# Development of a Maintenance and Spare Parts Distribution Model for Increasing Aircraft Efficiency
## 4 Simulation Model Results
### Results for the Case Study of an Aircraft Fleet  
Results of the simulation model in the selected scenarios are shown in Tables 2 and 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**No.** & **Key Indicator** & **Model PF-1** & **Model PF-2** & **Model PV-1** & **Model PV-2** \\ \hline
1 & \(\mathcal{O}\) spare parts demand (units) & 144.4 & 144.4 & 114.8 & 114.8 \\
2 & On-time delivery (OTD) (\%) & 88.2 & 85.0 & 100.0 & 90.3 \\
3 & Service level (\%) & 71.0 & 67.4 & 100.0 & 90.1 \\
4 & \(\mathcal{O}\) spare parts backlog (units) & 984 & 998 & 0 & 56 \\
5 & \(\mathcal{O}\) stock in network (units) & 3080 & 3112 & 6586 & 6679 \\
6 & \(\mathcal{O}\) stock in CW (units) & 26 & 1895 & 37 & 5086 \\
7 & \(\mathcal{O}\) stock in RW (units) & 3054 & 1217 & 6549 & 1593 \\
8 & \(\mathcal{O}\) production (units) & 154 & 154 & 129 & 129 \\
9 & \(\mathcal{O}\) WIP (Work-In-Progress) (units) & 3650 & 1795 & 7046 & 2053 \\
10 & \(\mathcal{O}\) aircraft availability (\%) & 60.0 & 59.8 & 83.7 & 82.6 \\
11 & \(\mathcal{O}\) aircraft on ground (\%) & 40.0 & 40.2 & 16.3 & 17.4 \\
12 & \(\mathcal{O}\) interval for planned & 706 & 706 & 1567 & 1567 \\ maintenance (operating hours) & \multirow{2}{*}{1974} & \multirow{2}{*}{1974} & \multirow{2}{*}{1974} & \multirow{2}{*}{1577} & \multirow{2}{*}{1577} \\
13 & \(\mathcal{O}\) interval for unplanned & & & & \\ maintenance (operating hours) & \multirow{2}{*}{1974} & \multirow{2}{*}{1974} & \multirow{2}{*}{15  
\end{tabular}
\end{table}
Table 2: Simulation results for the first demand scenario—high demand for spare parts.  
Figure 10: Model validation with the extreme test for the number of employees (own elaboration).  
As can be observed from Table 2, the best results in terms of aircraft availability were for the PV-1 model, the push strategy with variable planned maintenance intervals. However, this model presents 5000 units more in WIP than the PV-2 model, which has only 1.1% lower aircraft availability. Moreover, the PV-1 model has units stored mainly in the regional warehouses while the PV-2 model has approximately 75% of units in the central warehouse. To decide which of the configurations is is optimal in terms of cost and spare part type, other analyses must be realized. Another important aspect is the fact that the variable models, PV-1 and PV-2, had almost 25% lower consumptions of spare parts due to the dynamic changes of maintenance intervals.  
As can be observed from Table 3, the best results in terms of aircraft availability were for the PV-1 model, the push strategy with variable planned maintenance intervals. However, this model presents almost 4000 units more in WIP than the PV-2 model, which hasonly 2.9% lower aircraft availability. In addition, it can be observed how the models with variable planned maintenance intervals, PV-1 and PV-2, present better aircraft availability. Furthermore, the models with a pull distribution strategy, PF-2 and PV-2, present lower WIP units, service level, and OTD compared to the push strategy models. This is a result of the allocation of stock closer to maintenance locations in the push strategy models, PF-1 and PV-1, as the units are stored mainly in the regional warehouses. As for the first scenario, in order to be able to decide which of the configurations is the best one in terms of cost and spare part type, other analyses must be realized. Another important aspect is the fact that the variable models, PV-1 and PV-2, had almost 25% lower consumptions and approximately 13% lower production of spare parts due to the dynamic changes of maintenance intervals.