How To Leverage Forecasting to Make the Aviation Supply Chain More Strategic and Less Reactionary

It may sound simple in theory to forecast parts requirements for aircraft — match the demand forecast regularly against market availability to adjust inventory planning. However, theory and practical execution seldom are the same. 

Due to lack of visibility and real-time information, the industry is working with the forecasts made based on historic purchasing data. Much of this is due to unplanned customer demand and as such these forecasts lead to planning anomalies and organizations stocking up on costly surplus inventory to overcompensate. 

So, how can these problems be alleviated? Let’s dive in!

Data Models + AI = Informed Decisions for Forecasting

MRO and airlines are currently experiencing massive lead times when it comes to procuring needed aircraft parts. This means that forecasting and planning are now more important than ever. 

By connecting the forecast with real-time market availability, you are able to adjust the planning and procurement to match the real needs of day-to-day operations. At the crux of this is AI. AI ensures that routine parts are delivered on time and at a good price point by running advanced algorithms in the background, while also providing solutions for complex situations that arise. 

“When coupling AI with appropriate data models, powerful forecasting features can be built that enable airlines and MROs to see the trends of which parts are in the market are becoming scarce, or have longer lead times, and pre-stock the parts before parts availability (or lack thereof) can impact operations.”

This technology can help airlines and maintenance organizations to monitor lead times and manage the flow of parts and components through the supply chain. By using real-time data and analytics, you can identify and mitigate potential delays, and prioritize the delivery of critical components.

When coupling AI with appropriate data models, powerful forecasting features can be built that enable airlines and MROs to see the trends of which parts are in the market are becoming scarce, or have longer lead times, and pre-stock the parts before parts availability (or lack thereof) can impact operations.

Within these models, a multitude of data points are ingested to build better forecasts (not perfect), including historical consumption figures and current market metrics such as parts availability and costs. This way, airlines can implement or improve MRO inventory planning and stay ahead of the curve. 

With forecasting analysis, you can find issues caused by the supply chain disruption, identity bottlenecks and improve planning (e.g. how quantity/prices fluctuate, where your parts are in the supply chain, what parts to stock in the warehouse, etc. to avoid future shortages).

The Future of Forecasting

The next evolution is moving into demand planning and simulations. At this stage, AI will analyze the structured consumption data from airlines and MROs giving them better than current recommendations on the parts to buy, and feeding the forecast to the suppliers so that they can plan out the production and stocking of the parts. 

Although it will be challenging to get it right, we foresee AI helping lead the collaborative forecasting efforts between demand and supply centers to ensure alignment on future part availability.  

Suppliers get a better understanding of the needs of buyers and get connected to more potential sales, while buyers get additional information on the location and availability of more parts from more suppliers. 

This allows both buyers and suppliers to make smarter operational decisions. And not only is this information available for the present situation and problems, but thanks to forecasting analysis, it’s possible to plan ahead of any potential parts shortages in our ever changing aviation landscape. By applying data to create forecasting models, material purchasers can become more strategic and less reactive.