Factor Contribution in demand forecasting

Most demand forecasts answer one question well:

“What will sales be?”

But planning teams, especially in CPG and retail, need answers to a far more important question:

“Why will sales be that way?”

That’s where Factor Contribution comes in.

In the screenshot above, you’re not just seeing a forecast line. You’re seeing the story behind the forecast broken down into drivers like seasonality, holidays, events, price, promotions, lagged demand, and offsets. This layer of explainability is what transforms forecasting from a black box into a decision system.

Why Explainability Matters in Demand Forecasting

Traditional forecasting models often fail adoption not because they’re inaccurate, but because they’re opaque.

Planners and business leaders ask:

• Can I trust this number?
• What happens if I change price or promotions?
• Is this spike real demand or just seasonality?
• Are holidays driving uplift or cannibalisation?

Factor contribution answers all of these in one view.

What You’re Seeing in the Factor Contribution View

In the chart below the forecast line, each bar represents how much a specific factor contributes to demand for that time period—positively or negatively.

Common Factors explained

Each bar shows direction and magnitude—what is pushing demand up, and what is pulling it down.

From Forecast Numbers to Forecast Narratives

Instead of saying:

“Forecast for Dec 31 is 178 units.”

You can now say:

“Demand is driven primarily by seasonality (+25%), lagged momentum (+15%), and pricing effects (+8%), partially offset by weaker promotion intensity and holiday normalisation.”

This shift from numbers to narratives is what enables cross-functional alignment across demand planning, supply planning, finance, and marketing.