Add privacy-safe demand forecasting case study

Synthetic monthly SKU/region demand notebook comparing a trimmed-mean
baseline against gradient boosting, with a gated TimesFM build-vs-buy
comparison. Includes rolling-origin validation, executive narrative,
architecture diagram, one-page PDF, and LinkedIn draft.

No company data, credentials, or private implementation details.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

README.md

@@ -0,0 +1,51 @@
+# 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/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.