Daisy is an AI‑driven decision platform for food processors that automatically ingests ERP, market, weather and satellite data to generate real‑time demand forecasts and production schedules. By continuously learning from new inputs, it reduces forecasting error by up to 60% and speeds monthly planning cycles tenfold, while offering an intuitive dashboard for planners to review and adjust outputs.
Funding
Funding not disclosed
Founders
Product
Problem
Food processors rely on manual, spreadsheet‑based forecasting that stitches together fragmented ERP, market, weather, and supply data. This leads to high forecasting errors, slow planning cycles, and frequent internal debate, causing waste, over‑ or under‑production, and missed revenue opportunities.
Solution
Daisy provides an AI‑driven decision layer that automatically ingests a processor’s ERP data along with external drivers such as weather, market trends, and satellite imagery. Its predictive models continuously update forecasts in real time, reducing margin of error by up to 60% and accelerating monthly forecasting speed by tenfold. Planners retain final control, using an intuitive dashboard to review and adjust production schedules while the system handles data integration and model tuning. The platform can be deployed within two weeks and scales to support full sales‑and‑operations‑planning (S&OP) workflows, aligning supply, demand, and capacity decisions.
Target Audience
Primary customers are mid‑to‑large food processing companies that manage complex supply chains and operate on thin margins, as well as their supply chain planners and S&OP teams.
Features
- Automated ingestion of ERP, market, weather, and satellite data into a unified forecasting engine
- Advanced AI/ML models that continuously learn from new data to improve forecast accuracy
- Real‑time dashboard with visualizations for easy interpretation and manual decision overrides
- Rapid implementation cycle (integration and initial results within one month)
- Capability to extend from forecasting to end‑to‑end S&OP optimization, including inventory and capacity planning
- Cloud‑based architecture that updates forecasts without daily manual maintenance