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30% Fewer Stockouts. 25% Less Overstock. One AI Forecasting Engine.

Imagine predicting product demand with laser precision—cutting holding costs, optimizing stock levels, and boosting revenue, all while saying goodbye to guesswork.

Client

Leading tools and equipment retailer

Industry

Retail & Distribution

Timeline

5 months

Quick Snapshot

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.

Brief

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.

Challenges

Limited Inventory Visibility
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.
Lack of Real-Time Demand Signals
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.
Complex, Non-Linear Demand
Seasonality and erratic spikes made standard time-series methods unreliable. Each SKU needed customized windows and decomposition logic.
High Product Variability
Diverse sales behaviors across SKUs meant a single-model strategy fell flat—requiring a modular, product-specific approach.

Solution

We built a robust forecasting pipeline by combining time-series modeling with tree-based machine learning, tailored to each product’s behavior.

Feature
Engineering

Introduced lag-based variables, rolling stats, and seasonality markers to capture short-term spikes and long-term patterns.

Model
Training

Evaluated and optimized multiple ML models(Random Forest, XGBoost, and LightGBM) to benchmark predictive performance.

Advanced
Validation

Used MAE, RMSE, and R² scoring to track model accuracy, while ranking feature importance to refine forecasting logic.

Deployment at
Scale

Integrated forecasting outputs to support real-time inventory decisions—ready for peak sales, flash promos, or quiet seasons.

Impact

30%

Reduction in Stockouts

AI-powered forecasts kept fast-moving items on shelves, eliminating lost sales due to demand misjudgments. With predictive visibility across SKUs and regions, inventory planners closed critical gaps in real-time.

25%

Drop in Overstocking

Precision forecasting cut excess inventory and reduced holding costs. The result? Leaner stockrooms, lower waste, and more capital freed up for growth initiatives.

20%

Improvement in Forecast Accuracy

Advanced tree-based models captured complex demand patterns with greater precision—boosting forecasting reliability across diverse product categories.

15%

Increase in Revenue Retention

With fewer markdowns and tighter inventory alignment, businesses improved margins and reduced lost revenue—turning forecasting accuracy into bottom-line impact.

Before vs After: Forecast Accuracy
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.

Ready to make your inventory decisions less reactive and more predictive?

See how AI can move your planning from the warehouse whiteboard to real-time, revenue-driven intelligence.
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Our Solutions in Action

Read how we have transformed businesses along the way.

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