As supply-chain volatility, longer lead times, and AOG risks continue to challenge the aviation aftermarket, inventory is evolving from a stock-control function into a strategic business tool. In this exclusive Q&A, Miguel Foncerrada, CEO of AIQuote.ai and LogisticsMRO.com, discusses how AI and real-time data are enabling airlines, MROs and parts suppliers to make faster, smarter inventory and procurement decisions. He also explores how organisations can adopt inventory intelligence incrementally improving availability, resilience and procurement efficiency without undertaking a complete digital transformation.
Q – How AI is reshaping forecasting, procurement and supply-chain resilience in aviation MRO
Ans – As supply-chain disruptions, longer lead times and Aircraft on Ground (AOG) risks continue to challenge the aviation aftermarket, inventory is becoming more than just a stock-control function, it is turning into a strategic decision-making tool. Across the MRO ecosystem, operators are increasingly looking beyond traditional inventory management towards data-driven intelligence that combines operational insights, predictive analytics and AI-powered procurement to improve availability while controlling costs.
In this exclusive conversation with MRO Business Today, Miguel Foncerrada, CEO of AIQuote.ai and LogisticsMRO.com, explains why the future of inventory lies in smarter decision-making rather than bigger stockpiles. He shares his views on AI-powered demand forecasting, real-time market intelligence, procurement automation and the practical steps airlines, MROs and parts suppliers can take to build more intelligent inventory strategies without undertaking a complete digital overhaul.
Q – The industry is talking about “Inventory Intelligence” rather than simply inventory management. How do you define this shift, and why is it becoming critical for today’s MRO ecosystem?
Ans – Intelligence and management are completely different things (some may say mutually exclusive). Inventory intelligence helps us decide why a part is needed, when we are likely to need it and what happens if it is unavailable. Inventory management tells us what we have, where it is and what it costs.
The concept itself is not new. The industry has used inventory intelligence for decades, beginning with initial lay-in and resupply tools such as TFD Group’s VMetric, and after accumulating real-life history moving toward usage-based planning using historical data, turnaround times, fill rates and service levels. What has changed is our ability to process and optimize all that information with AI.
That matters in aviation because inventory is not static. Demand changes with fleet activity, maintenance programs, component reliability, regulatory requirements and market availability. A part may show very little historical demand but still be mission-critical if not having it can ground an aircraft.
AI makes it easier to bring operational, technical and commercial data together. It helps companies separate genuinely slow-moving stock from strategically important inventory and adjust their plans as conditions change.
Q – AI-powered demand forecasting is gaining momentum across aviation. What are the biggest limitations of traditional forecasting models, and how can AI help MROs predict parts demand more accurately?
Ans – Traditional forecasting usually follows one of two paths. The first relies on historical averages and assumes future demand will look like past demand. That is a major limitation in aviation, where demand is often intermittent, part populations can be small and one maintenance event can change consumption significantly.
The second is design-based marginal analysis, which is heavily dependent on how accurately the expected life of a component was forecast in the first place.
Both approaches become extremely complex once you begin adding all the variables that affect demand: flight hours and cycles, fleet age, scheduled checks, reliability trends, removal rates, location, seasonality, lead times, superseded part numbers and many others. You then have to balance those results against mission criticality, inventory cost and the available budget.
AI is much better at finding relationships across all those variables. It can update forecasts as new operational and transactional data comes in, and it can provide a range of probabilities instead of one fixed demand number.
That said, neither traditional models nor AI will produce good results without clean data, industry knowledge and engineering expertise. The best approach combines predictive technology with maintenance experience and human judgment—especially for safety-critical parts and low-frequency events.
Q – Airlines and MRO providers continue to face supply-chain volatility, AOG events and longer lead times for critical components. How can smarter inventory planning improve resilience without significantly increasing inventory costs?
Ans – Resilience is the ability to bounce back, recover, adapt after setbacks. That is exactly what we want to avoid. The goal is not buying more of everything. It means identifying the critical parts you can keep on-hand within the budget the supports the mission.
Companies need to look beyond unit cost and historical usage. They must also consider operational criticality, sourcing risk, lead-time variability, repair turnaround time, interchangeability and the financial impact of an AOG event. That allows them to protect a relatively small group of high-risk parts while reducing unnecessary inventory.
Better planning can also show when inventory should be moved between locations, when pooling makes sense, where repairable assets are getting held up and when material should be purchased ahead of a likely demand increase. Often, better visibility and faster decisions improve resilience more than simply adding stock.
The major change AI brings is the ability to automate much of the procurement process. Finding parts, sending RFQs, receiving and evaluating quotes, and issuing a purchase order can now be automated. If the supplier’s sales process is automated as well, a transaction that once took days or weeks can potentially be completed in minutes.
The objective is not just to minimize inventory. It is to optimize the total cost of having a part available when it is needed, including capital, storage, expediting, lost aircraft utilization and AOG exposure. Speed is now part of that calculation.
Q – At AIQuote.ai, you’re working at the intersection of data and aviation logistics. What role do real-time market intelligence and predictive analytics play in helping operators make faster and better procurement decisions?
Ans – Procurement teams often work with fragmented information. Inventory may be in one system, historical transactions in another, and supplier responses may be scattered across emails. By the time someone collects and compares everything manually, the market may already have changed.
Real-time intelligence gives buyers a clearer picture of availability, pricing, lead times, supplier performance and alternative sources. Predictive analytics adds another layer by estimating future demand, identifying possible shortages and showing the risk of waiting to buy.
At AIQuote, our goal is to automate both sides of the aviation supply chain: procurement and supply – purchasing and sales. On the purchasing side, the system can locate suppliers, send RFQs automatically, receive responses and capture information such as price, condition, certification and availability. It can then evaluate the offers and either recommend a decision or automatically create a purchase order.
On the sales side, it can extract RFQ details, connect them with internal data and, when licensed and integrated, market information, then calculate pricing and identify which opportunities need human attention.
The point is not to remove people from the process. It is to reduce transaction time from days—or even weeks—to minutes. People can spend less time gathering and entering information and more time negotiating, managing risk and building supplier relationships.
Q – As next-generation aircraft and engines introduce more complex maintenance requirements, how do you see digital inventory ecosystems evolving over the next five years? What capabilities will become essential?
Ans – I do not have a crystal ball, and my focus right now is on delivering the improvements AI has already made possible.
We are applying AI to problems the aviation industry has dealt with for many years and my team has been working on for 40 years. We are seeing revolutionary improvements in supply speed. Putting those solutions to work in real operating environments is already a full-time job, so I will leave the more futuristic predictions to others.
For me, the priority is straightforward: take what is possible today, make it reliable and use it to solve real problems for operators, MROs and suppliers.
Q – Looking ahead, what advice would you give airlines, independent MROs and parts suppliers that want to build a more intelligent, data-driven inventory strategy without undertaking a complete digital overhaul?
Ans – Start with one measurable problem rather than launching a company-wide technology project. It might be excess inventory, recurring shortages, slow RFQ responses, poor repair turnaround visibility or high procurement costs.
Then add an intelligence layer that works with the systems you already have. AI can automate procurement not only with sophisticated companies that offer APIs, but also with smaller suppliers that still rely mainly on email. It can read those exchanges, capture the relevant information and bring it into the buyer’s existing workflow.
The most successful companies will introduce these solutions piece by piece. An incremental improvement that solves a problem today is often worth more than a complete system redesign that may (or may not) deliver results for another two years.
