## 4 Development and Simulation of a Predictive S&OP Methodology
### Simulation Case Study
#### 4.3.2 Assumptions and Restrictions  
The following restrictions were defined, and several assumptions were made. These restrictions and assumptions are the same for both models, unless otherwise specified:  
* Time restrictions: As this model considers from long-term to short-term horizons, four working years are simulated to evaluate influences in the short, medium, and long term. Assuming 250 working days per year, 1000 time periods are simulated. * The existing product is in a mature stage of its lifecycle, with stable demand of 33 units per day in the first working days of the simulation. * Production capacity has a nominal capacity of 36 units per day in three shifts at the beginning of the simulation for the manufacturer process, and 40 units per day for supplier and distribution. As a consequence, the model at initial time is able to supply, produce, and distribute based on the stable demand of 33 units per day, given a stable model at the beginning:
* During the simulation, two investment options for the manufacturer shops are considered: an increase of 9 units per day or an increase of 18 units per day. * Two other investment options are also analyzed. These include capacity increases of 20 units per day for the supplier and distributor. * Customer demand is read from Excel files. * Volume loss depends on the customer demand level and the customer order lead time. * Random numbers for future demand are read from Excel files. \begin{table}
\begin{tabular}{c c c c c} \hline
**No.** & **Area** & **Classical S\&OP Model** & **Predictive S\&OP Model** \\ \hline
1 & Demand scenarios & \(\bullet\) & No treatment. & & \(\bullet\) & Statistical treatment for the identification of demand patterns, expected demand changes, and generation of random numbers. \\  & & & & & \(\bullet\) & Use of statistical software and datasheets in Excel. \\ \hline
2 & Forecasting techniques & \(\bullet\) & SMA, CMA, linear regression. & \(\bullet\) & Based on random number generation. \\  & & \(\bullet\) & Selection based on historical data analysis for demand pattern identification. & & \(\bullet\) & Adjustment of the random number generation based on the forecast error between planned and current demand data, as shown in Figure 5. \\ \hline
3 & Sales plan & \(\bullet\) & Based on the forecast for the planning horizons. & \(\bullet\) & Generation of an original demand plan based on statistical treatment. \\  & & & & \(\bullet\) & Adjustment of the original demand plan based on a planned/actual comparison for the planning horizons. \\ \hline
4 & Procurement, production, distribution and personal plans & \(\bullet\) & Based on the classical S\&OP model sales plan, the subsequent plans are derived. & \(\bullet\) & Based on the predictive S\&OP model sales plan, the subsequent plans are derived. \\ \hline
5 & Investment planning & \(\bullet\) & Decisions and limits for initiating a measure are not defined. As a result, the implementation time is longer. & \(\bullet\) & Investment assessed based on the control limits derived from the root cause analysis and preparation of solutions. As a result, the implementation time is shorter. \\ \hline \end{tabular}
\end{table} 
Table 7: Simulation logic differences between models (own elaboration). * The simulation model considers sales losses starting from a customer order lead time greater than 60 days. * The bodywork shop is assumed to need one unit of the supplier and one unit of press shop 1 to produce the body of the car. * The order backlog is the same for both models at the beginning of the simulation. * A car is a finished product after it initiates the distribution following the final inspection shop. * The warehouses have no stock capacity limitations. It is assumed that outsourcing warehouses for stocks can be found nearby with extra-holding costs. * There is no transport limitation between the different production stages. It is assumed that additional third-party logistics can be found. * There is a limitation in production capacities and distribution capacities.