Canadian manufacturing sales intelligence (monthly)

Name: Canadian manufacturing sales intelligence (monthly)
Creator: Statistics Canada
License: https://www.statcan.gc.ca/en/terms-conditions/open-licence
Keywords: manufacturing, industry, canada, statcan, monthly, signals, inventories, momentum

Commercial use OKFresh

Query in workbench

The full snapshot isn't published yet — check back after the next refresh.Download sample CSV Download sample JSONSample rows only (up to 20) — not the complete dataset.

Monthly Canadian manufacturing intelligence from Statistics Canada's Monthly Survey of Manufacturing (table 16-10-0047-01): sales (shipments) joined with new orders, unfilled orders and total inventories for the headline aggregate, the durable- and non-durable-goods aggregates and the 21 NAICS 3-digit subsectors — Canada-national, seasonally adjusted, thousands of dollars. Momentum (MoM, 3-month-annualized, YoY) on every measure; inventory-to-sales and orders-coverage ratios plus a book-to-bill ratio; 60-month inventory z-scores; a documented 0-100 manufacturing-momentum score with per-month industry ranks and m1-m4 tiers; record, contraction, backlog-surge/drawdown and inventory-glut flags; breadth diffusion; and an additive decomposition of headline sales YoY into subsector contributions (they sum exactly to the published total). Caveats: unfilled orders are sparse for some industries (kept null, never imputed); the latest month is preliminary and may be revised; published about 6 weeks in arrears. Statistics Canada Open Licence (commercial reuse allowed); source: Statistics Canada.

Source: Statistics Canada
Rows: 9,672
Columns: 40
Source cadence: Monthly
Last refreshed: Sep 27, 2026
Theme: economy

Column	Type	Description
month	string	Reference month: first day of the month, ISO date. (unit: ISO date)
country_code	string	ISO alpha-3 country code (CAN for every row). (unit: ISO 3166-1 alpha-3)
industry_code	string	Industry key: 'total' for the headline 'Manufacturing [31-33]' aggregate, 'durable' / 'nondurable' for the durable-goods and non-durable-goods aggregates, else the NAICS 3-digit subsector code. Primary join key with month. (unit: NAICS code)
industry_label	string	Provider-verbatim industry label.
is_headline	integer	1 for the headline aggregate row, 0 for the 23 industry rows. (unit: binary)
industry_group	string	One of total / durable / nondurable / subsector. (unit: categorical)
sales_thousands	float	Sales of goods manufactured (shipments), seasonally adjusted, as published: in thousands of Canadian dollars (multiply by 1,000 for dollar amounts). Non-negative float; genuine upstream gaps are missing, never zero-filled; the latest month is preliminary and may be revised. (unit: thousands of CAD, seasonally adjusted)
new_orders_thousands	float	New orders — estimated values of orders received during the month, seasonally adjusted, as published: in thousands of Canadian dollars. Can be negative upstream when net cancellations exceed bookings (leather [316] was -$18.8M in 2016-10); kept verbatim, never clipped. Same vintage caveats as sales_thousands. (unit: thousands of CAD, seasonally adjusted)
unfilled_orders_thousands	float	Unfilled orders — estimated values of orders at end of month, seasonally adjusted, as published: in thousands of Canadian dollars. Sparse for some industries (beverage & tobacco [312] ends 2006-05; non-metallic mineral products [327] ends 2025-10; several others have interior gaps) — genuine upstream gaps are missing, never imputed. (unit: thousands of CAD, seasonally adjusted)
inventory_thousands	float	Total inventory — estimated values of total inventory at end of the month, seasonally adjusted, as published: in thousands of Canadian dollars. Same vintage caveats as sales_thousands. (unit: thousands of CAD, seasonally adjusted)
inv_sales_ratio	float	Inventory-to-sales ratio computed from the levels: months of supply at the current shipment pace. Median \|computed - StatCan-published\| = 0.0025 over 415 months. Null when sales are null or zero. (unit: ratio)
inv_sales_ratio_statcan	float	Inventory-to-sales ratio as published by StatCan (rounded to 2 decimals); diagnostic cross-check, not used in any gate. (unit: ratio)
orders_coverage_months	float	Unfilled orders divided by sales: months of backlog sitting on the books at the current shipment pace. Null where unfilled orders are unpublished or sales are null/zero. (unit: months)
book_to_bill_ratio	float	New orders divided by sales. Above 1 means the order book is growing faster than shipments; below 1 means shipments are being fed by the backlog. Null when sales are null or zero. (unit: ratio)
sales_mom_pct	float	Month-over-month percent change in sales. (unit: percent)
sales_ann_3m_pct	float	3-month change in sales annualized: ((sales_t / sales_{t-3}) ^ 4 - 1) * 100. (unit: percent, annualized)
sales_yoy_pct	float	Year-over-year percent change in sales. (unit: percent)
new_orders_mom_pct	float	Month-over-month percent change in new orders. (unit: percent)
new_orders_yoy_pct	float	Year-over-year percent change in new orders. (unit: percent)
unfilled_orders_mom_pct	float	Month-over-month percent change in unfilled orders. (unit: percent)
unfilled_orders_yoy_pct	float	Year-over-year percent change in unfilled orders. (unit: percent)
inv_mom_pct	float	Month-over-month percent change in inventories. (unit: percent)
inv_yoy_pct	float	Year-over-year percent change in inventories. (unit: percent)
ratio_z_60m	float	Z-score of inv_sales_ratio against its trailing 60-month mean/std (minimum 24 observations; null when the window std is 0). Positive = inventories elevated relative to the 5-year norm. (unit: z-score)
manufacturing_momentum_score	float	Documented 0-100 composite: 100 * (0.50 * min-max(winsorized sales_yoy_pct, +/-15pp) + 0.50 * min-max(winsorized new_orders_yoy_pct, +/-15pp)), min-maxed within each month across the 21 subsectors. Higher = shipments and new-order demand both running hot. Null for the headline and aggregate rows. (unit: 0-100 score)
momentum_rank	integer	Per-month dense rank of manufacturing_momentum_score across the 21 subsectors (1 = hottest). Null for the headline and aggregate rows. (unit: rank)
momentum_tier	string	Quartile tier of momentum_rank within the month: m1 = ranks 1-6 (top quartile), m2 = 7-11, m3 = 12-16, m4 = 17-21. Null for the headline and aggregate rows. (unit: categorical)
sales_record_12m_flag	integer	1 when sales equal the trailing-12-month maximum (inclusive). (unit: binary)
orders_record_12m_flag	integer	1 when new orders equal the trailing-12-month maximum (inclusive). (unit: binary)
contracting_flag	integer	1 when sales AND new orders are both down more than 5pp YoY. (unit: binary)
backlog_surge_flag	integer	1 when unfilled_orders_yoy_pct exceeds +10% AND new_orders_mom_pct is positive: demand piling up faster than it is being fulfilled. (unit: binary)
backlog_drawdown_flag	integer	1 when unfilled_orders_yoy_pct is below -10% AND sales_yoy_pct is positive: the backlog is being worked off. (unit: binary)
inventory_glut_flag	integer	1 when ratio_z_60m exceeds +1.0 (inventories more than one std above the 5-year norm). (unit: binary)
top_driver_industry	string	NAICS code of the subsector with the largest absolute contribution to headline sales YoY growth that month. Month-level attribute: repeated on every industry row of the month. Deterministic: earliest NAICS code wins absolute ties. (unit: NAICS code)
top_driver_industry_label	string	Provider-verbatim label of the top driver subsector (month-level attribute, repeated on every industry row).
top_driver_contrib_pp	float	The top driver's signed additive contribution to headline sales YoY growth, in percentage points (month-level attribute, repeated on every industry row). (unit: pp)
industry_breadth_share	float	Share of the 21 subsectors with sales YoY growth above zero that month. (unit: share)
as_of	string	Latest reference month present in the fetched panel (YYYY-MM); identical across rows and across runs on the same published vintage, so a re-fetch of unchanged data produces a byte-identical panel. (unit: YYYY-MM)
source_table	string	StatCan product ID behind the row (16100047).
row_hash	string	Deterministic 16-hex row hash of industry_code + month (idempotency).

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

month	country_code	industry_code	industry_label	is_headline	industry_group	sales_thousands	new_orders_thousands	unfilled_orders_thousands	inventory_thousands	inv_sales_ratio	inv_sales_ratio_statcan	orders_coverage_months	book_to_bill_ratio	sales_mom_pct	sales_ann_3m_pct	sales_yoy_pct	new_orders_mom_pct	new_orders_yoy_pct	unfilled_orders_mom_pct	unfilled_orders_yoy_pct	inv_mom_pct	inv_yoy_pct	ratio_z_60m	manufacturing_momentum_score	momentum_rank	momentum_tier	sales_record_12m_flag	orders_record_12m_flag	backlog_surge_flag	backlog_drawdown_flag	top_driver_industry	top_driver_industry_label	top_driver_contrib_pp	industry_breadth_share	as_of	source_table	row_hash
1993-01-01	CAN	total	Manufacturing [31-33]	1	total	24,241,579	24,954,123	21,526,040	34,947,137	1.442	1.44	0.888	1.029	-0.321	2.486	8.017	3.226	11.192	3.423	5.286	-0.798	-2.815	—	—	—	—	0	1	0	0	336	Transportation equipment manufacturing [336]	2.693	0.81	2026-07	16100047	4ea1f5e863d1a32e
1993-01-01	CAN	durable	Durable goods industries	0	durable	13,004,739	13,673,676	19,821,427	20,764,895	1.597	1.6	1.524	1.051	-1.569	4.608	11.042	4.447	16.754	3.493	4.988	-0.394	-1.821	—	—	—	—	0	1	0	0	336	Transportation equipment manufacturing [336]	2.693	0.81	2026-07	16100047	29b8fa8ef4afa013
1993-01-01	CAN	nondurable	Non-durable goods industries	0	nondurable	11,236,840	11,280,447	1,704,613	14,182,242	1.262	1.26	0.152	1.004	1.164	0.096	4.716	1.784	5.123	2.625	8.88	-1.383	-4.235	—	—	—	—	1	1	0	0	336	Transportation equipment manufacturing [336]	2.693	0.81	2026-07	16100047	da263cf9874b6ef6
1993-01-01	CAN	311	Food manufacturing [311]	0	subsector	3,350,457	3,354,721	157,784	3,027,284	0.904	0.9	0.047	1.001	-0.16	5.681	2.989	0.261	3.12	2.777	17.813	-1.79	0.652	—	48.689	12	m3	0	1	1	0	336	Transportation equipment manufacturing [336]	2.693	0.81	2026-07	16100047	0667a228147da20d
1993-01-01	CAN	312	Beverage and tobacco product manufacturing [312]	0	subsector	699,722	699,995	2,063	1,490,558	2.13	2.13	0.003	1	2.478	-8.927	-4.059	2.518	-4.022	15.251	15.251	-0.096	0.253	—	18.297	19	m4	0	0	1	0	336	Transportation equipment manufacturing [336]	2.693	0.81	2026-07	16100047	df384c84844d0bf9
1993-01-01	CAN	313	Textile mills [313]	0	subsector	230,559	227,483	249,162	436,173	1.892	1.89	1.081	0.987	-1.192	2.405	3.489	-1.439	2.109	-1.219	-7.43	-0.108	-2.715	—	48.318	13	m3	0	0	0	0	336	Transportation equipment manufacturing [336]	2.693	0.81	2026-07	16100047	4837bd3c0dcdccf4
1993-01-01	CAN	314	Textile product mills [314]	0	subsector	150,785	145,680	50,640	349,177	2.316	2.32	0.336	0.966	-2.945	-4.713	1.546	-6.281	-1.892	-9.158	-22.291	0.955	-2.391	—	36.553	15	m3	0	0	0	1	336	Transportation equipment manufacturing [336]	2.693	0.81	2026-07	16100047	b229afa2463dc8a8
1993-01-01	CAN	315	Apparel manufacturing [315]	0	subsector	494,450	480,992	294,140	1,150,485	2.327	2.33	0.595	0.973	-2.171	2.51	-0.55	-6.895	-3.257	-4.375	-10.235	0.267	-4.329	—	28.777	17	m4	0	0	0	0	336	Transportation equipment manufacturing [336]	2.693	0.81	2026-07	16100047	095a8b6f107ebbc6
1993-01-01	CAN	316	Leather and allied product manufacturing [316]	0	subsector	73,742	61,366	104,730	207,086	2.808	2.81	1.42	0.832	-3.274	-0.384	-0.444	-14.151	-17.152	-10.568	22.727	-4.061	-7.372	—	9.484	20	m4	0	0	0	0	336	Transportation equipment manufacturing [336]	2.693	0.81	2026-07	16100047	d31bde675e49d6de
1993-01-01	CAN	321	Wood product manufacturing [321]	0	subsector	1,400,009	1,386,484	220,892	2,251,238	1.608	1.61	0.158	0.99	4.351	40.352	30.947	3.364	29.682	-5.77	-5.832	-2.677	3.183	—	100	1	m1	1	1	0	0	336	Transportation equipment manufacturing [336]	2.693	0.81	2026-07	16100047	ca7045fa3243eea5

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

cURL

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

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/statcan_mfg_sales_intel/ca_mfg_sales_intel_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/statcan_mfg_sales_intel/ca_mfg_sales_intel_monthly

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

Cite this snapshot

Pinned to snapshot 20260927T094832Z-6fba41d56d43 and its content hash, so readers get exactly the data you used.

APA

Statistics Canada. (2026). Canadian manufacturing sales intelligence (monthly) [Data set, snapshot 20260927T094832Z-6fba41d56d43, sha256 6fba41d56d43]. Datazimuts. Retrieved 2026-09-27, from https://datazimuts.com/en/datasets/statcan_mfg_sales_intel/ca_mfg_sales_intel_monthly?snapshot=20260927T094832Z-6fba41d56d43

BibTeX

@misc{dz_statcan_mfg_sales_intel_ca_mfg_sales_int_6fba41d5,
  title        = {{Canadian manufacturing sales intelligence (monthly)}},
  author       = {{Statistics Canada}},
  year         = {2026},
  publisher    = {Datazimuts},
  howpublished = {\url{https://datazimuts.com/en/datasets/statcan_mfg_sales_intel/ca_mfg_sales_intel_monthly?snapshot=20260927T094832Z-6fba41d56d43}},
  note         = {Snapshot 20260927T094832Z-6fba41d56d43, sha256 6fba41d56d43e99cc0b9de17cbd9e2af4d4924a76d43817d0e513f79c3774f75; accessed 2026-09-27}
}

Embed a table or a chart

Paste this into any page. The embed is pinned to the same snapshot, follows the reader's light or dark setting, and always shows the source, license and a link back.

Type

X axis

Value

Aggregate

Theme

HTML

<iframe src="https://datazimuts.com/embed/chart?dataset=statcan_mfg_sales_intel%2Fca_mfg_sales_intel_monthly&lang=en&theme=auto&snapshot=20260927T094832Z-6fba41d56d43&x=month&y=is_headline&agg=avg" title="Canadian manufacturing sales intelligence (monthly)" width="100%" height="380" style="border:0" loading="lazy"></iframe>

Canadian manufacturing sales intelligence (monthly)

Follow this dataset

Cite this snapshot

Embed a table or a chart

Use your own AI key