## 7 Sensitivity Analysis and Numerical Results
### Comparison of Numerical Results for Instance 1 and Instance 2  
A comparative review of results for Instances 1 and 2 in the situation of deterministic demand is shown in Table 4. The value of the objective function \(J^{1}\) is decreased for 14,384 units, i.e., to the amount of 3021 units for the LFL ordering policy. The average level of inventories is increased by 14,384 units, which is a consequence of the decreased number of launched and received orders. The number of planned and realized deliveries is decreased by four, which significantly impacts delivery costs, but the average order \begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Instance 2:** & **Case 1:** & **Case 2:** \\
**LFL Ordering Policy** & **Deterministic Demand** & **Stochastic Demand** \\ \hline
1 & Lead time (month) & 5 & 5 \\
2 & Planned average level of stock (unit) & 63,000.00 & 63,000.00 \\
3 & Security stock (unit) & 25,200.00 & 25,200.00 \\
4 & Max level of stock T = 24 months (unit) & 94,600.00 & 94,599.00 \\
5 & Average level of stock T = 24 months (unit) & 59,979.00 & 59,967.00 \\
6 & Total number of stock cuts & 5 & 5 \\
7 & Total number of launched orders & 10 & 10 \\
8 & Total number of received orders & 7 & 7 \\
9 & max (\(J^{1}\)) = (2)–(5) & 3021.00 & 3033.00 \\
10 & min (\(J^{2}\)) = (6) & 5 & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Numerical results of Case 1 and Case 2 for Instance 2. Figure 10: Elements of the spreadsheet model for Case 1: LFL policy with deterministic demand. quantity is also increased from 32,000 units (Figure 6) to 48,338 units (Figure 10). The decreased number of deliveries drives the increase of the average inventory level. Except for the first five months, the criterion function \(\hat{J}^{2}\) is zero, i.e., without stock-out situations. However, if we compare the numerical results of Case 2 for Instance 1 and Case 2 for Instance 2 (Table 5), the number of orders and consequently delivery costs are decreased. Therefore, based on previously declared assumptions of this control model, taking into account the defined objective functions (15) and (16), the performed numerical experiments show that the FOQ gives better results than the LFL policy. The FOQ ordering policy will decrease the average amount of inventories and increase the stability in the supplier production process, i.e., the production series will be based on FOQ quantity. Nevertheless, considering the delivery costs, which are not assumptions of this model, the LFL policy could obtain better results than the FOQ policy due to the impact of the decreased number of deliveries. Moreover, for the LFL ordering policy (Table 5), the total number of launched and received orders is decreased for four orders. Bearing in mind that the average lead time of delivery is five months, the waiting time for deliveries is decreased by 20 months, and the risk of undelivered orders is much lower. \begin{table}
\begin{tabular}{c c c c c} \hline  & & **Instance 1: FOQ** & **Instance 2:** & \\  & & **Ordering Policy** & **LFL Ordering Policy** & \(\Delta\) \\
**No.** & **Parameters** & **Case 1:** & **Case 1:** & **Deterministic** & **(Instance 1–Instance 2)** \\  & & **Demand** & **Demand** & \\ \hline
1 & Lead time (month) & 5 & 5 & 0 \\
2 & Planned average level of stock (unit) & 63,000.00 & 63,000.00 & 0 \\
3 & Security stock (unit) & 25,200.00 & 25,200.00 & 0 \\
4 & Max level of stock T = 24 months (unit) & 70,400.00 & 94,600.00 & 24,200.00 \\
5 & Average level of stock T = 24 months (unit) & 45,595.00 & 59,979.00 & 14,384.00 \\
6 & Total number of stock outs & 5 & 5 & 0 \\
7 & Total number of launched orders & 14 & 10 & \(-\)4 \\
8 & Total number of received orders & 11 & 7 & \(-\)4 \\
9 & max (\(\hat{J}^{1}\)) = (2)–(5) & 17,405.00 & 3021.00 & \(-\)14,384.00 \\
10 & min (\(\hat{J}^{2}\)) = (6) & 5 & 5 & 0 \\ \hline \end{tabular}
\end{table} 
Table 4: Comparison of numerical results for Instance 1 and Instance 2 in Case 1.