Logistics
23 columns
200 rows
8 formats

Retail Inventory Replenishment Cycles

This dataset provides detailed records of inventory replenishment cycles across multiple retail locations, including product, supplier, timing, and stock level information. It enables comprehensive analysis of supply chain efficiency, demand forecasting, and stockout prevention, making it invaluable for retail operations and analytics teams.

last updated 21 Jul 2025
by GoMask

The brief that made it

Optimizing inventory replenishment schedules to reduce stockouts

Sample rows

preview · 8 of 200 rows · all 23 columns

replenishment_cycle_idstring	store_countrystring	lead_time_daysinteger	stockout_occurredboolean	store_statestring	store_idstring	store_namestring	store_citystring	product_idstring	product_namestring	skustring	categorystring	replenishment_order_datedate	replenishment_delivery_datedate	replenishment_quantityinteger	unit_costfloat	total_costfloat	supplier_idstring	supplier_namestring	on_hand_inventory_beforeinteger	on_hand_inventory_afterinteger	replenishment_frequency_daysinteger	notesstring
RCY000001	USA	2	false	IL	S001	Market Square	Chicago	P1001	Organic Whole Milk Gallon	DAIRY-MILK-001	Dairy	2024-03-01	2024-03-03	120	3.85	462	SUP01	Fresh Dairy Farms	34	154	7	Normal weekly replenishment
RCY000002	USA	0	false	TX	S002	Downtown Grocers	Houston	P1002	Banana Bunch	PRODUCE-BNN-002	Produce	2024-03-04	2024-03-04	200	0.29	58	SUP02	Green Harvest	23	223	2	Same-day delivery
RCY000003	USA	0	false	AZ	S003	Hillside Market	Phoenix	P1003	Sourdough Bread Loaf	BAKERY-SD-003	Bakery	2024-03-05	2024-03-05	70	2.15	150.5	SUP03	Sunrise Bakery	12	82	3	Normal replenishment
RCY000004	Canada	11	true	blank	S004	Maple Supercenter	Toronto	P1004	Aged Cheddar Block	CHEESE-CHED-004	Cheese	2024-02-20	2024-03-02	300	9.45	2835	SUP04	Ontario Dairy Producers	0	300	30	International shipment
RCY000005	USA	1	false	MA	S005	Harbor Foods	Boston	P1005	Almond Milk Unsweetened	DAIRYALT-ALM-005	Dairy Alternatives	2024-03-06	2024-03-07	60	2.95	177	SUP05	Pure Plant Drinks	15	75	5	Increased demand
RCY000006	USA	2	false	CA	S006	Sunset Mart	San Diego	P1006	Orange Juice 1L	BEV-OJ-006	Beverages	2024-03-02	2024-03-04	190	2.09	397.1	SUP06	Citrus Valley	21	211	6	Normal replenishment
RCY000007	USA	0	false	FL	S007	Lakeside Grocers	Orlando	P1007	Greek Yogurt Plain	DAIRY-YGRT-007	Dairy	2024-03-01	2024-03-01	75	1.65	123.75	SUP01	Fresh Dairy Farms	9	84	3	Same-day delivery
RCY000008	UK	15	true	blank	S008	Urban Foods	London	P1008	Pantry Baking Powder	PANTRY-BKP-008	Pantry	2024-02-10	2024-02-25	500	1.19	595	SUP07	Sterling Foods Ltd	0	500	90	Annual contract
RCY000009	USA	1	false	TX	S009	MetroMart	Dallas	P1009	Red Apples 3lb Bag	PRODUCE-APP-009	Produce	2024-03-03	2024-03-04	120	2.39	286.8	SUP08	Orchard Select	30	150	7	Normal weekly replenishment
RCY000010	UAE	17	true	blank	S010	Desert Fresh Market	Dubai	P1010	Camel Milk Quart	DAIRY-CAML-010	Dairy	2024-02-15	2024-03-03	250	8.95	2237.5	SUP09	Emirates Dairy Co.	0	250	60	International shipment
RCY000011	USA	1	false	TX	S002	Downtown Grocers	Houston	P1011	Cola 12-pack	BEV-COLA-011	Beverages	2024-03-09	2024-03-10	90	4.5	405	SUP10	RefreshCo	15	105	4	Increased demand
RCY000012	USA	1	false	MA	S011	Harbor Foods	Boston	P1012	Salted Butter Sticks	DAIRY-BUT-012	Dairy	2024-03-08	2024-03-09	110	2.99	328.9	SUP01	Fresh Dairy Farms	13	123	7	Normal replenishment
RCY000013	USA	0	false	CA	S012	Bayview Foods	San Francisco	P1013	Frozen Peas 500g	FROZEN-PEA-013	Frozen	2024-03-07	2024-03-07	80	1.49	119.2	SUP11	Northern Freezers	17	97	4	Same-day delivery
RCY000014	USA	8	false	FL	S013	Seaside Market	Miami	P1014	Chocolate Bar 100g	CONFECTION-CHO-014	Confectionery	2024-03-02	2024-03-10	1000	1.09	1090	SUP12	Candyland Inc.	90	1090	28	Bulk replenishment after holiday sale
RCY000015	Australia	16	true	blank	S014	Outback Grocers	Sydney	P1015	Macadamia Nut Butter	PANTRY-MNB-015	Pantry	2024-02-12	2024-02-28	400	8.75	3500	SUP13	Down Under Foods	2	402	90	International shipment
RCY000016	South Africa	15	true	blank	S015	blank	Cape Town	P1016	Biltong Snack Pack	SNACKS-BLTG-016	Snacks	2024-02-05	2024-02-20	500	2.19	1095	SUP14	Cape Meats Co.	0	500	60	International shipment
RCY000017	UK	13	true	blank	S016	Urban Foods	London	P1017	Earl Grey Tea Bags	BEV-TEA-017	Beverages	2024-02-20	2024-03-04	250	3.25	812.5	SUP15	London Tea Merchants	0	250	30	International shipment
RCY000018	USA	0	false	IL	S017	Market Square	Chicago	P1018	White Sandwich Bread	BAKERY-WHT-018	Bakery	2024-03-10	2024-03-10	95	2.09	198.55	SUP03	Sunrise Bakery	17	112	2	Normal replenishment
RCY000019	USA	1	false	MA	S018	Harbor Foods	Boston	P1019	All-Purpose Cleaner	CLEAN-APC-019	Cleaning	2024-03-06	2024-03-07	300	3.29	987	SUP16	Household Essentials	25	325	5	Spike due to local outbreak
RCY000020	USA	2	false	CA	S019	Sunset Mart	San Diego	P1020	Sparkling Water 6-pack	BEV-SPW-020	Beverages	2024-03-03	2024-03-05	120	4.25	510	SUP10	RefreshCo	19	139	6	Normal replenishment

What the 200 rows show

from the 200-row sample

USA (store country) stands out: mean lead_time_days is 0.94, against 13.9 for the rest.

25%stockout_occurred = true
1median lead_time_days
12categories
31suppliers
41store cities
47stores

Mean lead_time_days by store_country200 rows

lead_time_days200 rows, in bands of 2

Median 1, from 0 to 20.

store_state143 rows with a value · 57 left blank

CA26
TX23
FL21
IL13
AZ13
CO11
MA10
OR6
GA5
MO4

23 columns by typefrom the column list below

string 13
integer 5
float 2
date 2
boolean 1

Columns

23 columns in four groups

blueprint · 23 columns
column	type	description	example
Text 13 columns
`replenishment_cycle_id`	string	Unique identifier for each inventory replenishment cycle recordunique	RCY000001
`store_id`	string	Unique identifier for the retail store or location	S001
`store_name`	string	Name of the retail store or locationoptional	Market Square
`store_city`	string	City where the store is locatedoptional	Chicago
`store_state`	string	State or province where the store is locatedoptional	IL
`store_country`	string	Country where the store is located6 countries · optional	USA
`product_id`	string	Unique identifier for the product being replenished	P1001
`product_name`	string	Name or description of the productoptional	Banana Bunch
`sku`	string	Stock Keeping Unit identifier for the productoptional	DAIRY-MILK-001
`category`	string	Product category or department12 categories · optional	Dairy
`supplier_id`	string	Unique identifier for the supplier or vendoroptional	SUP01
`supplier_name`	string	Name of the supplier or vendoroptional	Fresh Dairy Farms
`notes`	string	Additional notes or comments about the replenishment cycleoptional	Same-day delivery
Numbers 7 columns
`replenishment_quantity`	integer	Number of product units delivered in this replenishment cycle1 or more	120
`unit_cost`	float	Cost per unit of the product at the time of replenishment0 or more · optional	3.85
`total_cost`	float	Total cost for this replenishment cycle (unit_cost * replenishment_quantity)0 or more · optional	462
`on_hand_inventory_before`	integer	Units of product in stock at the store before the replenishment delivery0 or more · optional	34
`on_hand_inventory_after`	integer	Units of product in stock at the store after the replenishment delivery0 or more · optional	154
`lead_time_days`	integer	Number of days between order placement and delivery (replenishment_delivery_date - replenishment_order_date)0 or more · optional	2
`replenishment_frequency_days`	integer	Number of days since the previous replenishment for this product at this store0 or more · optional	7
Dates and times 2 columns
`replenishment_order_date`	date	Date when the replenishment order was placed	2024-03-01
`replenishment_delivery_date`	date	Date when the replenishment stock was delivered to the store	2024-03-03
True or false 1 column
`stockout_occurred`	boolean	Indicates if a stockout occurred before this replenishment (true/false)optional	false

Use it for

A logistics dashboard
The stockout_occurred rate, lead_time_days by store_country and a breakdown of store_state. Excel, Power BI or Tableau.
Why do the 143 USA rows have a mean lead_time_days of 0.94?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Replenishment cycles200
RCY0000012USA
RCY00000411Canada
RCY00000815UK
A software demo
Believable replenishment cycles with store_id, store_name and store_city to fill a screen in front of a buyer.

This dataset200 rows23 columns

Yours1,000 rows23 columnsstore_city: UK only

blueprint · retail-inventory-replenishment-cycles

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 23 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

Record per product, store, and cycle.
Replenishment date, quantity, and supplier.
Stockout event flagged.
Historic sales trend merged.

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: retail-inventory-replenishment-cycles

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?