Foresight can apply several forecasting approaches depending on the data and the forecast type. This section explains each one in business terms: when to choose it, and where it is weak. Use it during blueprint discussions when a customer asks "why this method and not another."

5.1 The model menu

5.2 What Foresight chooses automatically

In most cases the consultant does not pick a model per item. The engine does:

• Inventory forecast. Uses a trend-fit (regression) approach when there is enough movement history, otherwise falling back to a moving average over the recent window. Confidence reflects how well the trend fits.

• Sales forecast. Uses a pattern-learning (Random Forest) approach per item when there is enough history, otherwise a recency-weighted moving average that blends weekday, weekend, and seasonal factors.

• Financial forecast. The chosen model is recorded on the forecast for transparency. Risk and confidence are scored from the prediction range and historical comparison.

5.3 Model selection decision tree (for the blueprint)

5.4 When each model struggles (warn the customer)

5.5 Confidence vs accuracy: the metric customers must understand

These are two different ideas, and they are often confused:

• Confidence. How sure the model is at the time of prediction, based on data volume and how well the pattern fits.

• Accuracy. How close the prediction turned out to be after the fact. It is calculated as accuracy = max(0, (1 − |predicted − actual| / |actual|) × 100).

A well-calibrated forecast has confidence close to accuracy. Foresight tracks the gap and flags overconfident items (confident but wrong). That gap is the signal to revisit a forecast. This honesty is a selling point: the system tells you what it does not know.