## 6 Conclusions  
This section is divided into theoretical, managerial, and empirical conclusions, in addition to an explanation of the limitations of the research performed and a description of the potential future research derived from the findings. Theoretical conclusions: This study aimed to develop a holistic approach for the assessment of the status and performance of manufacturing systems and processes. In order to achieve these goals, a simulation model is successfully applied:  
* A simulation modeling framework for Industry 4.0 based on second-order cybernetics is developed. * The interrelationships among manufacturing system areas are described. * Managerial conclusions:
* The current challenges and opportunities for manufacturing systems, digital twins, and manufacturing simulation were described. * The selected conceptual model applied to digital twin models can be a key tool in managerial decision making. Empirical conclusions: To prove the utility of this new concept, a simulation example for a metallurgical manufacturing system was created by applying system dynamics. Three different models were successfully developed: first, an input-output manufacturing process; second, a manufacturing process with a controller; third, a second-order cybernetics manufacturing process with self-regulation. The benefits of the change from an input-output process to a self-optimizing production system are as follows:  
* Global optimization of the production system. * Higher manufacturing system availability. * Higher product quality. * Higher system performance. * Higher delivery reliability. * Limitations of the research work:
* Individual interactions among staff, machines, robots, and other elements were not considered in the simulation model. * The simulation lacks feedback of the real system with real-time data from the manufacturing plant. * The complexity of the metallurgical manufacturer was partially built in the simulation model. * The complexity of processes such as steelmaking was not built in detail. * The organization structure and interfaces were not considered in the simulation model. * Certain quality requirements of the product were not considered. * Investment and costs were assumed. * Future research based on this study's findings could focus on the following:
* Transferring this research method to real production systems and applying it as a digital twin tool or an assistance tool for sales, production, quality, and maintenance leaders by centralizing all data related to the manufacturing system, thereby allowing the simulation of what-if-scenarios, as well as for the monitoring and assessment of the manufacturing system state. * Considering organization units and their communication within the simulation model. * Improving the model based on implementation feedbacks as well as applying it to production networks with several production plants. In summary, this study shows the potential benefits of the conceptual approach and the applied simulation case study, allowing for better decision-making for both manufacturing and assembly managers and equipment and service suppliers. This ensures continuous optimization along the manufacturing asset lifecycle. The proposed model offers an example of how manufacturing processes can be assessed, managed, and controlled in an integral way. As a result, the proposed methodology represents a useful tool for organizations and managers in order to increase their efficiency, competitiveness, and, therefore, viability over time. Furthermore, cloud computing and cloud manufacturing offer a new opportunity for enterprises, as they allow them to transform their traditional business models. Conceptualization, M.G.-G. and S.G.-G.; methodology, J.R. and S.G.-G.; software and programming, S.G.-G.; validation, J.G.-G. and S.G.-G.; data analysis, S.G.-G.; writing (review and editing), S.G.-G. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The authors declare no conflict of interest.