## 4 Reference Scenario
### Results of Simulation: Performance Analysis  
The company's operational strategy is focused on reducing inventory to the bare minimum. As a result, production is aligned with market demand of around 500 cabinets per month. The model reflects this productive approach. To ensure an accurate representation of actual conditions, experts conducted a test by defining 2 days as the initial break-in time. This period allows the system to stabilize and optimize processes, ensuring that simulation results are reliable and consistent with the company's actual production activity. To have a clear and reliable view of the production activity, this was simulated for three different time intervals representing a day, a week, a month, and six months of production \begin{table}
\begin{tabular}{l c c} \hline \hline
**Year 2022** & **Output of Real Production** & **Output of Digital Model** \\ \hline January & 78 & 110 \\ \hline February & 184 & 130 \\ \hline March & 213 & 151 \\ \hline April & 100 & 152 \\ \hline May & 153 & 155 \\ \hline June & 150 & 153 \\ \hline July & 145 & 153 \\ \hline August & 140 & 153 \\ \hline September & 155 & 152 \\ \hline October & 138 & 153 \\ \hline November & 140 & 153 \\ \hline December & 120 & 153 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Outputs of the digital model compared with those of the real production process. activity. The outputs of the digital model, representing the number of cabinets produced in these specific ranges, are shown in Table 7.  
The extraordinary capabilities of simulation are evident at this stage. The purpose of the simulation model is to define failures and bottlenecks in the process to optimize it through the implementation of technologically advanced tools. Witness software is used to identify important processes and verify actual results based on production data. For process analysis and evaluation, detailed process performance reports, including management of external variability, are generated without any impact on actual production and in a very short time. Among the elements of analysis in the software, \"statistics\" are taken into consideration for this study. These are performance indicators that provide detailed information on different aspects of the simulated system [107]. In detail, in the simulation model, each component element has its own statistics that provide differentiated information, varying according to the model element analyzed [108]. Machine statistics provide, as a percentage of the total simulation duration, detailed data on several operational aspects: the actual working time of the machine, the time it was stopped due to failure, setup, or waiting for the operator. But also, whether it has been stopped due to causes related to the other elements or waiting to work. These data not only provide insight into the operational efficiency of machines, but also highlight interactions and dependencies with other elements of the system, such as parts, buffers, and workers. This brings out the specific reasons why interruptions may occur, helping to identify and resolve bottlenecks in the production process (see Figure 7). The graph is composed of bars of different colors. Each color shows, in percentage form, the operating status of the machine. Yellow indicates the idle state of the machine and therefore waiting to work, red represents the fault state, blue shows a blocked state due to a lack of availability of material from the upstream machine, pink indicates waiting due to the unavailability of the operator who is needed for the process in certain phases, light blue indicates the setup phase to which the machine is subject, and finally, green represents the processing phase. However, given the large number of machines present in the process, for better analysis and a more in-depth study, the three macro-flows that make up the process were also studied in detail separately: structure flow statistics, statistics of doors/cover flow, statistics cabinet flow.