## 5 Experimental Results and Discussion
### Analysis of Possible Response Strategies  
The Digital Model of the real manufacturing system was also used to simulate and evaluate the goodness of two possible response strategies:  
1. The company of the case study enters into agreements with an additional supplier of the main raw material (i.e., wood), so as not to depend on a single producer. 2. The company of the case study enters into agreements with part-time workers, who are able to replace permanent expert workers in the event of an outbreak. Therefore, an additional 15 scenarios were considered, as in Table 4. The sign (\(\checkmark\)) means that the specific strategy is adopted. 10 runs were carried out for each of these new scenarios. The results are in Table 5. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & **Supplier’s** & **Manufacturing** & **Agreements** & **Agreements** \\  & **Delay** & **Operators’** & **with an** & **with Part-Time** \\  & **(Day)** & **Unavailability** & **Additional** & **Workers** \\  & & **(Day)** & **Supplier** & \\ \hline S1\({}^{\prime}\) & 7 & - & \(\checkmark\) & - \\ S2\({}^{\prime}\) & 5 & - & \(\checkmark\) & - \\ S3\({}^{\prime}\) & 2 & - & \(\checkmark\) & - \\ S4\({}^{\prime}\) & - & 7 & - & \(\checkmark\) \\ S5\({}^{\prime}\) & - & 10 & - & \(\checkmark\) \\ S6\({}^{\prime}\) & - & 14 & - & \(\checkmark\) \\ S7\({}^{\prime}\) & 7 & 7 & \(\checkmark\) & \(\checkmark\) \\ S8\({}^{\prime}\) & 7 & 10 & \(\checkmark\) & \(\checkmark\) \\ S9\({}^{\prime}\) & 7 & 14 & \(\checkmark\) & \(\checkmark\) \\ S10\({}^{\prime}\) & 5 & 7 & \(\checkmark\) & \(\checkmark\) \\ S11\({}^{\prime}\) & 5 & 10 & \(\checkmark\) & \(\checkmark\) \\ S12\({}^{\prime}\) & 5 & 14 & \(\checkmark\) & \(\checkmark\) \\ S13\({}^{\prime}\) & 2 & 7 & \(\checkmark\) & \(\checkmark\) \\ S14\({}^{\prime}\) & 2 & 10 & \(\checkmark\) & \(\checkmark\) \\ S15\({}^{\prime}\) & 2 & 14 & \(\checkmark\) & \(\checkmark\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Scenarios for assessing possible response strategies to disruptions. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & **Mean** & **Min** & **Max** & **stdDev** & **95\% Confidence** \\  & **(Unit)** & **(Unit)** & **(Unit)** & **(Unit)** & **Interval (Unit)** \\ \hline S1\({}^{\prime}\) & 629.3 & 624.0 & 636.6 & 4.244 & (626.3; 632.3) \\ S2\({}^{\prime}\) & 659.4 & 654.0 & 665.0 & 3.204 & (657.1; 661.7) \\ S3\({}^{\prime}\) & 697.6 & 693.0 & 702.0 & 3.134 & (695.4; 699.8  
As can be seen from the content of Table 5, the adoption of one or more response strategies can have a very significant effect on the performance of the manufacturing plant. In this context, it can be extremely useful to make a comparison between pairs of scenarios that differ only in the adoption/non-adoption of response strategies (i.e., S1 and S1\({}^{\prime}\), S2 and S2\({}^{\prime}\),..., S15 and S15\({}^{\prime}\)). In particular, the most significant effect of the response strategies occurs in scenarios eight and nine, where on average, productivity increases by approximately 26%, thanks to agreements with an additional supplier and part-time workers. It should be noted that the scenarios between the second and sixth see a limited increase in average productivity, which oscillates between 0.2% and 3.7%, probably because one and only one of the two disruptive events is taken into consideration. In any case, the strategies examined always involve an average increase in productivity, which means minimizing lost earnings. To replicate reality as much as possible, it is necessary to specify that for part-time workers, with whom agreements are made (i.e., second response strategy), an efficiency of 0.7 has been hypothesized. Therefore, their presence makes it possible to limit the loss of productivity due to simulated disruptive events but could lead to a slight increase in defective units (i.e., waste), and therefore, in disposal costs.