## 6 AOG Downtime Reduction and Implementation Results
### SmartLynx Inventory Management control  
In aviation, air operators commonly aim for a 90% service level (Z = 1.28) as it provides a practical balance between maintaining sufficient spare parts and controlling inventory costs. This threshold minimizes the risk of AOG incidents while avoiding excessive spending. For critical components where downtime is highly disruptive, some air operators may adopt a more conservative 95% service level (Z \(\approx\) 1.65) to prioritize reliability over cost. In specialized cases, such as for high-value or hard-to-source parts in remote locations, service levels of 98-99% (Z \(\geq\) 2.05) are used to prevent shortages and ensure uninterrupted operations. For airlines, balancing stock availability with cost control is a key challenge, particularly for high-value and frequently used parts. Using data from 2020 to 2023, SmartLynxexamined how lead time, demand variability, safety stock, and reorder points interplay especially for high-value and frequently moved items. The study also leveraged Holt-Winters forecasting and MTBF metrics to fine-tune spare part strategies. Among 502 data points, the authors calculated safety stock as a buffer against unexpected demand or supply delays, maintaining a 90% service level (Z = 1.28). This precaution will prove vital for highly variable parts, which could otherwise lead to AOG incidents if left unmanaged. The Z-score remains constant for a given service level, but with a higher MTBF, the total safety stock needed may decrease as per the following formula:  
\[\text{Safety Stock}=z\times\sigma d\times\sqrt{LT}\]  
Overall, SmartLynx's approach, which is anchored by a 90% service level, would help minimize the risk of stockouts while avoiding excessive holding costs. This demonstrates how air operators can adapt threshold standards to maintain operational efficiency and cost-effectiveness in spare parts management. A frequency chart (Figure 24) further explores the relationship between lead time and demand variability. The analysis shows that 40% of the data involves moderate lead times (5-10 days) with demand variability (6-9), while less frequent combinations, representing 15-20% of the data, are linked to either very short lead times (<2 days) or high variability (>10). Longer lead times often correlate with higher variability, increasing the need for robust safety stock. Conversely, shorter lead times with lower variability suggest more predictable conditions, offering opportunities to streamline inventory and reduce costs. Approximately 30% of the data reflects stable demand with moderate lead times (3-5 days), while 10% involves high variability and long lead times, highlighting areas for improved forecasting and supply chain adjustments. Building on the frequency chart of demand standard deviation by lead time, the scatter plot (Figure 25) highlights the relationship between safety stock (S) and reorder point (R), offering insights into inventory optimization. The plot (Figure 25) shows a strong positive correlation, where higher safety stock levels align with increased reorder points, reflecting the need for larger buffers to manage demand variability and longer lead times. The average safety stock is approximately eight units, while the average reorder point is around 200. Clusters in the lower-left quadrant represent predictable parts with low variability  
Figure 24: Frequencies of demand std dev by lead time. and minimal safety needs. In contrast, points in the upper-right quadrant (safety stock over 10, reorder points above 300) indicate critical or high-demand items requiring more potent inventory strategies. A Z-score of 1.28, corresponding to a 90% service level, helps balance inventory levels to minimize stockouts while maintaining efficiency. Factoring in a Mean Time Between Failures (MTBF) of 1245.138 h, the Z-score adjusts dynamically to account for equipment reliability. Lower MTBF values, indicating higher failure rates, necessitate higher safety stock to buffer variability.