Helping a Global Packaging Leader Deliver Faster, Smarter, and More Reliably with Intelligent Forecasting

But forecasting demand at this magnitude had become increasingly difficult. Shipment volumes fluctuated constantly across regions, product categories, and seasons. Existing forecasting systems struggled to accurately predict demand, making it harder for planners to optimize routes, allocate vehicles, and manage delivery schedules. Machine learning models existed, but developing, deploying, and maintaining them required significant manual effort. Teams operated in silos, with limited visibility into model performance or consistency. This created operational friction across the delivery chain. Route planning became reactive. Resource allocation lacked precision. Delivery timelines were harder to optimize. And for frontline teams and customers alike, unpredictability became part of the experience. To continue delivering at scale while improving speed and reliability, the organization needed to transform forecasting into a real-time, intelligent capability embedded across operations.