of solving complex tasks independently. They are based on specialized algorithms and methods of artificial intelligence on the basis of machine learning and deep learning. They are learning from data and can largely act without any human interaction even in unknown situations and environments. Examples for autonomous systems are not only classic robots and networks, but also production facilities, vehicles, drones and software systems. The different challenges for creating an autonomous system are the sensing of the environment (e.g. computer vision), interpreting the gathered information automatically, decision-making based on these information and automatically carrying out actions to solve the tasks of the system. An additional challenge for creating and working with autonomous systems is the development of infrastructure and communication methods that allow the different parts (e.g. multiple robots) to collaborate and cooperate with each other, which is often needed for highly complex or big scaled tasks of an autonomous system.
