# Market-Oriented Procurement Planning Leading to a Higher Service Level and Cost Optimization
## 3 Design of the Conceptual and Simulation Models
### Demand Planning and Forecasting Theory  
The purpose of demand planning is to improve the decisions that affect demand accuracy [41]. The main task is to calculate the future demand, and this comprises the selection of a specific forecasting method and its parameters [42]. The typical demand patterns are stationary, seasonal, trend, and sporadic [43]. There are three methods in requirement planning: demand-driven or deterministic, stochastic, and heuristic [42]. Deterministic demand planning assumes that when defining the demand planning, there is knowledge regarding the future, and this cannot be claimed as given without contradiction [43]. As an alternative, the consumption-oriented demand planning can be used by applying stochastic methods using the known historic consumption [43]. Stochastic methods determine the expected demand with the support of statistical forecast processes, while heuristic methods define the demand forecast based on subjective estimations of the planner [44]. In the model developed, we applied stochastic methods for demand planning as they can be applied for all typologies of inventories, even for the supply of make-to-order producers until they have real customer orders. The most used are the stochastic requirements planning methods, and the three most named in the literature are linear regression, moving average, and first-order exponential smoothing [42]. The following demand forecasting methods in Table 2 and in detail in Appendix A are applied to the conceptual model and compared based on the forecast accuracy to determine which method will be used for the next planning period:  
The suitability of a forecasting method depends on the demand pattern [45]. Therefore, several models were selected in the model to select one matching demand and strategy. The comparison of the values calculated from the above-mentioned stochastic methods was performed with a randomly created future demand. This random data series as future demand was created based on a function in Vensim called Forecast. The forecasts of the different methods in the conceptual model were compared by means of the forecast error. There are different methods to measure and provide conclusions regarding the accuracy of the used forecast method [46]. The mean absolute deviation (MAD), the mean square error (MSE), and the mean absolute percentage error (MAPE) are methods in Appendix A that produce consistent results when comparing different forecasting methods [43]. As a result, the MAD method is considered in the conceptual model to assess the suitability of the previously mentioned demand forecasting techniques. The forecasting techniques are compared after every planning period within the simulation. Using the forecasting method selected after the comparison, demand planning is performed according to different planning horizons that, as mentioned before, are related to the different decisions they influence. Therefore, demand is aggregated in weeks for short-term planning and in months and in quarters of a year to obtain demand forecasts based on the control loop for three different planning horizons. In summary, in the demand planning area, the model calculates the variation coefficient to provide an indication of the variability of the demand [43]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Demand Forecasting Method** & **Formula** \\ \hline \(1\) & Cumulative moving average (CMA) & \(F_{(t+1)}=\frac{1}{n}\sum\limits_{t=1}^{n}\) item demand (t) \\ \hline \(2\) & Linear regression & \(F_{(t+1)}=a\ \times t_{trend}+b\). \\ \hline \(3\) & Exponential smoothing of the first order & \(F_{(t+1)}=\ \alpha\ \times D_{t}+(1-\alpha)\times F_{(t)}\) \\ \hline \(4\) & Exponential smoothing of the second order & \(F_{(t+1)}=\ L_{(t)}+\ T_{(t)}\) \\ \hline \(5\) & Exponential smoothing of the third order & \(F_{(t+1)}=(L_{(t)}+\ T_{(t)})\times S_{(t+1-s)}\) \\ \hline \(6\) & Croston method & \(F_{(t+1)}=\frac{F_{(t)}}{F_{(t)}}\) \\ \hline \hline \end{tabular}
\end{table}