Fruit cooperative · South Tyrol
Harvest forecasting and storage optimisation for fruit cooperatives
The starting situation
Two cooperating cooperatives process more than 100,000 tonnes of fruit per year. Variety-changeover times cost productive hours, one site is overfilled while others are underused, and packaging material is purchased separately by each cooperative.
How we solve it
We combine time-series forecasting for incoming harvest volumes with an optimisation model (MILP / CP) for storage and sorting allocation. A computer-vision pilot on one sorting line adds automatic quality classification. Integration runs through existing ERP and WMS interfaces — no data copy into a third-party environment.
What you get
Losses from variety changeovers fall, storage utilisation evens out, and bundled packaging purchases become negotiable based on data. Spoilage and inventory become visible before they turn into problems — the pilot shows possible six-figure annual savings in the first season.
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Why AI projects work →This use case is based on a real client project. Sector and region are named, the company itself remains anonymous.