Optimizing operations through yield forecast automation with data SSOT and AI/ML
The client is one of the largest growers of premium fresh tomatoes in North America.
Their operations comprise over 1,000 acres of greenhouse facilities with year-round
production.
The yield forecast is one of the most important projects for the client that aims to provide
6 weeks yield forecast capabilities to the manufacturing and planning team by deploying
a Data-as-a-Service platform, using Artificial Intelligence, to manage, process and create
accurate yield forecasting based on sets of variable inputs. They also required this
intelligence to be made available on a configurable user front end to develop insights and
reporting.
Automation of yield forecasts would help them to improve accuracy, eliminate human error
and provide better client service. It would also help to manage allocation or excess
volume more efficiently within a 6 weeks time frame and avoid unexpected volume
variations.