How a Leading CPG Company Captured $500K in Missed Demand with AI-Powered Forecasting

• $500K Demand Captured: During a seven-week Super Bowl season, forecasts identified and captured over half a million dollars in demand that would have otherwise been missed.
• 50,000+ Units Recovered: AI-driven forecasting uncovered more than 50,000 units of incremental demand across nearly 1,000 SKUs.
• 25% Improvement in Accuracy: Mean squared error (MSE) improved significantly, increasing overall forecast precision across seasonal profiles.
• 45% Reduction in Under-Forecasting: Sharper demand visibility reduced missed sales opportunities while maintaining balanced inventory levels.
• 60% of Products Improved: A majority of SKUs saw reduced under-forecasting, with minimal regression across the portfolio.
• Months to Hours: Manual profile creation that once took months is now completed in hours—freeing teams to focus on higher-value planning.

A large consumer packaged goods company with a diverse portfolio of nearly 10,000 food products and complex seasonal demand patterns.

Seasonality was always part of the business—but scaling it wasn’t.

With thousands of SKUs and regional demand variability, the team needed a better way to understand when products would spike, where demand would shift, and how to plan accordingly. Their existing approach relied on manually created “sales profiles,” built over weeks or months by planners trying to map products to seasonal patterns.

This method created limitations. It lacked validation, couldn’t adapt to regional differences, and made it difficult to confidently align forecasts with real-world demand. As a result, inaccurate forecasts led to incorrect order points and missed sales opportunities.

The goal was clear: move from manual estimation to a data-driven, scalable approach that could accurately capture seasonal demand—and do it faster.

TrueCommerce ReplenishAI introduced a new way to approach forecasting—one built on data, validation, and speed.

Relying on manually defined assumptions, it identified natural demand patterns and grouped products into validated seasonal profiles.

The solution was built on a three-step pipeline:

• Data Engineering: Preparing and structuring large-scale demand data
• Modeling: Standardizing and clustering demand patterns into seasonal profiles
• Validation: Testing results against unseen data to ensure accuracy and confidence

This validation layer became a key differentiator. By measuring performance using metrics like mean squared error (MSE) and under-forecasted demand (UFD), the system ensured forecasts didn’t just look accurate—they performed in real-world conditions.

Equally important was the “human-in-the-loop” approach. Planners could review AI-generated insights, apply expertise where needed, and focus attention only where it mattered most.

The biggest takeaway wasn’t just the technology— it was the combination of AI and human expertise.

By pairing advanced modeling with planner insight, the company created a forecasting approach that adapts, learns, and improves over time. The result is not just better forecasts, but better decisions—ones that protect revenue, reduce risk, and support growth.

As seasonal demand continues to evolve, this foundation ensures the business can respond with confidence, speed, and precision.