# From demand question to forecast evidence

## The case

Build a repeatable forecasting flow without exposing operational data. The public version uses synthetic monthly demand by SKU and region while preserving the delivery decisions that matter: data preparation, a transparent baseline, model comparison, validation, and a clear operating boundary.

## Architecture

```mermaid
flowchart LR
  source[Synthetic demand] --> prep[Preparation/features]
  prep --> baseline[Trimmed-mean baseline]
  prep --> model[Gradient boosting]
  baseline --> validate[Rolling validation]
  model --> validate
  validate --> forecast[12-month forecast]
```

## Delivery narrative

Start with a baseline that stakeholders can audit. Add one model only when validation shows a useful improvement. Keep the trigger manual while the workflow is being proved, then expose the same boundary to an orchestrator such as Airflow.

## Evidence

The notebook computes validation metrics and produces comparison charts. This handout deliberately makes no numeric business claims because the data is synthetic.