For years, demand planning in FMCG was equal parts science and instinct. Experienced planners knew which festivals moved which SKUs, which distributors consistently over-ordered, and when to trust the model and when to override it. That institutional knowledge was genuinely valuable.

It was also fragile, inconsistent, and impossible to scale.

The Limits of Experience-Based Forecasting

Experience-based planning works — until it does not. When your most experienced planner leaves, the knowledge leaves with them. When you launch a new SKU in a new region, there is no historical context to draw on. When an external event disrupts demand in ways your model has never seen, the forecast breaks.

The deeper problem is that even experienced planners are working with incomplete information. They can see historical sales data. They often cannot see the weather patterns, local event calendars, competitor promotional activity, or regional economic signals that are actually driving demand. The model is as limited as the inputs.

What Modern Demand Planning Looks Like

The most sophisticated FMCG demand planning today uses hybrid forecasting models that combine your internal historical data with external signals — weather, local events, promotional calendars, regional retail data — to build forecasts that reflect the full picture of what is driving demand, not just the internal view.

When Seven Billion built a hybrid demand forecasting model for a leading FMCG client, the integration of external signals — including local event data, weather patterns, and regional retail indicators — drove a 64% improvement in forecasting accuracy. Inventory stocking efficiency improved by 86%. Stockouts dropped by 32%.

These are not incremental improvements. They are structural changes in how the supply chain operates.

What Accuracy Gains Actually Mean

A 30 to 64% improvement in forecasting accuracy does not just mean fewer stockouts. It means less capital tied up in buffer stock. It means better promotional ROI because you are not running out of stock in the middle of a campaign or carrying excess inventory afterwards. It means the supply chain team spends less time firefighting and more time planning.

At enterprise scale, the financial impact of improved forecast accuracy compounds across every SKU, every region, and every distribution channel simultaneously.

Getting Started

The transition from experience-based to data-driven planning does not require a perfect data infrastructure. It requires honest clarity about what data you have, what signals you are currently missing, and what the highest-value improvement would look like for your specific supply chain.

That is exactly what Seven Billion's Phase 0 discovery process is designed to surface — before any commitment, before any build, with a clear picture of what is achievable and how long it will take to get there.

Conclusion

The question is not whether you have enough data to forecast better. You almost certainly do. The question is whether you are using it — or whether you are still relying on the experience-based planning model your business outgrew three years ago.