## 7 Discussion  
Firstly, the literature identified a need for an innovation management model that could cope with the environment's complexity and define the gap in the existing application of innovation management tools, techniques, and systems for innovation. The conceptual model and the case study results show how a digital ecosystem can be developed and provide a platform for selecting, managing, and monitoring innovation projects based on the PDCA methodology. In addition, many companies perform assessments, especially at the conception of new ideas, inventions, and concepts, without introducing any information into a system, thus reducing the traceability and the learning options for the innovation project as well as for future innovation projects. In this regard, the DE4.0 and the DIIS provide a suitable response to this challenge, providing transparency and adaptability to enhance and develop dynamic capabilities, thus securing organizational sustainability. Secondly, it is clear from the results that the organizational topology in terms of how a company is able to introduce an innovation is of vital importance. In some cases, this can be helpful when deciding whether to proceed or not with a certain innovation. This led us to the need to develop customized innovation plans depending on organizational topology. This topology is to be defined by the maturity level in a set of criteria such as culture, organization, technical, etc. Thirdly, the PDCA methodology can be performed continuously or in discrete intervals. If the ecosystem is digital, it can be assessed at any time from any location, making it possible to perform adjustments in real time while others are performed at time intervals to evaluate indicator dynamics leading both options to efficient project management. Fourthly, as shown from the results of the different companies, it has been described how the data, information, and learning from similar innovation project experiences are key when designing and deciding on new innovations. Therefore, this article suggests including the iterative learning process in the innovation life cycle as it provides significant advantages for the scope and scalability of the innovation introduction, as well as for its market diffusion. Fifthly, the use of information systems connected with digital twins of an organization and its processes provides the basic framework for enhancing the system with simulation capabilities. These are key for identifying the policies and innovation characteristics that reduce the overall risks in the short and long term, thus not only for the future of the organization based on the innovation implementation but also for the sustainability of the whole company, its supply chain, and the related environment. With multiple sources of storing, treating, and sending/receiving data and information, cybersecurity plays a key role in these digital ecosystems. Furthermore, artificial intelligence is fundamental to enhancing the capabilities of the DE4.0 and the DIIS. The advantage of this approach is the ability to convert all historical data, facts, and information from stakeholders and organizations, each with a specific set of capabilities, operating in different related market environments, into a fact- and scientific-based innovation plan. Moreover, this approach can improve dynamically over time thanks to the combination of elements that lead to a new management era that provides adaptability mechanisms oriented to organizational goals. Management is about running and changing any kind of organization. In this context, this research provides a step forward as it generates a new management framework based on innovation management according to current technological possibilities that enable ubiquitous information interconnection in a single source of truth. As a result, the paper recommends putting innovation at the core of organizational sustainability as the driver of change while securing future operating stability.