Zansa › Answers › Forecasting and data science
When is machine learning not worth it for forecasting?
Machine learning is often not worth it when you have little history, stable demand, few items, or when a simple method is nearly as accurate. The extra cost of building, explaining and maintaining a model must be repaid by better decisions. If a seasonal average gets you close enough, use that.
Signs that simpler methods may win:
- Fewer than two years of data.
- Noisy, tiny volumes where nothing predicts well.
- Decisions that do not change with small accuracy gains.
- No one available to monitor the model.
- A need for full transparency.
Machine learning tends to pay off with many products or locations, rich drivers like prices, promotions and weather, and decisions where small percentage gains are worth a lot of money. Test against a baseline before deciding.
Want this applied to your business? Zansa builds dashboards and forecasts for small and mid-sized companies worldwide. Start with a free intro call.
Book a call