## 3 Methodology and Materials  
### Methodology  
In the research performed, the methodology is as follows:  
1. Literature review based on search by keywords \"Viable System Model\", \"Organizational Strategy\", \"Innovation Management\", \"New Technologies and Products Decision-Making\", \"Projects Evaluation for Innovations, New Products, and Technologies\", \"Digital Twin for New Technologies and Products Decision-Making\", \"Innovation Management Systems\", \"Innovation tools\", \"Information System for Innovation\".
2. Methodological approach selection: The Viable System Model (VSM) was selected for its structure for viability and System Dynamics (SD) for its ability to analyze the behavior of complex systems over time. The VSM is built on three main principles: viability, recursivity, and autonomy. Viability is a property of every system that can react to internal and external perturbations in order to maintain separate existence [32]. [PERSON] explained that a company is also a viable system because it tries to survive [33]. Innovation management has been an increasingly covered topic in scientific and management literature over the last fifty years, as innovation is of key importance for the survival of an organization. When an organization is deeply involved with--and used to--what they are good at (core competencies), it becomes trapped in it. When the environment changes (e.g., changing consumer needs, changing regulations, etc.), organizations are not able to adapt [1]. The VSM suggests a new organizational approach that is based on the principle of recursion, according to which viable systems are themselves composed of viable systems, each having self-organizing and self-regulatory characteristics [34]. Based on this capability of information exchange, the term \"Industry 4.0\" was introduced by representatives of business, politics, and academics that promoted the idea of digitization together with autonomy and self-behavior of machines as an approach to strengthening the competitive power of the German manufacturing industry [35]. Self-optimization [35] and the advances of new IT, such as IoT, cloud computing, and Big Data, enable smart characteristics such as self-sensing, self-organizing, and self-adaptive [36]. This enables the smart product to self-organize its required manufacturing processes and its flow throughout the factory in a decentralized manner by sharing smart data with the CPS [37]. However, the theories on self-organized systems are not mature, and complex system research remains a hot topic [38].
3. Conceptual model design: The definition of recursion levels, management, and control tasks for a novel innovation management model for strategic, tactical, and operative levels.
4. Development of novel methodology for digital ecosystems: digital twin, simulation, quality techniques, and information systems' databases.
5. Simulation and analysis of results: The development of various simulation models based on predefined assumptions, logic formulation, scenario extraction, and analysis of results for a given set of Key Performance Indicators (KPIs).
6. Critical reflection and outlook: Cooperation between research partners and the chosen methodological approach was set as a combination of a literature review, conceptual development, digital information ecosystems, quality management, and control techniques and simulation in a case study for different companies.