📋 Store 43 – Forecasting Accuracy Summary
| Model | MAE ($) | MAPE (%) | RMSE ($) |
|---|---|---|---|
| Excel | 2,658 | 125.10% | 4,161 |
| Machine Learning | 1,075 | 62.65% | 1,652 |
Store 43 faced major forecasting challenges — but ML cut the error in half and delivered sharper performance even during heavy volatility.
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
📬 Contact
- Email: micahshull.datascientist@gmail.com
- LinkedIn: https://www.linkedin.com/in/micahshull/
- Phone: 415-317-6814