AI for Aerospace: Fewer AOGs. Leaner stocks.

Turn your fleet, maintenance, and procurement data into daily, risk-adjusted decisions.

Our AI and experts tell you what to buy, what to repair, where to stage, and when to move parts, so your aircraft stay flying with less capital tied up in spares.

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AI for Aerospace: Fewer AOGs. Leaner stocks

Lokad brings a new tool to the table, one that is both powerful and innovative. But on top of that, Lokad has shared with Air France Industries its expertise in inventory optimization and Supply Chain management, thus bringing not only a complimentary IT solution but also a real consulting expertise, which our teams can rely on.

Charles Segondat

Head of Inventory Management, Air France Industries

In video Read the case study

Lokad supports our decision-making with a structured, data-driven approach that helps us balance inventory levels, cost efficiency, and customer requirements. The platform provides the analytical clarity needed to navigate complex operational scenarios, while the collaboration with the Lokad team remains highly collaborative and solution-oriented. Together, we continuously refine our planning logic to stay aligned with both our operational reality and commercial objectives. This partnership has become a valuable component of how we plan and grow.

Clemens Schrettl

Head of Sales and Marketing, Spairliners

In video

When I arrived at MRO Holdings I was pleasantly surprised to find that Lokad was already a partner working on probabilistic demand forecasting. I don't know if I'm disclosing one of the secrets for success in this kind of business, but this is really the way for coping and embracing the volatility and the complexity.

Ricardo Alvarez Henao

Supply Chain Director, MRO Holdings

In video

Since switching to Lokad, we've seen undeniable improvements in our supply chain. Lokad's user-friendly dashboards provide clear visualizations, helping us quickly identify optimal decisions. This has simplified the decision-making process, enabling us to make decisions faster, and ensured our choices are more assertive.

Valdir Chiarioni

Purchasing Senior Manager, Azul

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Aerospace problems we fix

AOG risk: one missing rotable or consumable grounds an aircraft; MEL "no-go / go-if / go" rules make misses costly
Repair & Pool uncertainty: variable TATs, yields, and exchanges complicate "repair vs. buy" decisions
Lead-time volatility: OEMs, repair shops, customs, and logistics create long, lumpy delays
Excess & obsolescence: SBs/retrofits change demand; parts linger due to low scrap rates and resale discounts
Fragmented networks: line stations, hubs, warehouses, and partners need coordinated stocking and transfers.
Scheduling bottlenecks: conflicting hangar, crew, tooling, and parts constraints lead to expensive delays, missed slots, and idle assets.

Aerospace mechanics working on aircraft engine

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How we do it

Supply Chain Scientists (SCS)

A dedicated SCS team encodes your economic and business drivers (AOG penalties, MEL criticality, freight options, repair SLAs) and partners with you from kickoff to go-live and beyond for continuous improvement.
Decisions scored in money

Every feasible action (buy, transfer, repair, expedite) gets an expected financial score that balances uptime risk against holding, freight, MOQs, obsolescence, and AOG penalties. We pick the highest-return set every night for every PN.
Probabilistic forecasts

We model full demand and lead-time distributions (including intermittent usage and TAT variability) so plans reflect spike risk, not just "most likely" demand.
Network-wide optimization

On a daily basis, our algorithms solve where to hold stock and how much (by site and PN), factoring capacity, budget, and service constraints. This is done across the entire network (line stations, bases, and DCs).
Validation before automation

First, we run your current process and our new process simultaneously, quantify €/$ impact (AOG risk, total cost), so you can see the financial rewards. Then, we automate routine buys/transfers, while keeping human oversight in the loop whenever necessary.
No new hardware or software

Lokad layers on top of your ERP/MRO (SAP, AMOS, Ramco, Maximo, IFS). Data flows via flat files/APIs. Lokad's decisions are sent directly to your existing workflows, and every decision is white-boxed with a dashboard.

Project implementation

Air France & Lokad: Running a Large Scale Aviation Supply Chain

Jacques Dauvergne (Air France Director of Supply Chain) explains how Air France Industries leverages Lokad to optimize complex aviation supply chains, boosting reliability, responsiveness, and efficiency across its global operations.

“Lokad’s ability to deliver a customized solution in an efficient and effective way, with a short lead time, was impressive.”

Jacques Dauvergne

Air France Industries Director of Supply Chain

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Common questions answered

How fast will we see results?

A few weeks to prove initial value with a “dual-run” (current practice vs Lokad’s system). After that, it takes a few months to scale to full automation. We start by ranking deltas in money so you can cut AOG risk and inventory with confidence. Typical full “go live” is under 6 months.

Can Lokad handle repairables and pools?

Yes. We model repair queues, TAT distributions, yields, interchangeability (one-way/full), pool levels, and “repair vs. buy vs. borrow/cannibalize” economics.

Do you account for MEL criticality and AOG penalties?

Yes. Essentiality and AOG costs are a critical part of our algorithm and calculations, which is why our stocking decisions reflect your real financial constraints. We factor repair TAT distributions and pool levels to balance exchanges, holds, and buys.

Will planners still have control?

Absolutely. Recommendations are explainable, auditable, and planners can override if they feel it is necessary. Planners can set approval gates and access rights per action type/site.

Do we need a new ERP/EAM?

No. Lokad sits on top of your stack and feeds your ERP/MRO with approved decisions.

How is the solution priced?

A simple monthly subscription aligned to network size. No surprise costs. No long-term commitment.

The entire Smart Planning project team at Airbus Atlantic is profoundly pleased with the successful completion of the initial phase of our advanced planning initiative. Thanks to the unwavering commitment, rigorous approach, and high-performing collaboration with the teams we have received the green light for the next steps and are excited and optimistic about continuing this journey together

Julien Fournat

Project Manager Industrie 4.0 Airbus Atlantic

Aviation has been promised inventory optimization solutions since I started my career over three decades ago. However, every time, aviation proved too hard: too many parts, too many processes and too little volume. With Lokad, we have finally found a partner that can integrate the aviation expert knowledge of Revima's teams right into our systems. And its' AI, as 'augmented intelligence' for aviation, combines latest Artificial intelligence technology with Data Scientists screening the data.

Olivier Legrand

Revima Group President & CEO of Revima

The technical details

Unified forecast + optimized pipeline

We don’t stop at predictions. We combine probabilistic modeling with optimization so outputs are actual decisions (buy, repair, transfer, schedule) under real constraints and trade-offs.

Probabilistic modelling (distributions, not single points)

Instead of one “most likely” value, we estimate full probability distributions across outcomes. This approach is crucial for identifying and quantifying the financial risk associated with slow-moving parts, spiky demand, and uncertain lead times/TATs.

Decisions optimization under uncertainty

Every feasible action (buy, transfer, repair, expedite) gets an expected financial score that blends downtime risk, working capital, logistics, price breaks, MOQs/lot-multiples, shelf-life, etc. These economic drivers are explicit and auditable, not hidden inside a solver. We then pick the decisions that maximize financial return (or whatever criteria you wish to optimize). Stochastic optimization methods (e.g., Stochastic Discrete Descent) turn those distributions into ranked actions that respect MOQs, budgets, service targets, and network constraints.

Repairables & rotables done right

We model pools, repair queues, TAT distributions and yields. We can prioritize “repair vs. buy vs. cannibalize” to keep planes flying with minimal capital tied in spares.

Scheduling & resource allocation

Scheduling is more than your physical BOM (Bill of Materials). Effective scheduling must factor your entire bill of parts, tools, and skills required to execute a task (e.g., repair). This is the BOR (Bill of Resources) perspective that Lokad uses when optimizing decisions. At scale, we use Latent Optimization for the heavy combinatorial complexity in aerospace (balancing all the possible combinations of hangers, bays, crews, skills, tools, parts, etc.).

Lead-time & TAT decomposition

We model the entire delay chain (admin, procurement, transit, receiving, inspection/TAT) using full delay distributions instead of basic averages. This granularity allows us to identify where the serious financial risks lie and to optimize supply chain decisions to avoid financial losses (e.g., expedite a delivery that is likely to be late and cause an expensive delay).

Interchangeability, retrofits, BOMs

Rules like one-way/full interchangeability and SB/retrofit effects are natively featured in our programming logic. For scheduling problems, we optimize for the entire Bill of Resources (BOR) (parts, tool, and skills) required to complete a sequence of actions. This is a much more financial rewarding approach to problem-solving than trying to optimize a BOM (Bill of Materials) in isolation.

AI for Aerospace: Fewer AOGs. Leaner stocks.

Aerospace problems we fix

How we do it

Air France & Lokad: Running a Large Scale Aviation Supply Chain

Common questions answered

The technical details

White papers

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