Logistics
20 columns
200 rows
8 formats

Supply Chain Inventory Turnover

This dataset provides detailed inventory turnover metrics across suppliers and distribution centers, including product-level tracking, time periods, and cost analysis. It enables logistics managers to monitor stock efficiency, identify excess inventory, and optimize supply chain operations for cost reduction and improved responsiveness.

last updated 20 Aug 2025
by GoMask

The brief that made it

Optimizing inventory levels across multiple distribution centers

Sample rows

preview · 8 of 200 rows · all 20 columns

inventory_turnover_idstring	distribution_center_countrystring	inventory_turnover_ratefloat	excess_inventory_flagboolean	supplier_idstring	supplier_namestring	distribution_center_idstring	distribution_center_namestring	distribution_center_street_addressstring	distribution_center_citystring	distribution_center_statestring	distribution_center_postal_codestring	product_idstring	product_namestring	inventory_period_start_datedate	inventory_period_end_datedate	beginning_inventoryfloat	ending_inventoryfloat	cost_of_goods_soldfloat	notesstring
INVTO-00001	USA	2.44	false	SUP-1001	Eastern Essentials	DC101	Chicago Central Hub	221B Baker St	Chicago	IL	60614	P-10001	Long Grain Rice	2024-01-01	2024-01-31	52000	7700	112000	Bulk shipment aligned with festival season demand spike.
INVTO-00002	Singapore	0.34	true	SUP-1002	Global Imports Ltd.	DC201	Singapore South Distro	19 Changi Lane	Singapore	SG	486820	P-10002	Canned Tuna	2024-02-01	2024-02-28	1300	1800	540	Restock exceeded forecast; flagged for excess review.
INVTO-00003	USA	2.26	false	SUP-1003	Baker’s Best	DC102	Boston North	42 Charles St	Boston	MA	02114	P-10003	Sourdough Bread	2024-03-01	2024-03-07	380	0	860	Promotional batch sold out rapidly in local stores.
INVTO-00004	USA	2.1	false	SUP-1004	Midwest Dairy Co.	DC103	Kansas City Depot	16 Main Ave	Kansas City	MO	64108	P-10004	Whole Milk	2024-04-01	2024-04-21	2400	1600	4200	Steady throughput, minimal spoilage reported.
INVTO-00005	USA	0.25	true	SUP-1005	Northern Produce	DC104	Minneapolis Fresh	100 Lake St	Minneapolis	MN	55401	P-10005	Red Apples	2024-02-15	2024-02-28	1500	1450	370	Low movement post-holiday; flagged for review.
INVTO-00006	Switzerland	0.36	true	SUP-1006	Swiss Specialties AG	DC301	Zurich Distro	8 Bahnhofstr.	Zurich	ZH	8001	P-10006	Emmental Cheese	2024-04-01	2024-04-30	900	1300	420	Restock exceeded demand; excess inventory detected.
INVTO-00007	USA	3.33	false	SUP-1007	Western Grain Group	DC105	Denver West	555 Colfax Ave	Denver	CO	80204	P-10007	Wheat Flour	2024-03-10	2024-04-10	40000	3200	67000	Bulk movement for local bakery contracts.
INVTO-00008	UK	0.14	true	SUP-1008	UK Packaged Goods	DC401	London Storage	44 Piccadilly	London	LDN	W1J 0DS	P-10008	Packaged Biscuits	2024-05-01	2024-05-31	2400	2800	380	Overstocked for summer; excess flagged.
INVTO-00009	USA	2.92	false	SUP-1009	Southern Harvest	DC106	Dallas Main	200 Commerce Rd	Dallas	TX	75201	P-10009	Sweet Corn	2024-06-01	2024-06-07	650	0	1100	High turnover due to summer promotion.
INVTO-00010	USA	2.38	false	SUP-1010	Atlantic Seafood Inc.	DC107	New York City Fish	21 Fulton St	New York	NY	10038	P-10010	Fresh Salmon	2024-07-01	2024-07-08	400	15	920	Rapid sale due to restaurant demand.
INVTO-00011	USA	2.1	false	SUP-1001	Eastern Essentials	DC101	Chicago Central Hub	221B Baker St	Chicago	IL	60614	P-10011	Brown Rice	2024-02-01	2024-02-28	14000	5000	21000	Steady throughput to regional supermarkets.
INVTO-00012	Canada	0.27	true	SUP-1011	Canadian Produce Co.	DC501	Toronto Fresh Center	23 Queen St	Toronto	ON	M5H 2M9	P-10012	Maple Syrup	2024-03-01	2024-03-31	1100	1400	350	Restock for spring season exceeded projections.
INVTO-00013	Singapore	0.31	true	SUP-1002	Global Imports Ltd.	DC201	Singapore South Distro	19 Changi Lane	Singapore	SG	486820	P-10013	Dried Mango	2024-05-01	2024-05-31	900	1200	320	Inventory flagged for restock excess after promotion.
INVTO-00014	USA	2.19	false	SUP-1003	Baker’s Best	DC102	Boston North	42 Charles St	Boston	MA	02114	P-10014	Rye Bread	2024-06-01	2024-06-07	210	0	460	Local event increased sales for the week.
INVTO-00015	USA	2.24	false	SUP-1012	Pacific Seafood	DC108	Seattle Port	500 Pike St	Seattle	WA	98101	P-10015	Crab Legs	2024-08-01	2024-08-14	600	180	1100	High demand during seafood festival.
INVTO-00016	USA	2.13	false	SUP-1013	Fresh Fields	DC109	San Francisco Market	700 Market St	San Francisco	CA	94103	P-10016	Organic Kale	2024-09-01	2024-09-14	400	60	780	Steady supply for local grocers; minimal waste.
INVTO-00017	USA	1.8	false	SUP-1004	Midwest Dairy Co.	DC103	Kansas City Depot	16 Main Ave	Kansas City	MO	64108	P-10017	Greek Yogurt	2024-10-01	2024-10-21	1200	900	1800	Consistent demand with low spoilage.
INVTO-00018	Germany	0.29	true	SUP-1014	Rhine Valley Foods	DC302	Frankfurt Logistics	12 Mainzer Landstr.	Frankfurt	HE	60329	P-10018	Sauerkraut	2024-11-01	2024-11-30	1100	1500	390	Inventory flagged for review after restock.
INVTO-00019	USA	0.3	true	SUP-1005	Northern Produce	DC104	Minneapolis Fresh	100 Lake St	Minneapolis	MN	55401	P-10019	Green Grapes	2024-12-01	2024-12-14	1000	920	280	Low movement post-holiday; flagged for excess.
INVTO-00020	USA	1.96	false	SUP-1015	Rocky Mountain Ranch	DC110	Denver South	77 Market St	Denver	CO	80209	P-10020	Ground Beef	2025-01-01	2025-01-31	3400	800	5100	Steady sales throughout cold season.

What the 200 rows show

from the 200-row sample

USA (distribution center country) stands out: mean inventory_turnover_rate is 2.0, against 0.22 for the rest.

27%excess_inventory_flag = true
1.4median inventory_turnover_rate
48distribution center states
1,175median beginning_inventory
865median ending_inventory
715median cost_of_goods_sold

Mean inventory_turnover_rate by distribution_center_country200 rows

inventory_turnover_rate200 rows, in bands of 2

Median 1.4, from 0.11 to 13.1.

20 columns by typefrom the column list below

string 13
float 4
date 2
boolean 1

Columns

20 columns in four groups

blueprint · 20 columns
column	type	description	example
Text 13 columns
`inventory_turnover_id`	string	Unique identifier for each inventory turnover recordunique	INVTO-00001
`supplier_id`	string	Unique identifier for the supplier	SUP-1001
`supplier_name`	string	Name of the supplier	Eastern Essentials
`distribution_center_id`	string	Unique identifier for the distribution center	DC101
`distribution_center_name`	string	Name of the distribution center	Chicago Central Hub
`distribution_center_street_address`	string	Street address of the distribution centeroptional	221B Baker St
`distribution_center_city`	string	City where the distribution center is locatedoptional	Chicago
`distribution_center_state`	string	State or province of the distribution centeroptional	IL
`distribution_center_postal_code`	string	Postal code of the distribution centeroptional	60614
`distribution_center_country`	string	Country of the distribution center6 countries · optional	USA
`product_id`	string	Unique identifier for the product	P-10001
`product_name`	string	Name of the product	Long Grain Rice
`notes`	string	Additional notes or comments about the inventory turnover recordoptional	Bulk shipment aligned wit…
Numbers 4 columns
`beginning_inventory`	float	Inventory level at the start of the period0 or more	52000
`ending_inventory`	float	Inventory level at the end of the period0 or more	7700
`cost_of_goods_sold`	float	Total cost of goods sold during the period0 or more	112000
`inventory_turnover_rate`	float	Calculated inventory turnover rate for the period0 or more	2.44
Dates and times 2 columns
`inventory_period_start_date`	date	Start date of the inventory measurement period	2024-01-01
`inventory_period_end_date`	date	End date of the inventory measurement period	2024-01-31
True or false 1 column
`excess_inventory_flag`	boolean	Indicates if excess inventory was detected during the periodoptional	false

Use it for

A logistics dashboard
The excess_inventory_flag rate and inventory_turnover_rate by distribution_center_country. Excel, Power BI or Tableau.
Why do the 148 USA rows have a mean inventory_turnover_rate of 2.0?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Inventory turnovers200
INVTO-000012.44USA
INVTO-000020.34Singapore
INVTO-000050.25USA
A software demo
Believable inventory turnovers with supplier_id, supplier_name and distribution_center_id to fill a screen in front of a buyer.

This dataset200 rows20 columns

Yours1,000 rows20 columnsdistribution_center_street_address: UK only

blueprint · supply-chain-inventory-turnover

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 20 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

Track inventory by product and supplier
Calculate turnover period per item
Segment by warehouse location
Flag slow-moving products

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: supply-chain-inventory-turnover

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?