Korean battery manufacturers have faced growing limitations in traditional demand forecasting approaches as order volatility from automotive OEMs continues to increase. Through an initial collaboration with Deepflow, OEM sales data and production schedules were integrated and trained as leading indicators, resulting in a 10% improvement in order forecasting accuracy compared to existing methods. The partnership continues as both sides further enhance model performance over time.

Material suppliers are also leveraging Deepflow to better understand ordering patterns from battery cell manufacturers, helping alleviate inventory imbalances in cathode and anode materials. By integrating raw material price forecasting into procurement timing decisions, they are further strengthening cost competitiveness.