Lesson 234 of 1550
AI-Powered Demand Forecasting: When to Trust the Numbers
ML demand forecasts can outperform humans on routine demand — and badly miss black-swan events. Operations teams need to know which is which.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The premise
- 2demand forecasting
- 3time series
- 4black swan
Concept cluster
Terms to connect while reading
Section 1
The premise
ML demand forecasts work well for stable patterns and badly for shocks; the human knows which regime they're in.
What AI does well here
- Use ML forecasts for routine demand patterns where history is a good predictor of future
- Compare ML forecast to human-baseline forecast and investigate large divergences
- Flag scenarios where ML can't be trusted (new product launch, supply disruption, demand shock)
- Maintain confidence intervals — point forecasts hide the uncertainty that matters for inventory decisions
What AI cannot do
- Predict events the model has never seen (pandemics, supply shocks, viral demand surges)
- Substitute for the buyer's judgment about supplier reliability
- Replace inventory policy choices that are about risk tolerance, not just forecast accuracy
Key terms in this lesson
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