## 4 Development and Simulation of a Predictive S&OP Methodology
### Simulation Results for the Case Study of an Automotive Plant  
After the validation, the results for the three demand scenarios were extracted for the selected key performance indicators. In the first potential scenario, the predictive S&OP simulation model produces better results for all relevant indicators, as shown in Table 8, except for the average capacity utilization. However, this model's capacity utilization level is lower, due to the greater prior increase in capacity, compared to the classical S&OP model. Among these parameters, it is important to point out five characteristics:  
* The volume loss of 32 units versus 1320 units in the classical approach. This means that the predictive model can adapt itself to demand needs by not allowing the loss of customers and the associated orders. * The total quantity delivered is more than 2000 additional units over the simulation period. Moreover, the quantity delivered on time is almost 20% higher than that in the classical approach. This means that the model can fulfill its commitments in terms of delivery quantity and date. As a result, the planning stability of this model in this scenario is higher than that in the classical S&OP model. * The WIP stock along the manufacturer's production process is almost 20 times lower for the predictive S&OP model, as it is able to regulate production and stocks by predicting the cyclical demand patterns. * The customer order lead time is more than two times lower in the predictive S&OP model aligned with almost half of the average order backlog. * The operational savings in the predictive S&OP model are higher due to this model's ability to plan personal resources based on future demand needs. Therefore, similar to the first scenario, based on a comparison of the models, the results show that the predictive S&OP simulation model would be desirable in this demand scenario, due to its combination of a highly-efficient lead time, on-time delivery, and WIP stock, while also securing the potential customer demand with higher operational savings and the same level of investments as that of the classical S&OP approach. Figure 15: Results of the validation for nominal capacity using the extreme-value test (own elaboration). Legend: red lines for lower nominal capacity; blue lines for higher nominal capacity. In the second potential scenario, the predictive S&OP simulation model produced better results for all relevant indicators, as shown in Table 9. In addition, two different trends can be outlined, in comparison to scenario 1:  
* The capacity utilization level is higher for the predictive S&OP model because the investment in new capacities is 5.7, while for the classical S&OP model, this value is 14.7 million euros. This difference occurs because the predictive S&OP model is able to determine that no more capacity is needed to secure delivery on time (95.3%), and a delivery quantity of almost 1500 units more than the classical model can be secured. * As the customer demand in this scenario is lower, and the predictive S&OP model optimizes resources and investments, the difference between the two models is not as high as that under the first demand scenario. Finally, as in the first scenario, based on a comparison of the models, the results show that the predictive S&OP simulation model is most desirable in this demand scenario, due to its combination of a highly-efficient lead time, on-time delivery, and WIP stock, while also securing potential customer demand with higher operational savings and a lower level of investments, compared to the classical S&OP approach. As explained for scenarios 1 and 2, similar results were obtained for the third scenario as it shows Table 10. However, there are two relevant facts:  
* In this scenario, the supply chain system is under stress due to cyclical demand with a high average; the predictive S&OP model is, however, able to achieve 97.0% on-time delivery while maintaining low stock levels, customer order lead times,