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>

docs/case-study-one-page.md

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+# 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.