Case Study: Leading Korean MNC achieves more accurate sell-in forecasting with BRIDGEi2i Analytics Solutions

AI-powered sell-in forecasting for Consumer Electronics

Leading Korean MNC, a Korean manufacturer and marketer of home appliances with more than 100 subsidiaries and over 2,000 products, needed a more accurate supply chain forecast to support product availability within an 8-week lead time. Its existing planning-system algorithms were missing important demand patterns and causal relationships, and the company aimed for a 10% forecast accuracy value-add. BRIDGEi2i Analytics Solutions was brought in to help improve sell-in forecasting for consumer electronics using machine learning-based forecasting.

BRIDGEi2i Analytics Solutions deployed its Forecasting Engine, which includes 32 tuned forecasting algorithms, to match the right models to different product types and estimate the “headroom” for forecast improvement by segment. The solution also used BRIDGEi2i’s Trust Index to help planners build confidence in the ML forecasts. The result was a more targeted forecasting approach designed to deliver faster time to value and improve decision quality, with the project aligned to the client’s 10% accuracy value-add target.