# Enhancing Production Efficiency Through Digital Twin Simulation Scheduling
## 3 Simulation Model--Practical Verification  
To verify the scheduling system, a parametric simulation model of the above-mentioned production system was created. Control methods for decision-making were compiled according to pre-designed algorithms, which can be found in the previous section. With the help of communication protocols, the connection of the simulation model with the database and the production system was established. The model included all key constraints of the production system: the machines, manual workplaces, human resources, logistics system, handling units, and warehouse management. Figure 5: Demonstration of the system in practice [34]. To objectively assess the accuracy and adequacy of the proposed model, acceptable deviation limits between the simulation and the real system were defined. The evaluation was conducted by comparing selected key parameters, using the following metrics. * The mean absolute percentage error (MAPE) for the production cycle time, with an acceptable error of up to 5%. The obtained MAPE value was 4.2%. * The difference in the total number of produced units compared to the real system was within the acceptable error margin of 3%, with an observed deviation of 2.8%. * The accuracy of resource utilization predictions (machines, AGVs, operators) was evaluated through the statistical analysis of historical data. The model was considered adequate if at least 90% of the simulated results fell within the predefined limits. These deviation thresholds were selected based on an analysis of typical fluctuations in production processes caused by factors such as variability in machine cycle times, differences in operator performance, and minor logistics delays. The 5% threshold for the production cycle time ensures that the model remains reliable even in the presence of minor discrepancies due to unpredictable real-world conditions. Similarly, the 3% deviation in the total production output was chosen to reflect the natural variability observed in manufacturing environments while maintaining the simulation accuracy for decision-making purposes. In order to increase the reliability of the experimental results, an error analysis and the calculation of confidence intervals for key simulation parameters were performed. The main factors affecting model accuracy include systematic errors (e.g., simplifications in the simulation model) and random errors (e.g., input data variability). To quantify the uncertainty in the results, 95% confidence intervals were calculated for the average production time and the number of units produced. The intervals were determined based on Student's \(t\)-test. The simulation model itself contains a number of logical constraints, input data (Figure 6), and a user interface for the control of the simulation (Figure 7). These bindings and input data are grouped based on defined properties into logical units and are stored in separate components that perform a certain function in the model. Figure 6: Simulation model environment. * Input Data--This section contains tables for the storage and updating of the input data for the simulation. The most important ones include a list of workplaces and manually set cycle times, a table with real cycle times from the TaT system, production orders, packaging regulations, the parameters of individual products, etc. Before the simulation run is started, the data from the regulations or the current data from the database are used, depending on the settings in the control menu. The connection to the MySQL database shown in Figure 8 is realized using the \"ODBC connectivity\" method. * Control Menu--This contains individual elements of the control menu, ensures its correct functionality, and performs basic calculations necessary to start the simulation run. Through the control menu, the user sets the simulation settings and manages the production scheduling process. * AGV--This is a tab that contains a library of logistics equipment, their characteristics, and the control methods.