## 4 Development and Simulation of a Predictive S&OP Methodology
### Simulation Results for the Case Study of an Automotive Plant
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Key Performance Indicator (KPI)**} & **Classical S\&OP** & **Predictive S\&OP** \\  & & **Simulation Model** & **Simulation Model** & **Simulation Model** \\ \hline
1 & Cumulated potential demand (units) & 34,062 & 34,062 \\
2 & Cumulated demand (units) & 32,742 & 34,030 \\
3 & Cumulated volume loss (units) & 1320 & 32 \\
4 & Cumulated quantity delivered (units) & 32,154 & 34,478 \\
5 & Average quantity delivered on-time per week (\%) & 77.3 & 95.9 \\
6 & Average WIP stock (units) & 4374 & 278 \\
7 & Average capacity utilization of the production plant (\%) & 71.0 & 66.8 \\
8 & Average customer order lead time (days) & 65.0 & 28.7 \\
9 & Average mean absolute deviation (MAD) per week (units) & 28.5 & 17.0 \\
10 & Average order backlog (units) & 1847 & 971 \\
11 & Cumulated operational savings (M euros) & 0.1 & 0.8 \\
12 & Cumulated investment value (M euros) & 14.7 & 14.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Simulation results for demand scenario 1 (probable scenario). \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Key Performance Indicator (KPI)**} & **Classical S\&OP** & **Predictive S\&OP** \\  & & **Simulation Model** & **Simulation Model** \\ \hline
1 & Cumulated potential demand (units) & 32,174 & 32,174 \\
2 & Cumulated demand (units) & 31,202 & 32,064 \\
3 & Cumulated volume loss (units) & 972 & 110 \\
4 & Cumulated quantity delivered (units) & 30,948 & 32,445 \\
5 & Average quantity delivered on-time per week (\%) & 76.6 & 95.3 \\
6 & Average WIP stock (units) & 4159 & 267 \\
7 & Average capacity utilization of the production plant (\%) & 68.9 & 71.7 \\
8 & Average customer order lead time (days) & 59.7 & 32.8 \\
9 & Average mean absolute deviation (MAD) per week (units) & 31.5 & 20.0 \\
10 & Average order backlog (units) & 1602 & 1004 \\
11 & Cumulated operational savings (M euros) & 0.2 & 0.7 \\
12 & Cumulated investment value (M euros) & 14.7 & 5.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Simulation results for demand scenario 2, the feasible scenario. and securing potential demand with only 131 car orders lost. This model also yields lower investments by almost six million euros alongside almost one million euros in operational savings compared to the classical S&OP model. * As the customer demand in this scenario is higher than that in the previous two scenarios, and the predictive S&OP model optimizes resources and investments, the differences between the two models are greater than those under other scenarios. Therefore, based on a comparison of the models, the results show that the predictive S&OP simulation model is most desirable among the three demand scenarios due to its higher efficiency in terms of customer service level, planning stability, investments, and operational savings. The key factor for these results is the ability to predict potential inefficiencies to prevent them from happening by applying measures before the short term, at which point only corrective measures can be applied. Another key factor is that the forecast error, measured with the MAD, is much lower for the predictive S&OP model under all three scenarios. This provides adequate planning stability to detect deviations, identify effective measures, and determine the best fit to decide and implement the relevant solutions to maintain and boost the supply chain system's efficiency. Finally, a comparison of the factors among the three simulation scenarios was performed, as shown in Table 11. This table compares the scenarios by classifying them as low, middle, or high value in comparison with the other two scenarios. Scenario 1 has a middle demand level and middle values for many of the factors. On the other hand, as the demand uncertainty is lower here than in scenario 2, the volume loss is lower, the lead time is shorter, and the order backlog is lower.