Full schema reference for the Grow Datahub parquet lake. Tables, column definitions, data lineage diagrams, and source-to-target mappings.
Technical case for Grow's DuckDB + Parquet + Python analytics stack. Architecture layers, schema standards, no-infrastructure rationale, implementation roadmap.
Grow's end-to-end forecasting methodology: demand models, decay curves, newsvendor inventory targets, and seasonal planning. Interactive charts and calculators.
Why standard accuracy metrics miss the real cost of inventory errors, and how the newsvendor model sets smarter stocking targets using Grow's Cu and Co values.
Six-tab master report: 2026 plan (31,453 units, +30.1% vs H2025), seasonal demand curves, historical SKU performance, growth target assessment, V6 config as-run, and Engine Audit.
Real-time FM-01 engine simulator. Bear / Base / Bull scenario toggle, H2025 actuals vs H2026 forecasted, retired fragrance actuals, and Export Config (.yaml).