📋 Store 37 – Forecasting Accuracy Summary

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Time Series Forecast - Store 37 ML Forecast - Store 37

📋 Store 37 – Forecasting Accuracy Summary

Model MAE ($) MAPE (%) RMSE ($)
Excel 2,611 19.16% 3,598
Machine Learning 867 6.28% 1,216

At Store 37, our ML model delivered a dramatic improvement in forecast precision — cutting error by more than 65% and making planning significantly more accurate.

We evaluate forecast performance using three key metrics:

  • MAE (Mean Absolute Error): This tells us how far off the forecast was each day, on average — in dollars. Lower is better.
  • MAPE (Mean Absolute % Error): A percentage-based measure that shows how inaccurate the forecast was, relative to actual sales.
  • RMSE (Root Mean Squared Error): This emphasizes the big forecasting mistakes. It’s more sensitive to major sales swings — and a strong indicator of risk.

Traditional Excel-style forecasts tend to miss large spikes and drops, especially during volatility. Our machine learning model adapts to changing trends, promotions, and seasonality — giving you forecasts you can actually rely on.

Bottom line: lower forecast error means better planning, less waste, and fewer surprises.

💡 Why Accurate Forecasting Matters

  • 📉 Protect Cash Flow: Poor forecasts lead to wasted inventory, missed revenue, and bad staffing decisions.
  • 📅 Plan with Confidence: Reliable forecasts improve budgeting, purchasing, and sales alignment.
  • ⚠️ Spot Trouble Early: ML models detect downturns sooner — giving you time to act, not just react.

🚀 Want to See It with Your Data?

If your business has a few months or years of daily or weekly sales, we can:

  • ✅ Review your data for free
  • ✅ Deliver a sample forecast
  • ✅ Help you see if ML forecasting is a good fit

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