Industry
Agriculture and Food Production
in yield-forecasting accuracy
in product waste per harvest cycle
in greenhouse capacity utilization
One of North America’s largest premium fresh-tomato growers needed to improve six-week yield forecasting across its year-round greenhouse operations. Manual planning relied heavily on individual expertise, resulting in inconsistent forecasts and limited use of available production data.
Altimetrik developed an AI-powered Data-as-a-Service platform that unified agricultural data, automated ingestion, and generated configurable yield forecasts. The solution improved forecast accuracy, reduced waste, and enabled faster, more informed production planning.
Yield forecasts were created primarily through manual processes and the experience of harvest planners. This approach overlooked important variables and did not effectively use the organization’s extensive agricultural data.
Forecast accuracy for snacking products was limited to 82%, affecting On-Time-In-Full performance and making it difficult to manage allocation, excess volume, and unexpected production variations within a six-week planning window.
Inaccurate forecasts created risks across harvesting, manufacturing, supply planning, and customer service. The client needed reliable predictions to align planting cycles with demand, reduce waste, optimize greenhouse capacity, and respond proactively to production changes.
Altimetrik created a Single Source of Truth by consolidating data from facilities and greenhouses. An automated ingestion pipeline moved the data into a self-service cloud platform, reducing manual effort and creating a reliable foundation for analytics.
Data scientists performed exploratory analysis to identify the variables most influential to yield. AI and machine learning models were then trained, deployed, and monitored at plant and greenhouse levels, with target accuracy of 95% at Week 1 and 90% at Week 6.
A user-friendly dashboard allowed planners to review forecasts, model scenarios using production assumptions and historical data, and receive alerts when simulations were completed or failed. Weekly performance reviews supported continuous model improvement.
Increased net-pound forecasting accuracy by 30% and reduced planning simulation time by 30%, enabling faster and more confident decisions.
Reduced product waste per harvest cycle by 20% and improved greenhouse capacity utilization by 20% through better alignment of planting, supply, and demand.
Improved operational efficiency by 20% and developer productivity by 30% through automated data ingestion and streamlined workflows.
Altimetrik combined data engineering, AI/ML, and experience design to turn fragmented agricultural data into actionable yield intelligence.
