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A leading tools and equipment retailer struggled with erratic inventory cycles due to demand unpredictability. By deploying advanced forecasting models, such as Random Forest and XGBoost—trained on weekly sales trends—we helped them automate predictions, reduce overstocking, and improve revenue retention.
Inventory is capital in boxes—too little, and you lose sales; too much, and margins bleed. Northern Tools faced the classic challenge: predicting demand across hundreds of SKUs with variable sales patterns and limited inventory data. Sales anomalies, seasonal swings, and product diversity made manual planning unreliable and time-consuming.
The result?
Smoother stock levels, fewer backorders, and higher revenue retention lower costs.
To cut through this complexity, Technostacks implemented an AI-powered forecasting engine that decoded past trends and predicted future sales with unmatched accuracy.
Only aggregate stock data was available—no granular, time-stamped movement at SKU level—making it hard to track true availability or identify stockout-driven dips in demand.
The retailer relied heavily on weekly sales snapshots, which couldn’t capture demand surges during promotions or weather-driven events. This lag between market activity and system awareness led to missed opportunities and reactive stocking.
Seasonality and erratic spikes made standard time-series methods unreliable. Each SKU needed customized windows and decomposition logic.
Diverse sales behaviors across SKUs meant a single-model strategy fell flat—requiring a modular, product-specific approach.
We built a robust forecasting pipeline by combining time-series modeling with tree-based machine learning, tailored to each product’s behavior.
Introduced lag-based variables, rolling stats, and seasonality markers to capture short-term spikes and long-term patterns.
Evaluated and optimized multiple ML models (Random Forest, XGBoost, and LightGBM) to benchmark predictive performance.
Used MAE, RMSE, and R² scoring to track model accuracy, while ranking feature importance to refine forecasting logic.
Integrated forecasting outputs to support real-time inventory decisions—ready for peak sales, flash promos, or quiet seasons.
RMSE (Root Mean Square Error) measures how close your model’s predictions are to actual outcomes.
Lower RMSE = Higher Accuracy. These results reflect measurable, data-backed gains in forecasting precision.
See how AI can move your planning from the warehouse whiteboard to real-time, revenue-driven intelligence.
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