Fuel your optimization engine with the right demand signal. We combine classical statistical methods with modern ML techniques to generate clean baseline forecasts, remove non-recurring outliers, and account for complex seasonality. By separating signal from noise, we bridge the gap between raw sales data and your optimization models so production, inventory, and capacity plans are built on mathematical reality instead of instinct.

Key Benefits

✓Improve forecast accuracy by automatically selecting best-fit algorithms for each SKU
✓Capture complex seasonality and cyclical patterns that manual planning misses
✓Leverage Machine Learning to detect demand shifts and outlier anomalies
✓Feed optimization models automatically with clean, validated demand signals
✓Reduce safety stock buffers by narrowing the gap between forecast and actuals

Use Cases

•Generating robust baseline forecasts for monthly S&OP cycles
•Short-term demand sensing to adjust production schedules dynamically
•Modeling the impact of seasonality and promotional uplifts
•Forecasting for New Product Introductions (NPI) with limited history
•Long-term capacity planning based on trend projection

Implementation Approach

We start by ingesting your historical sales, shipment, and order data into our secure platform. For complex demand patterns, we incorporate external regressors - such as economic indicators, weather events, or market indices - to refine the signal. Our system performs automated data cleansing to remove non-recurring outliers and noise from the history. We then test multiple statistical and ML models against your data to select the champion algorithm for each product family, seamlessly piping the result into your optimization models.

Demand Forecasting & Planning

Key Benefits

Use Cases

Implementation Approach

Turn Complexity into Profitability