Precision Demand Forecasting for Footwear and Apparel

Cases

I. Client Overview & Industry Context

The client is a leading global manufacturer of footwear and apparel. Operating within a highly competitive and trend-driven retail sector, the client manages a vast product catalog (SKUs) and complex seasonal demand cycles.

II. Business Challenge: Minimizing Stockouts and Excess Inventory

The traditional demand planning process relied heavily on historical sales averages and subjective expert judgment.1 This resulted in two significant and costly inventory issues:
  • Lost Revenue (Stockouts): Under-forecasting popular or trending items, leading to missed sales opportunities and customer dissatisfaction.
  • Wasted Capital (Excess Inventory): Over-forecasting non-moving or end-of-season styles, resulting in high warehousing costs and deep, margin-eroding markdowns.
The core objective was to replace the reliance on lagging indicators with a dynamic, data-driven predictive system capable of forecasting product demand with high granular accuracy (SKU, geography, and week).

III. Our Solution: Implementing an Advanced XGBoost Forecasting Engine

We developed a robust, scalable Demand Forecasting Engine centered on Extreme Gradient Boosting (XGBoost). XGBoost was chosen for its superior performance in handling heterogeneous, noisy, and high-dimensional time-series data typical in retail environments. The model was trained on a comprehensive feature set designed to capture demand volatility:
  1. Internal Data: Historical sales volume, inventory levels, promotional spend, and product lifecycle status.
  2. External Data: Macroeconomic indicators, competitor pricing, and relevant weather patterns (critical for seasonal apparel and footwear).
  3. Temporal Features: Holidays, seasonal indicators, and trend signals derived from recent sales velocity.
The system delivers a probabilistic forecast (not just a single-point estimate) for each SKU, enabling the client’s planners to make risk-adjusted inventory and merchandising decisions.

IV. Quantifiable Impact

The deployment of the XGBoost-powered Demand Forecasting Engine led to immediate, measurable financial and operational improvements:

Metric

Improvement Achieved

Business Outcome

Forecast Accuracy (MAPE)

Reduced Mean Absolute Percentage Error (MAPE) by 18%

Higher precision in stock allocation; fewer planning errors.

Inventory Efficiency

Decreased Excess Inventory (carrying costs) by 15%

Released capital previously tied up in unsold stock; reduced warehousing costs.

Revenue Capture

Reduced Stockout Rate for high-volume SKUs by 12%

Direct increase in realized sales and improved customer loyalty.

Core Impact: By leveraging predictive analytics and adoption support, the client transitioned from reactive inventory management to proactive demand shaping, resulting in reducing the annual spend by upto 7%, recovered revenue through optimized inventory flow