# A Comprehensive Digital Model Approach for Adaptive Manufacturing Systems
## 4 Discussion
This innovation is particularly relevant as it allows for the assessment of various production strategies while taking into account real-time changes in capacity at different workstations, something most existing models fail to do. The methodology presented in this article can be applied to a range of adaptive manufacturing systems, including reconfigurable ones. Whether assessing the impact of configuration changes in reconfigurable systems or optimizing the operations of competency islands, the methodology provides a structured approach. However, this does not exclude the use of the described model for traditional manufacturing systems as well. As Industry 4.0 emerges, the interplay between digital models, simulation, and real-time responsiveness will shape the future of manufacturing. Further research could explore refining the methodology's implementation for reconfigurable systems and integrating it with emerging technologies such as IoT and artificial intelligence. In future investigations of adaptive production systems, the size of the number of objects and entities in the system will be the basic limiting factor. This is because even with abstraction, a higher number of objects can slow down simulation speed, potentially affecting the speed of decision-making in operative planning for system elements that rely on a digital model for calculations. This study highlights the potential of digital models in shaping adaptive manufacturing systems. Whether applied to competency islands, reconfigurable systems, or beyond, the methodology serves as a framework for harnessing data-driven intelligence to guide manufacturing towards a future characterized by efficiency, agility, and innovation. Several areas for future research can be identified based on the insights gained from this doctoral dissertation and previous work. The study describes adaptive manufacturing systems such as reconfigurable production systems and competency islands, but existing literature does not provide clear distinctions between these systems. Further research should specify the scenarios in which either system is most applicable. Additionally, developing expert systems and knowledge databases are essential for establishing adaptability within manufacturing systems. This applies not only to manufacturing systems but also to supply chain management, inventory control, and production operator work. Continuous updating of the knowledge database is crucial during ongoing processes. Advancing adaptive manufacturing systems should also include the use of hierarchical task network (HTN) methods for production task planning. In this approach, planning problems are specified in an HTN, providing a set of tasks that can be derived from the initial task network by decomposing complex tasks into simpler ones while maintaining ordering constraints. The Tecnomatix Plant Simulation software was used in this research however, comparing simulation results from this software with those from AnyLogic, which includes built-in agent-based simulation methods, could provide insights into data accuracy and software suitability. A deeper exploration of next-generation modeling and simulation technologies is necessary to align with the upcoming network-oriented, service-based, individualized, and intelligent manufacturing environment. This includes aspects like advanced digital models, model-based engineering, cloud-based simulation, data-driven modeling and simulation, and embedded simulation, among others.