Case Study: Global Candy Maker achieves real-time demand sensing and 23% forecast accuracy improvement with e2open Demand Sensing

Candymaker Leverages Demand Sensing AI to Resolve Volatile Demand

A century‑old global candy maker selling in more than 180 countries with over 34,000 employees found its demand planning strained by manual processes, limited forecast segmentation, and an inability to detect seasonal and short‑term demand shifts in time. This left planners overworked and the business at risk of stockouts, excess inventory, and margin pressure as demand became more volatile.

The company deployed e2open Demand Planning and Demand Sensing, using AI/ML and point‑of‑sale and external data to produce weekly statistical forecasts and automated daily demand sensing across North America, Asia Pacific, and Europe. The solution improved item‑location forecast accuracy by over 23%, automated low‑touch short‑term planning, boosted planner productivity, standardized processes globally, and enhanced replenishment and safety‑stock optimization as the cornerstone of its planning transformation.