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>
This commit is contained in:
Gabriel Pereira
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docs/architecture.mmd
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docs/architecture.mmd
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flowchart LR
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A[Synthetic monthly demand] --> B[Prepare dates and features]
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B --> C[Trimmed-mean baseline]
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B --> D[Gradient-boosting model]
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C --> E[Rolling validation]
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D --> E
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E --> F[Select by sMAPE]
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F --> G[12-month forecast]
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H[Manual trigger today] -.-> B
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I[Future Airflow boundary] -.-> B
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subgraph Delivery["Delivery ownership"]
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J[Business question] --> K[Reproducible evidence]
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K --> L[Review and adoption]
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end
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docs/architecture.svg
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docs/architecture.svg
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<svg xmlns="http://www.w3.org/2000/svg" width="1100" height="430" viewBox="0 0 1100 430" role="img" aria-labelledby="title desc">
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<title id="title">Demand forecasting delivery architecture</title>
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<desc id="desc">Synthetic demand flows through preparation, baseline and model, rolling validation, selection, and a twelve-month forecast. Manual triggering today has a future Airflow boundary.</desc>
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<style>
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.box { fill: #f5f7fb; stroke: #243b53; stroke-width: 2; rx: 12; }
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.accent { fill: #d9f0ff; }
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.text { fill: #102a43; font: 18px sans-serif; text-anchor: middle; }
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.small { fill: #486581; font: 15px sans-serif; text-anchor: middle; }
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.arrow { stroke: #486581; stroke-width: 2.5; fill: none; marker-end: url(#arrowhead); }
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.dashed { stroke-dasharray: 8 7; }
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</style>
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<defs>
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<marker id="arrowhead" markerWidth="10" markerHeight="7" refX="9" refY="3.5" orient="auto">
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<polygon points="0 0, 10 3.5, 0 7" fill="#486581"/>
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</marker>
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</defs>
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<rect class="box accent" x="25" y="155" width="155" height="70"/>
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<text class="text" x="102" y="185">Synthetic</text><text class="text" x="102" y="208">demand</text>
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<rect class="box" x="220" y="155" width="165" height="70"/>
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<text class="text" x="302" y="185">Prepare</text><text class="text" x="302" y="208">features</text>
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<rect class="box" x="425" y="80" width="180" height="70"/>
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<text class="text" x="515" y="110">Trimmed-mean</text><text class="text" x="515" y="133">baseline</text>
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<rect class="box" x="425" y="230" width="180" height="70"/>
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<text class="text" x="515" y="260">Gradient-boosting</text><text class="text" x="515" y="283">model</text>
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<rect class="box" x="650" y="155" width="175" height="70"/>
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<text class="text" x="737" y="185">Rolling</text><text class="text" x="737" y="208">validation</text>
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<rect class="box" x="870" y="155" width="190" height="70"/>
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<text class="text" x="965" y="185">12-month</text><text class="text" x="965" y="208">forecast</text>
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<path class="arrow" d="M180 190 H220"/>
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<path class="arrow" d="M385 190 H410 V115 H425"/>
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<path class="arrow" d="M385 190 H410 V265 H425"/>
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<path class="arrow" d="M605 265 H625 V190 H650"/>
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<path class="arrow" d="M825 190 H870"/>
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<text class="small" x="300" y="45">Manual trigger today</text>
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<path class="arrow dashed" d="M300 55 V145"/>
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<text class="small" x="740" y="365">Future orchestration boundary</text>
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<path class="arrow dashed" d="M740 350 V235"/>
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</svg>
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After Width: | Height: | Size: 2.5 KiB |
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docs/case-study-one-page.html
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docs/case-study-one-page.html
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<!doctype html>
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<html lang="en">
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<head>
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<meta charset="utf-8">
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<title>Demand Forecasting Case Study</title>
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<style>
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@page { size: A4; margin: 14mm; }
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body { color: #102a43; font: 10.5pt Arial, sans-serif; line-height: 1.35; margin: 0; }
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h1 { color: #0b4f71; font-size: 24pt; margin: 0 0 4pt; }
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h2 { color: #0b4f71; font-size: 13pt; margin: 10pt 0 3pt; }
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p { margin: 4pt 0; }
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.subtitle { color: #486581; font-size: 11pt; margin-bottom: 8pt; }
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.card { background: #f5f7fb; border-left: 4px solid #2f80a8; padding: 7pt 9pt; }
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img { display: block; margin: 5pt auto; max-width: 100%; }
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.columns { display: grid; grid-template-columns: 1fr 1fr; gap: 12pt; }
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ul { margin: 4pt 0 0 16pt; padding: 0; }
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li { margin: 2pt 0; }
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footer { border-top: 1px solid #bcccdc; color: #627d98; font-size: 8.5pt; margin-top: 9pt; padding-top: 5pt; }
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</style>
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</head>
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<body>
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<h1>From demand question to forecast evidence</h1>
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<div class="subtitle">A privacy-safe, player/coach delivery case study using synthetic monthly demand</div>
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<div class="card">
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<strong>The case.</strong> Build a repeatable forecasting flow without exposing operational data.
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The public version preserves the delivery decisions that matter: preparation, an auditable
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baseline, model comparison, validation, and a clear operating boundary.
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</div>
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<h2>Architecture</h2>
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<img src="architecture.svg" alt="Demand forecasting delivery architecture">
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<div class="columns">
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<div>
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<h2>Modeling choices</h2>
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<ul>
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<li>Monthly demand by synthetic SKU and region.</li>
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<li>Baseline: previous eight months, excluding one minimum and one maximum.</li>
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<li>One gradient-boosting model with lag and calendar features.</li>
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<li>Rolling-origin validation for a 12-month horizon.</li>
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</ul>
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</div>
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<div>
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<h2>Delivery choices</h2>
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<ul>
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<li>Start with a baseline stakeholders can audit.</li>
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<li>Add complexity only when validation earns it.</li>
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<li>Keep triggering manual while the workflow is proved.</li>
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<li>Leave a clean seam for future orchestration.</li>
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</ul>
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</div>
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</div>
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<h2>Evidence</h2>
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<p>The notebook computes validation metrics and produces comparison charts. This handout makes no numeric business claims because the data is synthetic.</p>
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<footer>Public-safe artifact: no company rows, identifiers, credentials, private URLs, or copied business values.</footer>
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</body>
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</html>
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docs/case-study-one-page.md
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docs/case-study-one-page.md
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# From demand question to forecast evidence
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## The case
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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.
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## Architecture
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```mermaid
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flowchart LR
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source[Synthetic demand] --> prep[Preparation/features]
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prep --> baseline[Trimmed-mean baseline]
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prep --> model[Gradient boosting]
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baseline --> validate[Rolling validation]
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model --> validate
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validate --> forecast[12-month forecast]
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```
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## Delivery narrative
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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.
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## Evidence
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The notebook computes validation metrics and produces comparison charts. This handout deliberately makes no numeric business claims because the data is synthetic.
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BIN
docs/case-study-one-page.pdf
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docs/case-study-one-page.pdf
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docs/linkedin-post.md
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docs/linkedin-post.md
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# LinkedIn draft
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I built a privacy-safe demand-forecasting case study to show how I approach AI delivery as a player/coach.
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The demo starts with synthetic monthly demand by SKU and region, then compares:
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- an auditable baseline based on the previous eight months;
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- one gradient-boosting model with lag and calendar features;
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- rolling time-based validation instead of a random train/test split.
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The important part is not adding the most models. It is making the path from business question to reproducible evidence explicit: prepare the data, establish a baseline, validate the model, document the operating boundary, and leave a clean seam for future orchestration.
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The public repository contains no company data or private implementation details.
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docs/publication-checklist.md
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# Publication checklist
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- [ ] No credentials, tokens, private keys, `.env` files, or secret names.
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- [ ] No company rows, identifiers, product names, plant names, or business values.
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- [ ] No private schemas, endpoints, account names, or internal URLs.
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- [ ] All charts and metrics come from synthetic data.
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- [ ] Numeric results are labeled illustrative and are not presented as business outcomes.
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- [ ] Notebook executes from a clean environment with `uv`.
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- [ ] Diagram and PDF contain only generic architecture terms.
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