090°Open data
Open datasets, fully documented — searchable here, and readable by any LLM.
1 datasets
US Retail Inventory-Cycle Intelligence (derived)
Monthly US retail inventory-cycle intelligence from the U.S. Census Bureau's Monthly Retail Trade Survey (official 'Inventories and Inventories/Sales Ratios' workbook: 9 published industries — retail trade total, ex-motor-vehicle total, motor vehicle and parts dealers (441), furniture/home-furnishings/electronics (4423X), building materials (444), food and beverage (445), clothing (448), general merchandise (452), department stores (4522); 1992-01 ->). Each industry-month row carries published inventory levels and inventories/sales ratios (seasonally adjusted and not adjusted, millions of USD / months of supply), inventory momentum (MoM, 3-month-annualized, YoY), the ratio's YoY change and 36-month z-score, a documented 0-100 inventory pressure score (50% winsorized ratio z-score +-2.5 + 30% winsorized inventory YoY +-25pp + 20% winsorized ratio YoY change +-1.0pp, min-maxed within each month) with deterministic rank and p1-p4 tiers across the 7 peer industries, overstock/understock flags (|z| > 1.0), 36-month ratio record-high and inventory-surge flags, the exact unpublished-kinds inventory residual on the ex-441 row, plus month-level context (total and ex-441 ratios, overstock/understock breadth, the month's top pressure industry, median pressure score). An online shop joins monthly inventory pressure to its own stock planning on (date, industry_code) for overstock/understock and markdown timing; a sales team joins category pressure to pipeline value on date. Method: one polite GET of the official Census workbook; a trailing-36-month verbatim transcription is embedded for offline safety; the transform is pure pandas in map_schema with no imputation (warmup cells are honest nulls). Caveats: the latest month is preliminary and revised next release; the published kind detail is not exhaustive (the unpublished residual covers 446/447/451/453/454); the 2020-04 ratios for clothing (18.63) and department stores (48.93 SA / 51.88 NSA) reflect the COVID sales collapse, not a data error; department stores (4522) are a subset of general merchandise (452) and are excluded from the additive identity.
Once today's free allowance is used up, AI features can run on your own provider account.
Kept in this browser tab only (cleared when you close it) and sent with each AI request. Our servers use it for that request and never store or log it.