## 4 Manufacturing Process Case Study
### Simulation Results  
As shown in Table 2, the third model presents better results than those of the second model, and the second model presents better results for all relevant indicators than those of the classical simulation model. The simulation results for the three models are presented in Table 2. It shows how all three models have the same demand and, for all of them, money availability is set as given, i.e., 100%. Furthermore, it can be seen that for all the other flow areas, information, material, energy, personnel, and capital equipment, availabilities increase from the classical simulation model to the integrated manufacturing simulation model and to the model with regulation. This occurs due to the different policies applied in each of the models as better process control, maintenance policy, quality control, and areas in focus enhance the availability of systems contributing to a better OEE that reaches a 74.1% for the model with regulation, in comparison with the classical simulation model with 28.7%, showing how the policies can determine the manufacturing performance of a system. This fact has a significant influence on labor productivity and on-time delivery to customers that increases from 32.1% in the classical simulation model to 95.4% for the model with regulation. This increase results from the regulation of the whole system to meet customer demand. Figure 7: Simulation model validation: number of employees (own elaboration). Figure 8 represents the average OEE for three different information disruption scenarios, low, medium, and high scenarios. It can be observed how, for a high information disruption level, the OEE is 5% lower than that of the low disruption scenario. As a result, the modeling and sensitivity analysis of these disruptions can provide a syndication of the risks related to each flow, process, and system, in this case related to the information flow. Moreover, the OEE in Figure 8 is derived from the aggregation of, between others, the daily availability rate of the manufacturing process. In another case, the model developed can also support the analysis of process parameters affecting product quality. For the case study, an example is the parameters of the shot peening process for the finishing of the shot-peened round bars. Thus, Figure 10 represents the average quality rate (%) results for a different set-up of shot peening parameters: low  
Figure 10: Average quality rate (%) results for shot peening parameters: low and high average and standard deviation of shot density. Figure 9: Average availability rate (%) results for information flow disruptions: low, medium, and high disruptions. and high average and standard deviation of shot density applied to the round bars to secure its surface product quality requirements. As it can be observed, the correct regulation and control of the parameters can lead to significant improvements in final quality rate with an almost 30% increase, from less than 70% in the low shot density and high standard deviation to almost 98% in the high shot density and low standard deviation. As a consequence of the Figures 8-10, the benefits of the approach can be derived as a monitoring based on a digital twin model together with the assessment of different scenarios to support the analysis of the manufacturing current state, the identification of bottlenecks resources and flow areas, as well as the definition of appropriate measures and policies to improve the manufacturing system state. The methodology applied is a natural evolution of the existing methodological approaches to a modeling framework toward the fourth industrial revolution. As a result, the proposed methodology considers more managerial aspects that can regulate the manufacturing process and, therefore, the complete system [14]. [PERSON] et al.