## 4 Development and Simulation of a Predictive S&OP Methodology
### Simulation Case Study  
#### 4.3.1 Design of the Supply Chain Flow and Planning Characteristics for a Manufacturer  
The supply chain system consists of a process that begins with steel stamping and ends with the distribution of the manufactured cars to end-customers. The supplier, manufacturer production shops, and distribution within the supply chain flow are shown in Figure 14 and listed below. The capacity values refer to the nominal values for three working shifts at the beginning of the simulation model:  
* Supplier: A nominal capacity of 40 units per day. It delivers pressed pieces to the bodywork shop of the manufacturer;
* Press, bodywork, paint, pre-assembly or assembly 1, mechanical assembly shop or assembly 2, final assembly or assembly 3, and the final inspection shops: All of these shops have a nominal capacity of 36 units per day;
* Distributor: Has a nominal capacity of 40 units per day. Both models, the classical approach and the predictive S&OP model, present the same supply chain flow. Moreover, their planning characteristics are as follows:  
* Order backlog: At the beginning of the simulation, the model has a certain level of WIP units. In addition, the model presents ordered and undelivered car units at the beginning of the simulation. This value relates to 1320 car units ordered and waiting to be delivered to end-customers. * Release of orders: The initiation of orders is controlled before production at press shop 1. This shop controls the number of orders that enter the production process. After the initiation of the production process, the planning logic is a push-strategy until reaching the end-customer. The release of orders depends on the order backlog, the customer order lead time, the nominal capacity of press shop 1, and the demand forecast. This process depends on the same four parameters for both models; however, the values of the parameters vary between both. * Demand forecasting: Both models forecast future demands at different planning horizons. Both models forecast future demand values. However, the methods for \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Demand Pattern** & **Period** & **Parameters** & **Method or Formula for Demand Data Generation** \\ \hline
1 & \(S_{1}\), Stationary & 1–105 & Mean & Mean with random numbers between (\(-2\sigma\), \(2\sigma\)) \\
2 & \(S_{2}\), Cyclical 1 & 106–932 & ARIMA & ARIMA value + noise \\
3 & \(S_{3}\), Cyclical 2 & 933–1000 & ARIMA & ARIMA value + noise \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Demand patterns, periods, and methods for random number generation. Figure 14: Simulated production flow (own elaboration). determining these values vary. The predictive S&OP model uses the generated random numbers with the adjustment factor, while the classical approaches calculate the demand forecast based on formulas according to historical data. Demand planning: The same process for the aggregation of demand at different planning horizons. Long-term demand planning is calculated for one year, medium-term demand planning is performed for one month and three months, and short-term demand planning is obtained on a daily basis. Production planning: As mentioned in the order release section, the production planning strategy seeks to control the order release to ensure that the WIP units are sufficient to meet the promised customer order lead time and the promised weekly delivery goals. In this context, after production initiation, the logic involves a push-production strategy with intermediate stock between the production shops. As a result, the production output in each shop aims to be as large as possible. However, if demand is not high, the model ensures that the WIP units are not too numerous at the end of the supply chain process without a customer order. This model, therefore, seeks to avoid make-to-stock production while favoring make-to-order production--orders placed by the customer, and not internal or dealer orders.