The Hidden Cost of Inaccurate Sales Forecasts

In a context where resource optimization is increasingly strategic, identifying the most effective levers for improving profitability is critical. One of the most underused levers is improving forecast accuracy. Many forecasting models still rely on partial data or rigid approaches, limiting their reliability. Better forecasting reduces the risks of dormant inventory, stockouts, and the indirect costs associated with these imbalances.

Errors That Impact the Entire Chain

Inaccurate forecasts lead to stock imbalances. These imbalances result in lower product availability, higher storage costs, and reduced customer satisfaction.

According to a Bain & Company study, up to 70% of in-store stockouts stem from poor initial forecasts. The consequences ripple across the entire chain: poor planning, inaccurate replenishment, and a disconnect between stock management and on-the-ground reality.

When Inventory Becomes a Burden

Misaligned stock levels are expensive. Capital tied up in non-moving products limits investment capacity.

Conversely, a stockout of a key item results in direct lost sales. Each stockout is estimated to cause a 4–8% revenue loss on the item affected, depending on the sector.

With optimized inventory management based on accurate forecasts, businesses can precisely identify their needs and align their supply accordingly.

AI: Turning Forecasting Into a Profitability Driver

The integration of AI into sales forecasting processes is a game changer. By combining historical data, external signals, and real-time purchasing behaviors, AI significantly increases forecast accuracy. This enables proactive stock level adjustments, precise logistics optimization, and improved tracking via smart commercial dashboards and margin simulators.

Inaccurate forecasts do more than misguide purchasing—they erode margins, disrupt operations, and weaken the supply chain. Fixing this requires a modernization of forecasting tools.