Why Spreadsheets Fail at Capturing Events and Seasonality Impact on Demand Predictions for Growing Brands

For many growing commerce brands, spreadsheets continue to serve as the primary planning tool for demand forecasting. While they provide flexibility and accessibility, spreadsheets were designed for static calculations rather than modeling complex behavioral demand patterns.

As demand becomes increasingly influenced by promotional events, campaign calendars, and seasonal buying trends, spreadsheet-based forecasting begins to show structural limitations.

In spreadsheet-driven planning environments, capturing the impact of events such as holiday promotions or influencer campaigns typically requires manual adjustments. Planners modify forecast values based on anticipated uplift or seasonal peaks.

While these overrides may improve short-term accuracy, they introduce variability and dependency on individual planner judgment.

• Event-driven demand adjustments are inconsistent
• Seasonality assumptions vary across planners
• Overrides are difficult to audit
• SKU-level adjustments increase workload
• Forecast reproducibility decreases over time

Spreadsheets often track forecast accuracy at an aggregated level, which can create the illusion of reliable planning. However, inventory decisions are executed at SKU-store granularity.

Seasonal demand variability captured at a category level may not translate accurately to individual SKUs, resulting in localized overstock or stockout situations.

Spreadsheet-based systems are not designed to simulate multiple demand scenarios associated with commercial events. Planners are typically forced to maintain separate forecast versions for baseline demand and event-driven uplift.

This fragmentation complicates procurement planning and increases the risk of misaligned inventory positioning.

• Version control challenges
• Delayed procurement alignment
• Limited scenario comparison
• Higher planning cycle time
• Reduced forecast transparency

Modern demand planning systems integrate commercial calendars directly into forecasting workflows. Instead of relying on manual overrides, AI-native platforms detect and quantify the impact of events automatically.

This enables planners to focus on decision-making rather than forecast maintenance, improving both accuracy and planning efficiency.

As growing brands increase their promotional cadence and channel footprint, spreadsheet-based planning becomes increasingly difficult to scale.

Capturing the true impact of events and seasonality requires forecasting systems capable of modeling demand behavior across SKU-level execution environments.