## 5 Conclusions  
As a result of this research, the main hypothesis was supported based on the following observations:  
* Using the new conceptual model for sales and operational planning, based on a statistical treatment of customer demand, planning stability and accuracy can be improved. * The predictive S&OP model is able to predict potential inefficiencies, define causes, determine effective measures, and implement solutions, thus increasing the efficiency of resource utilization within a supply chain and securing the long-term viability of manufacturers. * The predictive S&OP model enables manufacturers to improve their service levels, thus securing their entire potential customer demand. * The abovementioned points demonstrate the need for such a system as a standard tool for managers in the future to increase the efficiency and adaptability of manufacturing organizations by following the principle of \"predicting to define preventive measures to avoid correcting\". * System dynamics provides the necessary platform to determine the cause-effect parametrization and thus obtain results relevant to the research purpose. * The simulation of an automotive supply chain using the developed conceptual model offers better results than those obtained using currently-available S&OP approaches for dealing with different demand scenarios. The contributions of this paper can be summarized under three areas: theoretical, managerial, and empirical. In this section, the limitations of the study and potential future research are also described. Theoretical conclusions:  
* A description of current research challenges related to sales and operations planning;
* The design of a generic predictive methodology to optimize sales and operations planning: This developed methodology combines demand scenarios, a statistical analysis of demand, forecasting techniques, random number generation, and system dynamics;
* The development of a simulation model that applies system dynamics to evaluate the impact of the developed predictive approach. Managerial conclusions:
* The description of current practical challenges related to sales and operations planning;
* A predictive approach to identify planning challenges, prepare solutions, and implement these factors accordingly was proven to be key for increasing the competitiveness of organizations; \begin{table}
\begin{tabular}{c l c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Key Performance Indicator (KPI)**} & **Scenario 1—** & **Scenario 2—** & **Scenario 3—** \\  & & **Probable** & **Feasible** & **Non-Probable** \\ \hline
1 & Cumulated potential demand (units) & Middle & Low & High \\
2 & Cumulated demand (units) & Middle & Low & High \\
3 & Cumulated volume loss (units) & Low & Middle & High \\
4 & Cumulated quantity delivered (units) & Middle & Low & High \\
5 & Average quantity delivered on-time per week (\%) & Middle & Low & High \\
6 & Average WIP stock (units) & Middle & Low & High \\
7 & Average capacity utilization of the production plant (\%) & Low & High & Middle \\
8 & Average customer order lead time (days) & Low & Middle & High \\
9 & Average mean absolute deviation (MAD) per week (units) & Low & Middle & High \\
10 & Average order backlog (units) & Low & Middle & High \\
11 & Cumulated operational savings (Mill. Euros) & Middle & Low & High \\
12 & Cumulated investment value (M euros) & Middle & Low & Middle \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison of simulation results for the predictive S&OP models between scenarios and factors. * This predictive model can serve as a fundamental tool for managers, especially for those facing uncertain situations with different potential demand scenarios and future events. Empirical conclusions: To prove the effectiveness of the developed methodology, a statistical treatment of potential scenarios and a simulation of the supply chain of an automotive producer were employed.