Why Spreadsheets Fail at Capturing Events and Seasonality Impact on Demand Predictions for $10M–$100M Companies

For companies in the $10M–$100M range, spreadsheets remain the most widely used demand planning tool. Their accessibility and flexibility make them attractive during early growth stages.

However, as promotional cadence increases and product catalogs expand, spreadsheet-driven forecasting struggles to capture seasonal demand variability accurately.

In spreadsheet environments, planners often adjust forecasts manually to reflect upcoming promotions or seasonal peaks.

While overrides may improve short-term accuracy, they introduce inconsistency across planning cycles.

• Inconsistent demand adjustments
• High planner workload
• Limited scenario evaluation
• Reduced forecast reproducibility
• Delayed procurement alignment

As SKU counts grow, maintaining spreadsheet models becomes increasingly difficult.

Capturing SKU-level variability tied to promotional events requires structural demand modeling that spreadsheets cannot support.

Promotional schedules and seasonal sales calendars often exist outside spreadsheet-based forecasting models.

This disconnect prevents demand predictions from incorporating upcoming commercial events accurately.

Forecast inaccuracies during peak demand windows lead to stockouts or excess inventory.

Both outcomes introduce operational inefficiencies and financial risk.

Modern planning systems integrate commercial calendars and isolate baseline demand from event-driven uplift.

This supports scalable demand forecasting aligned with seasonal cycles.

For $10M–$100M companies, spreadsheet-driven planning introduces structural forecasting gaps.

AI-native systems enable procurement decisions aligned with demand variability, supporting scalable growth.