Demand forecasting and inventory optimisation for food producers and retailers

Our food system is caught in a destructive cycle. Modern agriculture feeds billions of people, but it's systematically destroying the very resources it depends on — soil, water, forests, and climate stability.

Livestock farming alone uses nearly 80% of agricultural land while producing just 18% of our calories. Industrial crop production relies heavily on fossil fuel-derived fertilizers that pollute waterways and strip soil of its natural fertility. Meanwhile, we're clearing forests at an alarming rate to create more farmland, even as we waste a third of all food produced.

This isn't sustainable. We need technologies that can maintain food security while regenerating the land, reducing emissions, and working within planetary boundaries.

Globally, we waste about 1.3 billion tons of food every year — enough to feed 3 billion people. In developing countries, most loss happens during harvest, storage, and transport due to poor infrastructure. In wealthy countries, most waste occurs at retail and consumer levels.

This waste represents a massive misuse of the land, water, energy, and labor that went into producing that food. Food waste in landfills also produces methane, making it a significant source of greenhouse gas emissions. Reducing food waste is one of the most cost-effective ways to improve food security and reduce environmental impact.

AI systems that predict demand more accurately and optimize inventory levels to reduce waste while avoiding stockouts. These can reduce food waste by 20-50% in retail and food service operations.

The technology combines sales data, weather forecasts, local events, and other factors to predict demand for specific products. Machine learning algorithms continuously improve predictions and can automatically adjust ordering, production schedules, and inventory levels to minimize waste.