Transforming inventory management for a global cosmetics brand.
A leading global cosmetic brand faced a critical challenge – inventory stagnation. Excess stock piled up in some locations while others experienced shortages, leading to lost sales, frustrated customers, and rising carrying costs. The root cause? A static demand and pricing model that couldn’t adapt to market dynamics. The brand needed a data-driven solution to optimize inventory allocation, improve stock turnover, and boost profitability.
By combining predictive analytics, dynamic pricing, and AI-driven optimization, Fuld created a smarter, more agile inventory management system tailored to the brand’s needs. The results were transformative.
After an 11-week change management program, the new system was trialed at five flagship stores across New York, Florida, California, Washington, and Illinois. Here’s what happened:
- Cleared Stagnant Inventory: Stores eliminated excess stock within 5–9 weeks, improving product rotation and sales velocity
- Reduced Carrying Costs: The 4-week average inventory value dropped by $1.8 million cumulatively across test stores
- Zero Stockouts: No stockouts occurred during the trial, ensuring consistent product availability and customer satisfaction
- Improved Profitability: The brand achieved a more agile and efficient inventory system, driving higher sales and reducing operational costs
This project didn’t just solve an inventory problem—it transformed the brand’s approach to supply chain management. By leveraging data and AI, we helped them build a future-ready system that adapts to market changes, maximizes profitability, and delivers exceptional customer experiences.
How We Did It
We implemented a comprehensive inventory optimization strategy powered by advanced analytics and AI. Here’s how we did it:
- Data Integration: Consolidated multi-source data—historical sales, inventory levels (in-store, ordered, in transit), pricing trends, and marketing calendars—into Azure databases for a unified view
- Demand Forecasting: Applied Bayesian and SARIMAX regression models to predict demand and price sensitivity for the next 13 and 52 weeks
- Dynamic Inventory Optimization: Leveraged Markov’s Decision Process to evaluate SKU-level inventory and implemented a dynamic order fulfillment algorithm to optimize lead times and costs.
- Real-Time Insights: Developed interactive Power BI dashboards to track inventory movement, sales trends, and performance metrics in real-time.
In a competitive industry like cosmetics, where trends change fast, and customer expectations are high, having the right products in the right places at the right time is critical. Our solution empowered the brand to stay ahead of demand, reduce waste, and focus on growth.