A specialty retailer with 500 SKUs and $180M in annual revenue lost an estimated $8M to stockouts annually, with inventory decisions driven by gut instinct and historical patterns. We built a machine learning demand forecast model using XGBoost, trained on four years of sales history, seasonality, promotions, and external signals, deployed as a weekly batch pipeline feeding the merchandising team's planning tool.
The retailer managed hundreds of SKUs across product categories, but inventory planning remained largely manual and reactive. Buyers and merchandisers relied on seasonal heuristics, the previous year's sales, and personal judgment. The result was predictable, inefficient, and costly, a combination of chronic stockouts on popular items and overstock on slow-moving inventory.
The company had invested in a merchandising planning tool but had no intelligence layer feeding it. They needed a demand forecasting system that could process historical patterns, seasonality, promotion impact, and external signals (weather, market trends, competitor activity) to generate weekly SKU-level demand predictions that could be embedded directly into buying workflows.
We cleaned four years of historical sales data, engineered features for seasonality, promotions, and external signals, trained and backtested an XGBoost model, deployed it as a weekly scoring pipeline, and integrated forecasts into the merchandising planning tool.
Demand forecasts enabled buyers to stock correctly for seasonal peaks and promotional events. High-demand SKUs now have buffer inventory. Revenue recovery from avoided stockouts is substantial.
Reduction in excess inventory, clearance discounts, and working capital tied up in slow-moving stock. Forecast-driven buying improved inventory turns and cash flow efficiency.
Model captures seasonality, promotion lift, and demand patterns with high precision. Merchandising team trusts forecasts and incorporates them into weekly buying plans.
Gradient boosting framework for demand forecasting, with strong performance on tabular sales and seasonal data. Provides feature importance and confidence intervals.
Feature engineering, data preprocessing, model training, hyperparameter tuning, and pipeline orchestration. Libraries for evaluation metrics and forecast visualization.
Weekly batch scoring DAG pulling fresh sales and promotion data, running predictions, and loading forecast results. SLA monitoring and error alerting.
Data warehouse for historical training data and weekly predictions. Tableau dashboards for forecast visualization, variance analysis, and merchandising team insights.
Bring the problem. We'll come back with a written brief, what to build, what to defer, and where AI actually moves the number. No deck pitches.