# Demand Forecasting Case Study

Privacy-safe demonstration of a demand-forecasting delivery flow using only synthetic data, written as an executive-readable AI delivery case study.

## What it demonstrates

- Monthly demand by synthetic SKU and region.
- A baseline forecast: the mean of the preceding eight observed months after removing one minimum and one maximum.
- One gradient-boosting model using lag and calendar features.
- Leakage-safe rolling-origin validation for a 12-month horizon.
- A manual trigger boundary that can later be connected to Airflow.
- Annotated charts and written observations connecting modeling choices to delivery governance.
- An optional, gated build-vs-buy comparison against a pretrained TimesFM foundation model (`RUN_TIMESFM=1`), evaluating operational cost alongside accuracy instead of switching models by default.

The notebook calculates evaluation metrics for model selection, but the published case-study narrative intentionally avoids numeric business claims.

The notebook is structured for an executive audience: it starts with the business question, explains why the baseline exists, visualizes the signal and validation trade-off, and closes with delivery implications for a lean AI task force.

## Run

```bash
uv sync
uv run jupyter notebook notebooks/forecasting_case_study.ipynb
```

To execute the notebook non-interactively:

```bash
uv run jupyter execute notebooks/forecasting_case_study.ipynb --inplace
```

To also run the optional TimesFM foundation-model comparison (downloads ~500M-parameter weights
from Hugging Face; needs `torch`/`transformers` and internet access):

```bash
RUN_TIMESFM=1 uv run --with torch --with transformers jupyter execute notebooks/forecasting_case_study.ipynb --inplace
```

## Privacy boundary

This repository contains no company rows, identifiers, schemas, credentials, private URLs, or copied business values. Review `docs/publication-checklist.md` before publishing.

## Artifacts

- `notebooks/forecasting_case_study.ipynb` — reproducible analysis.
- `docs/forecasting-case-study-notebook.pdf` — rendered notebook with charts and results for platforms that do not preview Jupyter notebooks.
- `docs/architecture.mmd` — editable architecture diagram.
- `docs/architecture.svg` — editable/exportable diagram asset used by the handout.
- `docs/case-study-one-page.md` — Markdown source for the interview handout.
- `docs/case-study-one-page.html` — print-ready handout source.
- `docs/case-study-one-page.pdf` — generated one-page interview handout.
- `docs/linkedin-post.md` — publication draft.