Logistics
21 columns
200 rows
8 formats

Warehouse Inventory Turnover Rates

This dataset provides detailed, period-based inventory turnover metrics for each product in every warehouse, including quantities, turnover rates, and inventory movement. It enables granular analysis to optimize stock levels, reduce holding costs, and improve supply chain efficiency. The dataset is ideal for operational analytics, forecasting, and strategic inventory management.

last updated 21 Jul 2025
by GoMask

The brief that made it

Warehouse inventory optimization and stock level planning

Sample rows

preview · 8 of 200 rows · all 21 columns

warehouse_idstring	product_categorystring	beginning_inventory_quantityinteger	warehouse_countrystring	warehouse_namestring	warehouse_street_addressstring	warehouse_citystring	warehouse_statestring	warehouse_postal_codestring	product_idstring	product_namestring	skustring	inventory_period_start_datedate	inventory_period_end_datedate	ending_inventory_quantityinteger	units_receivedinteger	units_soldinteger	average_inventory_quantityfloat	turnover_ratefloat	turnover_daysfloat	last_updateddatetime
WH001	Widgets	8500	USA	Northwest Fulfillment Center	1234 Industrial Dr	Seattle	WA	98109	P1001	Standard Widget	STD-WID-1001	2023-01-01	2023-01-31	3500	3000	8000	6000	1.33	23.3	2023-02-01T10:12:34
WH002	Gadgets	4800	USA	Atlanta Metro Warehouse	245 Peachtree Blvd	Atlanta	GA	30305	P1002	Deluxe Gadget	DLX-GAD-2002	2023-02-01	2023-02-28	1200	3000	6600	3000	2.2	12.7	2023-03-01T09:21:11
WH003	Widgets	7200	USA	Los Angeles Central DC	8765 Alameda St	Los Angeles	CA	90001	P1003	Economy Widget	ECN-WID-3003	2023-03-01	2023-03-31	2600	2000	6600	4900	1.35	22.9	2023-04-01T08:17:48
WH004	Sensors	1200	USA	Chicago Suburban Facility	9012 Wabash Ave	Naperville	IL	60540	P1004	Pro Sensor	PRO-SEN-4004	2023-04-01	2023-04-30	700	1000	1500	950	1.58	19	2023-05-01T11:10:57
WH005	Prototypes	700	USA	Dallas East Warehouse	2001 Main St	Dallas	TX	75201	P1005	Mini Prototype	MIN-PRT-5005	2023-05-01	2023-05-31	250	500	950	475	2	15.5	2023-06-01T08:59:34
WH006	Widgets	9500	USA	South Florida Depot	6543 Palm Ave	Miami	FL	33101	P1006	Ultra Widget	ULT-WID-6006	2023-06-01	2023-06-30	5000	2000	6500	7250	0.9	33.3	2023-07-01T13:40:49
WH007	Gadgets	6300	USA	Rocky Mountain Hub	777 Aspen Rd	Denver	CO	80203	P1007	Gadget Max	GAD-MAX-7007	2023-07-01	2023-07-31	1800	2500	7000	4050	1.73	17.9	2023-08-01T09:22:05
WH008	Widgets	4100	USA	Boston Area Distribution	2222 Charles St	Cambridge	MA	02139	P1008	Widget Mini	WID-MIN-8008	2023-08-01	2023-08-31	1500	1500	4100	2800	1.46	21.2	2023-09-01T14:47:19
WH009	Prototypes	0	USA	Cleveland Rapid Fulfillment	6789 Lakeview Dr	Cleveland	OH	44101	P1009	Prototype Alpha	blank	2023-09-01	2023-09-30	0	0	0	0	0	0	2023-09-30T17:01:12
WH010	Gadgets	5200	USA	San Francisco Tech Depot	500 Market St	San Francisco	CA	94111	P1010	Gadget Ultra	GAD-ULT-1010	2023-10-01	2023-10-31	1300	2000	5900	3250	1.82	17	2023-11-01T08:29:47
WH011	Widgets	5	Switzerland	Zurich Logistics Center	11 Bahnhofstrasse	Zurich	ZH	8001	P1011	Swiss Widget	SWI-WID-1011	2023-03-01	2023-03-31	2	3	6	3.5	1.71	18.1	2023-03-31T19:55:32
WH012	Sensors	8	Japan	Tokyo Industrial Hub	4-1 Marunouchi	Tokyo	TYO	100-0005	P1012	Sensor Prime	SEN-PRI-1012	2023-04-01	2023-04-30	3	5	10	5.5	1.82	16.5	2023-05-01T11:41:12
WH013	Machinery	4	Australia	Sydney Outbound Facility	22 George St	Sydney	NSW	2000	P1013	Machinery Core	MAC-COR-1013	2023-05-01	2023-05-15	2	1	3	3	1	15	2023-05-16T07:15:29
WH014	Gadgets	10	UK	London Thames Warehouse	10 Riverbank Rd	London	LND	SW8 2LF	P1014	UK Gadget Pro	UKG-GAD-1014	2023-06-01	2023-06-15	1	0	9	5.5	1.64	9.1	2023-06-16T12:22:53
WH015	Widgets	7	Mexico	Mexico City Hub	99 Reforma	Mexico City	CDMX	06600	P1015	Latin Widget	LAT-WID-1015	2023-07-01	2023-07-31	0	1	8	3.5	2.29	13.5	2023-08-01T09:06:10
WH016	Sensors	5	Hong Kong	Hong Kong Central DC	8 Queen's Road	Hong Kong	HK	999077	P1016	Asia Sensor	ASI-SEN-1016	2023-09-01	2023-09-15	3	2	4	4	1	15	2023-09-16T10:12:54
WH017	blank	0	Noland	Test Null Facility	NA	Nullville	NA	00000	P1017	Null Prototype	blank	2023-10-01	2023-10-02	0	0	0	0	0	0	2023-10-03T10:10:10
WH018	Artifacts	2	Norway	Oslo Innovation Center	3 Karl Johans gate	Oslo	03	0154	P1018	Nordic Artifact	blank	2023-11-01	2023-11-30	2	0	0	2	0	0	2023-12-01T08:33:21
WH019	Widgets	5100	USA	Honolulu Pacific Depot	321 Aloha Ave	Honolulu	HI	96813	P1019	Island Widget	ISL-WID-1019	2023-12-01	2023-12-31	1300	1500	5300	3200	1.66	18.7	2024-01-01T11:58:47
WH020	Machinery	2000	USA	Detroit Midwest DC	555 Woodward Ave	Detroit	MI	48226	P1020	Machinery Max	MAC-MAX-1020	2024-01-01	2024-01-31	800	700	1900	1400	1.36	22.8	2024-02-01T13:15:17

What the 200 rows show

from the 200-row sample

Widgets (product category) stands out: mean beginning_inventory_quantity is 3,493, against 1,300 for the rest.

1,200median beginning_inventory_quantity
54warehouse states
400median ending_inventory_quantity
500median units_received
1,100median units_sold
925.0median average_inventory_quantity

Mean beginning_inventory_quantity by product_category183 rows

beginning_inventory_quantity200 rows, in bands of 1,000

Median 1,200, from 0 to 10,000.

warehouse_country200 rows · top 10 of 16 values

USA136
Australia7
UK7
Switzerland6
Japan6
Mexico6
Canada6
Hong Kong5
Noland5
Norway5

21 columns by typefrom the column list below

string 11
integer 4
float 3
date 2
datetime 1

Columns

21 columns in three groups

blueprint · 21 columns
column	type	description	example
Text 11 columns
`warehouse_id`	string	Unique identifier for the warehouse	WH001
`warehouse_name`	string	Name of the warehouse	Atlanta Metro Warehouse
`warehouse_street_address`	string	Street address of the warehouse	1234 Industrial Dr
`warehouse_city`	string	City where the warehouse is located	Seattle
`warehouse_state`	string	State or province where the warehouse is located	WA
`warehouse_postal_code`	string	Postal or ZIP code of the warehouse	98109
`warehouse_country`	string	Country where the warehouse is located	USA
`product_id`	string	Unique identifier for the product	P1001
`product_name`	string	Name or description of the product	Standard Widget
`product_category`	string	Category or type of the product6 categories · optional	Widgets
`sku`	string	Stock Keeping Unit identifier for the productoptional	STD-WID-1001
Numbers 7 columns
`beginning_inventory_quantity`	integer	Quantity of product in stock at the beginning of the period0 or more	8500
`ending_inventory_quantity`	integer	Quantity of product in stock at the end of the period0 or more	3500
`units_received`	integer	Total units of product received into the warehouse during the period0 or more · optional	3000
`units_sold`	integer	Total units of product sold or shipped from the warehouse during the period0 or more	8000
`average_inventory_quantity`	float	Average inventory quantity for the product during the period0 or more	6000
`turnover_rate`	float	Inventory turnover rate for the product in the warehouse during the period (units_sold / average_inventory_quantity)0 or more	1.33
`turnover_days`	float	Average number of days it takes to sell through the inventory (calculated as period length / turnover_rate)0 or more · optional	23.3
Dates and times 3 columns
`inventory_period_start_date`	date	Start date of the inventory period being measured	2023-01-01
`inventory_period_end_date`	date	End date of the inventory period being measured	2023-01-31
`last_updated`	datetime	Timestamp when this record was last updated	2023-02-01T10:12:34

Use it for

A logistics dashboard
Beginning_inventory_quantity by product_category and a breakdown of warehouse_country. Excel, Power BI or Tableau.
Why do the 81 Widgets rows have a mean beginning_inventory_quantity of 3,493?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Warehouses200
WH0018500Widgets
WH0024800Gadgets
WH0037200Widgets
A software demo
Believable warehouses with warehouse_name, warehouse_street_address and warehouse_city to fill a screen in front of a buyer.

This dataset200 rows21 columns

Yours1,000 rows21 columnswarehouse_street_address: UK only

blueprint · warehouse-inventory-turnover-rates

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 21 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.

Rules it was built with

Monthly turnover per SKU in each warehouse.
Days inventory outstanding calculated.
Warehouse location and type included.
Seasonal variation annotated.

Rows1,000 rows10,000 rows100,000 rows1,000,000 rows

Open the blueprint in Data Factory

1 credit per row. Preview 20 rows free. Buy rows for the full run.

Exports: CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence: yours to use, including commercially
API slug: warehouse-inventory-turnover-rates

What should your data show?

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Sample rows

What the 200 rows show

Columns

Use it for

A logistics dashboard

A root-cause class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Logistics

What should your data show?