Using Historical Data to Predict Aviation Supply Chain Disruptions

Every airline has been there—waiting for a critical part that’s stuck in transit or delayed by a supplier, with aircraft grounded and schedules upended. For aviation companies, these disruptions aren’t just an inconvenience: they impact revenue, customer trust, and operational efficiency.

Supply chain issues in aviation don’t always have simple solutions, but there is a smarter way to manage them. Leveraging historical data means you can anticipate supply chain disruptions well before they unfold. With the power to see trends, spot risks, and make proactive adjustments, predictive maintenance transforms supply chain management from a guessing game into a strategic advantage.

This article explores how data insights can help everyone involved in the aviation supply chain work toward a shared goal: keeping planes in the air and operations running smoothly, no matter the challenges ahead.

Aviation supply chain management

Logistics are exceptionally complex in the global aviation industry, with every step—from sourcing to delivery—crucial to keeping aircraft operational and on schedule.

Efficient supply chain management (SCM) in aviation requires integrating the key domains identified in leading models, such as Gartner’s supply chain framework: planning, sourcing, making, delivering, and returning.

Planning involves forecasting demand and aligning resources to ensure operational readiness, while sourcing emphasizes vendor selection, procurement strategies, and contract management to secure critical parts.

The “make” phase, though less prominent in aviation, includes maintenance, repair, and overhaul (MRO) activities, ensuring parts and components are ready for deployment. Delivery focuses on logistics optimization—streamlining transportation and ensuring timely delivery of parts and components to minimize downtime.

Finally, the return process includes the recycling, refurbishing, or disposal of unserviceable parts to maintain sustainability and compliance with environmental regulations.

In addition to these core areas, aviation SCM must address cross-cutting functions like inventory management, workforce planning, and real-time data analytics. When balancing these interconnected components, aviation companies can maintain schedule integrity, adapt to disruptions, and meet the industry’s stringent safety and compliance standards.

Unique supply chain challenges in aviation:

Common disruptors in aviation supply chains

Aviation supply chains face a complex landscape of potential setbacks. A minor delay can escalate, disrupting operations and impacting schedules. Here are a few common disruptors that historical data can help manage:

Beyond these, factors like international tariffs, shifting trade policies, material shortages, or labor strikes introduce additional layers of uncertainty. While some of these may not be predictable with historical data alone, they highlight the importance of a flexible, proactive approach to aviation supply chain management.

By analyzing historical trends, aviation companies can still anticipate many risks, helping them create buffer strategies for more resilient operations.

Using historical data to prevent supply chain disruptions

Historical data provides insights into recurring patterns and potential issues that affect supply chain functions. Through examining previous hiccups—such as supplier delays, fluctuations in part demand, or seasonal supply bottlenecks—aviation companies can uncover future risks.

With these insights, airlines and MRO providers can prepare for and address issues instead of waiting for them to impact operations.

Key applications of aviation data analytics:

Predictive analytics builds on historical data to identify supply chain risks

While historical data helps identify past issues, predictive analytics goes a step further by using that data to forecast potential disruptions before they happen.

To an extent, analysts can look at historical data to plan for future events. But predictive analytics applies advanced machine-learning (ML) algorithms to historical records, enabling more pinpoint accuracy so aviation companies can make proactive adjustments with greater confidence that these measures are justified.

Here’s how predictive analytics works in a supply chain context:

These actionable insights are making a meaningful difference for aviation businesses today.

Real-world examples of predictive insights in aviation supply chains

Here are a few real-world applications of how companies have used historical data to prevent aviation supply chain disruptions.

Delta Air Lines

With over 95% accuracy in predicting potential failures, Delta’s predictive maintenance program sets a new standard for reliability. Its predictive maintenance uses both historical data and a continual influx of real-time data from IoT sensors on critical components to maximize MRO efficiency.

Alaska Airlines

AI systems, like Alaska Airlines' Flyways, optimize flight routes on the go. Through analyzing weather, aircraft weight, planned paths, and other historical data, Flyways technology finds the most efficient course. In just six months, Flyways saved 480,000 gallons of fuel (for an estimated savings of around $3 million) and cut 4,600 tons of carbon emissions.

These examples show how predictive analytics, backed by historical data, is helping aviation companies run smoothly, cut costs, and keep their fleets ready to fly.

Strategic advantages of using historical data for aviation supply chains

For aviation companies, the power of historical data goes beyond identifying individual issues—it provides a foundation for a more resilient supply chain and loyal customer base.

Maximized fleet availability

Predictive insights minimize unexpected delays and shortages, keeping aircraft ready for scheduled flights and reducing costly downtime. Through ensuring parts are available when needed, airlines can maintain higher operational availability and meet passenger demand more reliably —crucial when 40% of air passengers in the U.S. have a connecting flight. That figure is expected to rise to 45% by 2030.

Airlines using historical data plus AI to keep planes operational are poised to capture more market share from airlines that rely on dated methods to predict (guess at) future supply chain performance.

Cost savings

Historical data, continually updated and expertly analyzed, means better equipment maintenance and flight optimization, which can reduce fuel costs. With fuel being a huge component of any carrier’s annual budget, even a 1% reduction means savings of several million dollars.

Optimized passenger loads

Effective supply chain management can help airlines consistently maintain high load factors, targeting 85% or better. Timely aircraft maintenance ensures all parts are available in good working order when needed, minimizing flight cancellations and keeping more aircraft operational. This allows for more consistent, reliable scheduling.

Delta Air Lines, as one noted example, reduced its maintenance-related flight cancellations from 5,600 in 2010 to a staggering 55 in 2018—just by using historical data to implement better predictive maintenance strategies. That’s a 99.1% reduction in flights canceled due to MRO issues.

While this success is remarkable, many aviation companies have yet to adopt similar strategies. For every airline taking proactive steps like Delta, two others continue to rely on outdated methods, leaving money on the table, hurting profitability, and risking their reputations. Embracing data-driven insights, airlines can stay ahead of the curve and reap the benefits of a more resilient, efficient supply chain.

Stronger, more reliable partnerships

Analyzing historical performance allows companies to prioritize vendors with proven quality and reliability, creating a stable supply chain. This stability builds trust—from air fleets relying on dependable providers to passengers and freight customers trusting airlines to deliver consistently.

Achieving supply chain resilience with ePlaneAI

In aviation, supply chain resilience is critical. ePlaneAI’s automated inventory management platform delivers real-time monitoring and predictive insights to keep operations running smoothly. Here’s how:

ePlaneAI uses historical data to see patterns and predict needs, and real-time data to tackle issues as they come. Together, they keep your supply chain steady and your aircraft in the air.

Final thoughts

In aviation, timing and reliability are everything. Predicting supply chain disruptions with data is a must, to stay relevant in a world increasingly powered by AI.

Through partnering with ePlaneAI, companies can turn historical data into proactive strategies—reducing downtime, lowering costs, and boosting fleet readiness. A predictive approach means staying resilient, efficient, and prepared for challenges.

Ready to move beyond reactive maintenance and last-minute fixes? Whether you manage fleets, supply parts, or oversee aviation logistics, ePlaneAI can help transform your historical data into a predictive powerhouse. Streamline operations, improve efficiency, and keep the entire supply chain running smoothly—no matter what’s ahead.