5 Ways AI Is Transforming Galley Planning

Every flight that departs with too many meals, a misplaced trolley, or a crew scrambling to find a special request represents a failure that started on the ground. 

Galley planning — the unglamorous process of deciding what goes on the aircraft, where it goes, and how it supports service — has been held together for decades by spreadsheets, loading guides, and institutional knowledge. That was fine when onboard service was standardized. It is a lot less fine now, when passenger no-shows fluctuate, special meal requests have proliferated, pre-orders add flight-specific complexity, and catering is increasingly tied to retail revenue. 

The gap between what galley planning currently is and what it needs to be is where AI comes in.

The following are 5 ways in which AI can, and already is, having a transformative impact on airline catering operations.

1. Smarter demand forecasting: loading what you actually need

The traditional approach to provisioning is essentially defensive: load for full capacity and accept the waste. Forward-thinking airlines are beginning to use machine learning to embrace dynamic provisioning — loading only what's needed based on real-time consumption analytics. KLM has gone further still: its TRAYS system uses AI to predict how many booked passengers will actually board, and even predicts which passengers won't make connecting flights, so those meals are never loaded in the first place. The airline reports waste reductions of up to 63% as a result. 

2. Automated layout logic: eliminating the manual mapping problem

Once you know what to load, you still have to figure out where everything goes. On complex routes with multiple cabin classes, aircraft types, and service concepts, manually mapping carts, containers, and trolleys to galley compartments is slow, error-prone, and difficult to standardize across stations. AI-powered platforms can automatically apply preset business rules. They can integrate machine learning to produce accurate supply plans for all provisioned items and recommend the most efficient suppliers, ordering times, and distribution methods, while synchronizing inventory and equipment data across hubs and flights in real time. The result is fewer packing errors, more consistent execution, and less firefighting on departure day.

3. Real-time coordination: turning a static document into a live tool

Even a well-constructed galley plan can fall apart when things change late, as they often do. Updated passenger counts, last-minute meal substitutions, service adjustments: when airlines, caterers, and crew are working from static documents, clean communication is almost impossible. Modern platforms address this by making the galley plan a live operational layer rather than a PDF that slowly diverges from reality. Cabin crew get real-time, mobile-ready access to passenger preferences, service notes, and loadout summaries, eliminating paper manifests and reducing confusion during service. The plan stays accurate from briefing room to cruise altitude.

4. Consumption analytics: closing the feedback loop

Until recently, what happened in the galley after the doors closed was essentially a black hole. Airbus's AI-enabled Food Scanner addresses this directly: a downward-looking camera identifies what's on the meal tray as crew pull it from the trolley, then captures what remains when it's returned — providing data on what was served, what was eaten, and what was thrown away. Lufthansa has taken a similar approach: its Tray Tracker system scans returned meal trays to see precisely what passengers ate and what they didn't, feeding that information back into future menu planning to fine-tune portion sizes and offerings by route, cabin class, and passenger segment. 

An AI collaboration between Air New Zealand and LSG that photographed in-flight meals on their return from the aircraft gave the airline valuable insights on what worked or didn’t work with its in-flight menu, including that “customers weren’t all that enamoured with the blue cheese and beetroot hummus,” according to Air New Zealand CEO Greg Foran. AI here delivers a double win – giving airlines the opportunity to reduce food waste and improve passenger satisfaction. 

5. Buy-on-board optimisation: making the galley a revenue engine

Galley planning has traditionally been about cost control: load the right amount, avoid waste, avoid shortages. AI is beginning to flip the frame toward revenue generation. Platforms like IFCS’s GalleyX can analyse buy-on-board performance by city-pairing and intelligently adjust loading plans for each flight to maximise in-flight retail revenue – meaning the galley plan is no longer just a logistics document but a commercial tool. 

A strategic issue, not just a technical one

The thread running through all five is the same: galley planning is becoming a real-time decision layer rather than a back-office planning exercise. 

For in-flight catering executives, that makes it more than a technical process. It’s a strategic efficiency issue and a competitive advantage. Airlines that treat it as such will load smarter, waste less, serve better, and ultimately make more money per flight. Those still working from static load guides are carrying operational drag that they may not even be measuring.