Case Study: a leading global cosmetics manufacturer reduces SLOB risk with Mate Labs’ AI-driven forecasting

Leading Global Cosmetics Manufacturer - Customer Case Study

Mate Labs collaborated with a leading global cosmetics manufacturer that was struggling with significant financial losses, estimated at 1-2% of total revenue, due to its inability to accurately predict slow-moving or obsolete (SLOB) inventory. The client's challenge was two-fold: first, to predict which SKUs had a high propensity to become SLOB in the next 5-6 months, and second, to forecast the precise quantity of that future SLOB inventory. This issue was causing stockpile-ups, increasing supply chain costs, and reducing overall operational efficiency.

The vendor addressed this using its homegrown Auto ML tool, Mateverse. The platform processed historical sales data along with parameters like lead time and stock information to generate monthly SLOB propensity and quantity forecasts, which were further refined by factoring in events and promotions. As a result, Mate Labs successfully identified 85% of correct SLOB SKUs in the first month, protecting $3.15 million USD in business impact, and 71% in the second month, protecting $2.07 million. The overall accuracy for the SLOB quantity prediction was approximately 60%.