AI Atlas — The Global AI Opportunity Atlas

Optimizing food-waste forecasting

Retailers and canteens over-produce perishables because demand forecasts ignore local events and weather.

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Problem

Bakeries and grocery perishables desks dump unsold stock nightly while also stocking out of popular items.

Current workflow

Managers use last-week averages plus gut feel, then adjust mid-day with markdowns.

Consequences

Food waste, lost margin, and unnecessary logistics emissions.

How AI might help

Demand models combining weather, local events, and promotions can set production and ordering quantities.

Limits & risks

Cold-start for new products; sudden disruptions still need human override.

Understocking essential staples; amplifying biased historical shortages.

Alternatives today

Static safety stock, end-of-day apps for surplus sales.

Safety stock overshoots; surplus apps treat symptoms after production decisions.

Evidence

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