AI takes on food waste

The Star

16-06-2025

You have your safety belt on.
About an hour into the flight, hunger kicks in. You reach for the inflight menu, and a pack of nasi lemak catches your eye. Maybe it's the sambal, or it's the idea of enjoying a familiar local favourite at 30,000 feet in the air. Either way, it sounds perfect. You make a request.
Then the flight attendant breaks your heart: 'Sorry, we've run out.'
Such a situation is not unfamiliar to most airline passengers. When it comes to inflight meal planning, Catherine Goh – the CEO behind Santan, official inflight caterer for AirAsia – explains that there are a lot of factors to consider. Teams behind inflight meal planning relied on experience, historical data and rough estimates to not only decide how much nasi lemak to prepare but also ­estimate how many passengers would likely choose vegetarian options or prefer to have coffee instead of tea.
'There was a lot of guesswork around passenger preferences, last-minute cancellations, and unexpected bookings. This approach often led to challenges such as overstocking – resulting in waste – or understocking, which left some passengers ­disappointed,' Goh says in an interview with LifestyleTech .
She adds that flight delays or changes also added more ­complexity to an already ­delicate balancing act.
'On top of that, we had to ­consider the stock required for every single flight, accounting for different routes, turnaround times, and return legs – all of which influence the catering load,' she says.
According to Goh, Santan is currently testing an AI-powered demand planning tool built on a robust datasheet consisting of historical flight-level sales and loading data – capturing both pre-Covid trends and recent post-Covid recovery behaviours. — SANTAN
With over 1,200 flights daily in five regions, Goh says ­managing inflight food waste is one of their biggest challenges.
According to cabin waste audits commissioned by the International Air Transport Association (IATA) published in May, 34% of waste generated on flights comes from untouched food and beverages. The sector is estimated to be incinerating resources worth US$6bil (RM24.40bil) annually.
To address this, airlines and catering providers are being urged to improve planning and logistics to reduce cabin waste and contribute to the Sustainable Development Goal of halving global food waste by 2030.
Goh concedes that forecasting demand based on static or generic data is simply not ­sustainable.
'That's what drove us to explore AI-powered demand planning. Unlike traditional methods, AI allows us to factor in a wide range of dynamic ­variables – such as passenger demographics, travel and ­booking patterns, historical food preferences by route, meal-time segments, and even cultural events like festivities or the ­fasting month,' she adds.
Connecting the dots with data
According to Goh, Santan is currently testing an AI-powered demand planning tool built on a robust datasheet consisting of historical flight-level sales and loading data – capturing both pre-Covid trends and recent post-Covid recovery behaviours.
'Specifically, it has been trained using over three years of historical data, enhanced with the latest six months of operational insights and up to 12 months of forward-looking pre-booking forecasts,' she says, adding that it gives the model more historical depth and real-time relevance.
Goh shares that there is a broad spectrum of variables to be analysed including passenger numbers, routes and seasonability trends, nationalities, meal time segments and flight departure times.
'For instance, it can identify how meal preferences shift not just by destination, but also based on time of day or passenger mix – insights that are nearly ­impossible to act on through ­traditional planning,' she adds.
By looking at the system's ­recommendations for menu mixes, Goh says it has led to the company being able to offer meals that better match their passengers' expectations during peak travel times. Even with changes like flight delays or ­cancellations or a spike in last-­minute bookings, Goh says the AI can quickly recalculate expected demand.
Airlines and hotels are increasingly turning to artificial intelligence to better predict meal consumption patterns. Could this be the key to tackling food waste? — Image by freepik
'It is designed with real-world operational flexibility. This agility allows our supply chain and cabin crew teams to make timely adjustments – whether it's ­modifying loading volumes or ensuring we reserve popular items that are likely to be in demand,' she adds.
Since the system was ­implemented, Goh says it has been 'showing promising results', with forecast accuracy improving to over 95%. This has led to a noticeable drop in both overstocking and understocking of inflight food items.
'Inflight food waste has dropped by 20% over the past year – a clear win for both ­efficiency and ­operational ­performance. We've also seen better alignment between ­forecasted and actual demand, enabling more informed ­decision-making across our ­supply chain,' she says.
The AI system was developed in-house using the airline's ­central data infrastructure. Goh says all data is encrypted and access is strictly governed through its group-level data ­governance framework to ensure compliance and ­protection across all ­touchpoints.
'Developing the tool ­internally has also allowed us to fine-tune the system closely to operational ­realities. It has already delivered encouraging outcomes in live ­environments, and we're now preparing to scale it across the fleet to unlock greater ­precision and efficiency in our meal planning processes,' she adds.
Dining with data
Turning data into actionable insights that could translate to better ways to manage food waste isn't new. Back in 2020, Etihad Airways announced that it was partnering with Singapore food tech startup Lumitics to trial the use of ­computer vision and machine learning to track ­uneaten ­economy class meals. The goal was to highlight food consumption and wastage ­patterns across the network.
With the integration of AI into broader systems, its ­capabilities have steadily advanced. A 2024 review ­published in the peer-­reviewed journal Food Chemistry: X ­highlights how AI-powered tools such as deep learning and advanced robotics can greatly enhance food safety, improve quality, and boost ­efficiency throughout the ­supply chain.
It highlighted the potential of AI to enhance food waste ­management through ­'predictive algorithms' that could help to ­minimise overproduction and spoilage.
In Malaysia, Hilton Hotels announced that it adopted Winnow's AI-powered solution to help reduce wastage during Ramadan. — Hilton Hotels
An example cited in the ­article is the system by Winnow Solutions, featuring smart scales and image ­recognition technology to help kitchens reduce waste by ­pointing towards the source and adjusting portion sizes.
In Malaysia, Hilton Hotels announced that it adopted Winnow's AI-powered solution to help reduce wastage during Ramadan – a period when hotel buffets are typically more extensive and prone to excess. The system generated reports providing details on the most wasted ingredients, waste ­patterns and guest demographics to help kitchen teams make precise adjustments during food preparation.
The company claimed that last year, the same AI-powered ­initiative led to a 64% ­reduction in food waste at two hotels in Kuala Lumpur and Selangor.
Other companies that have adopted AI to monitor food waste have also started sharing some interesting insights. Last year, Air New Zealand chief customer and sales officer Leanne Geraghty shared in an interview that AI was used to analyse 30,000 ­photos of food trays on flights coming from Los Angeles and Hong Kong.
She told news.com.au that ­findings revealed that ­passengers didn't like beetroot hummus as an entree and blue cheese. The next step is to take measures to remove unpopular ingredients and deliver products that customers want, adding the AI-driven insights are helpful to reduce food waste.
A few obstacles remain
Despite its promise, widespread adoption of AI to reduce waste in the food industry faces several hurdles. The same review published in the Food Chemistry: X journal cited ­implementation costs, data ­security concerns, and the ­complexity of integrating with legacy systems as major ­barriers.
There are also ethical ­concerns, including privacy and fairness in algorithmic decision-­making, that need to be ­carefully addressed. For AI to play a broader role in curbing food waste, clearer ­regulations, greater transparency, and more affordable solutions to enable smaller players are ­essential, according to the report.
Some companies are using computer vision to monitor leftover food, providing data that helps kitchens identify which ingredients to cut back on. — Image by freepik
Rolling out a new tech-driven system also takes more than just software – it requires people to trust that the technology will deliver real results.
Goh says change management was key to successful adoption.
'We conducted hands-on ­workshops with our demand planners, supply chain and ­operations teams to walk them through how the model works and why it's reliable,' she adds.
Visual dashboards were ­introduced to compare forecasts with actual outcomes over time.
'Seeing the model's accuracy in action helped build trust ­organically and empowered our teams to make data-driven ­decisions with greater ­confidence,' Goh says.
The CEO also believes there is more potential for the system beyond inflight meals, including ground-based food services and group-level catering operations. As the company continues to evolve, Goh expects technology to drive ongoing green initiatives.
'AI empowers us to make smarter, faster decisions that reduce waste and boost ­efficiency, which are key pillars of our environmental responsibility efforts. We're integrating predictive analytics with ­procurement and eco-friendly packaging choices to further lower our carbon footprint,' she says.