930534dde2
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>
52 lines
2.4 KiB
Markdown
52 lines
2.4 KiB
Markdown
# Demand Forecasting Case Study
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Privacy-safe demonstration of a demand-forecasting delivery flow using only synthetic data, written as an executive-readable AI delivery case study.
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## What it demonstrates
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- Monthly demand by synthetic SKU and region.
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- A baseline forecast: the mean of the preceding eight observed months after removing one minimum and one maximum.
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- One gradient-boosting model using lag and calendar features.
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- Leakage-safe rolling-origin validation for a 12-month horizon.
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- A manual trigger boundary that can later be connected to Airflow.
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- Annotated charts and written observations connecting modeling choices to delivery governance.
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- 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.
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The notebook calculates evaluation metrics for model selection, but the published case-study narrative intentionally avoids numeric business claims.
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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.
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## Run
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```bash
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uv sync
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uv run jupyter notebook notebooks/forecasting_case_study.ipynb
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```
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To execute the notebook non-interactively:
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```bash
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uv run jupyter execute notebooks/forecasting_case_study.ipynb --inplace
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```
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To also run the optional TimesFM foundation-model comparison (downloads ~500M-parameter weights
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from Hugging Face; needs `torch`/`transformers` and internet access):
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```bash
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RUN_TIMESFM=1 uv run --with torch --with transformers jupyter execute notebooks/forecasting_case_study.ipynb --inplace
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```
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## Privacy boundary
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This repository contains no company rows, identifiers, schemas, credentials, private URLs, or copied business values. Review `docs/publication-checklist.md` before publishing.
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## Artifacts
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- `notebooks/forecasting_case_study.ipynb` — reproducible analysis.
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- `docs/architecture.mmd` — editable architecture diagram.
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- `docs/architecture.svg` — editable/exportable diagram asset used by the handout.
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- `docs/case-study-one-page.md` — Markdown source for the interview handout.
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- `docs/case-study-one-page.html` — print-ready handout source.
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- `docs/case-study-one-page.pdf` — generated one-page interview handout.
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- `docs/linkedin-post.md` — publication draft.
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