US business inventory cycle: inventories-to-sales panel (monthly)

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Monthly US business-inventory-cycle panel, 1992-01 onward: total business inventories (Census MTIS, $M, SA) and the inventories-to-sales ratio (months of sales on hand), redistributed keyless via FRED fredgraph.csv. Carries MoM/YoY momentum in inventories and the ratio, an implied total-business sales level with its YoY change, the ratio's deviation from its trailing-12m average, a look-ahead-free 0-100 inventory-overhang score (the ratio's trailing-36m percentile), documented lean/balanced/building/overhang regimes, and destocking/restocking flags (ratio down/up >= 0.05 YoY). One row per month both Census series published; any null fails loudly. Who joins this: demand forecasters watch the overhang regime for discounting and order cutbacks; retail and wholesale planners use destocking_flag as the restock-order signal; macro models read a rising ratio as a production-slowdown precursor.

Source: US Business-Inventory-Cycle Intelligence (Census MTIS via FRED, keyless)
Rows: 415
Columns: 19
Source cadence: Monthly
Last refreshed: Oct 1, 2026
Theme: economy

Column	Type	Description
date	date	Reference month (first day) on which both Census MTIS series published (panel join key). The panel grid is a complete monthly grid from 1992-01; any missing month fails loudly, never imputed. (unit: date)
year_month	string	Calendar month as YYYY-MM. (unit: string)
country	string	Country name (shared normalization layer). (unit: string)
country_code	string	ISO 3166-1 alpha-3 country code (USA). (unit: string)
inv_total_musd	float	Total business inventories (FRED BUSINV): end-of-period stock across manufacturing, wholesale and retail, millions of dollars, seasonally adjusted. The panel's stock level. (unit: millions of dollars, SA)
is_ratio	float	Total business inventories-to-sales ratio (FRED ISRATIO): end-of-month inventories divided by monthly sales — the months of sales sitting on shelves, seasonally adjusted. The panel's cycle gauge: rising = overhang building, falling = shelves clearing. (unit: ratio, SA)
sales_total_musd	float	Implied total-business sales level, millions of dollars, seasonally adjusted: inv_total_musd / is_ratio. Derived locally, not a Census publication. (unit: millions of dollars, SA)
inv_mom_pct	float	Month-over-month percent change in total business inventories. Null for the first month. (unit: percent)
inv_yoy_pct	float	Year-over-year percent change in total business inventories. Null for the first 12 months. (unit: percent)
sales_yoy_pct	float	Year-over-year percent change in implied total-business sales. Null for the first 12 months. (unit: percent)
is_ratio_mom_pp	float	Month-over-month change in the inventories-to-sales ratio, ratio points. Null for the first month. (unit: ratio points)
is_ratio_yoy_pp	float	Year-over-year change in the inventories-to-sales ratio, ratio points. The cycle momentum lens behind the destocking/restocking flags. Null for the first 12 months. (unit: ratio points)
is_ratio_12m_ma	float	Trailing-12-month average of the inventories-to-sales ratio. Null for the first 11 months. (unit: ratio, SA)
is_ratio_vs_12m_ma_pp	float	Deviation of the inventories-to-sales ratio from its trailing-12-month average, ratio points. Positive = ratio running hot vs the recent norm. Null for the first 11 months. (unit: ratio points)
overhang_score	float	0-100 inventory-overhang score: the look-ahead-free percentile of the current inventories-to-sales ratio within its trailing-36-month window (min 12 obs). 100 = the heaviest overhang in 3 years; 0 = the leanest. Drives overhang_regime. (unit: 0-100 score)
overhang_regime	string	Documented overhang regime from the score: lean (<25), balanced (25-50), building (50-75), overhang (>=75). Null while the score is null. (unit: string)
destocking_flag	integer	1 when the inventories-to-sales ratio fell >= 0.05 ratio points year over year — demand clearing shelves faster than supply replenishes (restock-order signal). 0/1 integer. (unit: 0/1 flag)
restocking_flag	integer	1 when the inventories-to-sales ratio rose >= 0.05 ratio points year over year — stock accumulating faster than sales (overhang precursor: discounting and order cutbacks ahead). 0/1 integer. (unit: 0/1 flag)
row_hash	string	Deterministic sha256[:16] over date\|inv_total_musd\|is_ratio; stable across re-ingests of identical upstream data, changes on any revision. (unit: string)

First 10 sample rows — a preview, not the complete dataset.

date	year_month	country	country_code	inv_total_musd	is_ratio	sales_total_musd	inv_mom_pct	inv_yoy_pct	sales_yoy_pct	is_ratio_mom_pp	is_ratio_yoy_pp	is_ratio_12m_ma	is_ratio_vs_12m_ma_pp	overhang_score	overhang_regime	row_hash
1992-01-01	1992-01	United States	USA	800,927	1.56	513,414.744	—	—	—	—	—	—	—	—	—	92a5c9248f529ada
1992-02-01	1992-02	United States	USA	802,413	1.56	514,367.308	0.186	—	—	0	—	—	—	—	—	a24df52427f0b7a5
1992-03-01	1992-03	United States	USA	806,267	1.53	526,971.895	0.48	—	—	-0.03	—	—	—	—	—	8fd957be6497e7d1
1992-04-01	1992-04	United States	USA	808,718	1.53	528,573.856	0.304	—	—	0	—	—	—	—	—	36f01765f7e0d098
1992-05-01	1992-05	United States	USA	808,186	1.52	531,701.316	-0.066	—	—	-0.01	—	—	—	—	—	6675803efc19b6e4
1992-06-01	1992-06	United States	USA	813,573	1.52	535,245.395	0.667	—	—	0	—	—	—	—	—	96db30c7236fae91
1992-07-01	1992-07	United States	USA	817,069	1.51	541,105.298	0.43	—	—	-0.01	—	—	—	—	—	55dda5b7441ba005
1992-08-01	1992-08	United States	USA	819,296	1.53	535,487.582	0.273	—	—	0.02	—	—	—	—	—	ff5b0035a1683a9f
1992-09-01	1992-09	United States	USA	820,056	1.51	543,083.444	0.093	—	—	-0.02	—	—	—	—	—	9889f76773e24fec
1992-10-01	1992-10	United States	USA	820,439	1.51	543,337.086	0.047	—	—	0	—	—	—	—	—	1ccf9384f0d0b34a

Profiled Oct 1, 2026 from snapshot 20261001T154945Z-dc53f8827c79

Measured

Completeness: 98.4%
Rows: 415
Columns: 19
Columns with gaps: 9

Rows per yeardate · 1992–2026 · 35 years

19922026

Column	Missing	Distinct	Range	Distribution
datedate	0%	385	Jan 1, 1992 → Jul 1, 2026	—
year_monthvarchar	0%	408	—	1992-011 1992-021 1992-031
countryvarchar	0%	1	—	United States415
country_codevarchar	0%	1	—	USA415
inv_total_musddouble	0%	433	800,927 → 2,764,708median 1,433,845	10 outside 1st–99th percentile
is_ratiodouble	0%	29	1.24 → 1.74median 1.38	5 outside 1st–99th percentile
sales_total_musddouble	0%	339	513,415 → 2,140,002median 1,108,680	10 outside 1st–99th percentile
inv_mom_pctdouble	0.24%	396	-2.34 → 2.57median 0.3079	10 outside 1st–99th percentile
inv_yoy_pctdouble	2.9%	380	-14.15 → 21.62median 3.64	10 outside 1st–99th percentile
sales_yoy_pctdouble	2.9%	432	-20.64 → 41.32median 4.61	10 outside 1st–99th percentile
is_ratio_mom_ppdouble	0.24%	23	-0.18 → 0.23median 0	10 outside 1st–99th percentile
is_ratio_yoy_ppdouble	2.9%	69	-0.48 → 0.33median -0.01	10 outside 1st–99th percentile
is_ratio_12m_madouble	2.7%	243	1.26 → 1.53median 1.38	8 outside 1st–99th percentile
is_ratio_vs_12m_ma_ppdouble	2.7%	207	-0.12 → 0.2808median -0.0042	10 outside 1st–99th percentile
overhang_scoredouble	8.4%	33	2.78 → 100median 44.44	30 outside 1st–99th percentile
overhang_regimevarchar	8.4%	4	—	overhang127 lean121 balanced80
destocking_flagbigint	0%	2	0 → 1median 0
restocking_flagbigint	0%	2	0 → 1median 0
row_hashvarchar	0%	474	—	004bf0c101f64c391 008dbf9db463ded41 011a76ea66fdfe911

Point any LLM at the metadata endpoint — the documentation above is machine-readable too (JSON-LD + Croissant).

cURL

curl "https://datazimuts.com/v1/datasets/us_inventory_cycle_intel/us_business_inventory_cycle_monthly" | jq '{title, rows, columns_count, license}'

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/us_inventory_cycle_intel/us_business_inventory_cycle_monthly").json()
print(ds["title"], ds["rows"], "rows")

# Sample rows for an LLM context window
for row in ds.get("sample_rows", [])[:5]:
    print(row)

API endpoint: https://datazimuts.com/v1/datasets/us_inventory_cycle_intel/us_business_inventory_cycle_monthly

Tip: fetch /llms.txt for the full machine-readable catalog.

Cite this snapshot

Pinned to snapshot 20261001T154945Z-dc53f8827c79 and its content hash, so readers get exactly the data you used.

APA

US Business-Inventory-Cycle Intelligence (Census MTIS via FRED, keyless). (2026). US business inventory cycle: inventories-to-sales panel (monthly) [Data set, snapshot 20261001T154945Z-dc53f8827c79, sha256 dc53f8827c79]. Datazimuts. Retrieved 2026-10-02, from https://datazimuts.com/en/datasets/us_inventory_cycle_intel/us_business_inventory_cycle_monthly?snapshot=20261001T154945Z-dc53f8827c79

BibTeX

@misc{dz_us_inventory_cycle_intel_us_business_inv_dc53f882,
  title        = {{US business inventory cycle: inventories-to-sales panel (monthly)}},
  author       = {{US Business-Inventory-Cycle Intelligence (Census MTIS via FRED, keyless)}},
  year         = {2026},
  publisher    = {Datazimuts},
  howpublished = {\url{https://datazimuts.com/en/datasets/us_inventory_cycle_intel/us_business_inventory_cycle_monthly?snapshot=20261001T154945Z-dc53f8827c79}},
  note         = {Snapshot 20261001T154945Z-dc53f8827c79, sha256 dc53f8827c79df7a017688d318172865b37dbc3eea59807e5e93f68af195c1c8; accessed 2026-10-02}
}

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US business inventory cycle: inventories-to-sales panel (monthly)

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