Manufacturing
15 columns
200 rows
8 formats

Manufacturing Quality Control Dataset

This dataset provides detailed manufacturing quality control records, including batch production information, inspection results, defect types and severities, and quality scores. It enables manufacturers to monitor process performance, identify recurring issues, and drive continuous improvement in product quality and operational efficiency.

opened 132 times
last updated 20 Aug 2025
by GoMask

The brief that made it

Root-cause analysis of production defects

Sample rows

preview · 8 of 200 rows · all 15 columns

batch_idstring	shiftstring	quality_scorefloat	rework_requiredboolean	defect_typestring	production_datedate	product_codestring	operator_idstring	inspection_idstring	inspection_datedatetime	inspector_idstring	defect_severityinteger	defect_countinteger	factory_locationstring	commentsstring
A1234-20240601	afternoon	99.7	false	blank	2024-06-01	PRD-02	OP-5691	INSP-0001	2024-06-01T15:22:17	INS-2231	blank	0	Factory-E1	No issues detected. Batch meets all quality benchmarks.
B0876-20240602	morning	87.4	false	blank	2024-06-02	PRD-03	OP-1674	INSP-0002	2024-06-02T09:18:43	INS-9052	blank	0	Factory-Y7	Perfect batch. No defects observed.
C4218-20240603	night	81.1	false	scratch	2024-06-03	PRD-01	OP-2517	INSP-0003	2024-06-04T00:43:11	INS-0342	1	1	Factory-Z3	Minor scratch detected but within tolerance.
D3127-20240603	afternoon	94.3	false	blank	2024-06-03	PRD-04	OP-0033	INSP-0004	2024-06-03T16:08:29	INS-5961	blank	0	Factory-X0	Batch exceeded benchmarks.
E2098-20240604	morning	77.8	false	marking	2024-06-04	PRD-02	OP-5691	INSP-0005	2024-06-04T08:42:14	INS-9052	2	2	Factory-G9	Spot marking observed. No rework required.
F5921-20240605	afternoon	61.4	true	misalignment	2024-06-05	PRD-03	OP-7777	INSP-0006	2024-06-05T15:17:05	INS-2178	3	7	Factory-A2	Batch failed due to misalignment. Requires full rework.
G7423-20240606	night	59.2	true	contamination	2024-06-06	PRD-02	OP-0033	INSP-0007	2024-06-07T01:02:39	INS-0342	3	5	Factory-X0	Contamination detected. Rework required.
H9305-20240607	morning	69.8	true	mislabel	2024-06-07	PRD-04	OP-4178	INSP-0008	2024-06-07T10:40:15	INS-2231	2	3	Factory-Y7	Mislabel found, batch flagged for rework.
J4207-20240608	afternoon	100	false	blank	2024-06-08	PRD-01	OP-2517	INSP-0009	2024-06-08T14:55:21	INS-9052	blank	0	Factory-A2	No defects. Outstanding product.
K5182-20240609	night	3.2	true	major_crack	2024-06-09	PRD-02	OP-2517	INSP-0010	2024-06-10T00:31:46	INS-0342	4	17	Factory-E1	Complete failure. Batch must be discarded.
L1096-20240610	afternoon	93.8	false	blank	2024-06-10	PRD-03	OP-0033	INSP-0011	2024-06-10T16:11:34	INS-2178	blank	0	Factory-G9	Outstanding batch. No deviations.
M8315-20240611	morning	85	false	blank	2024-06-11	PRD-04	OP-5691	INSP-0012	2024-06-11T11:23:47	INS-2231	blank	0	Factory-Y7	Batch passed all tests.
N5012-20240612	afternoon	76.4	false	dent	2024-06-12	PRD-01	OP-2186	INSP-0013	2024-06-12T15:03:58	INS-9052	2	1	Factory-G9	Minor dent observed, no impact on function.
O1238-20240613	night	41.9	true	cracking	2024-06-13	PRD-02	OP-2517	INSP-0014	2024-06-14T02:12:38	INS-0342	4	15	Factory-Z3	Cracking detected, batch failed.
P8762-20240614	morning	98.2	false	blank	2024-06-14	PRD-03	OP-0112	INSP-0015	2024-06-14T09:36:51	INS-5961	blank	0	Factory-X0	No issues detected.
Q2110-20240615	afternoon	72	false	discoloration	2024-06-15	PRD-01	OP-3452	INSP-0016	2024-06-15T16:21:07	INS-9052	1	1	Factory-G9	Minor discoloration. Within acceptable limits.
R5203-20240616	night	69.5	true	contamination	2024-06-16	PRD-02	OP-2186	INSP-0017	2024-06-17T01:19:53	INS-0342	3	8	Factory-A2	Contamination. Full rework required.
S1542-20240617	afternoon	100	false	blank	2024-06-17	PRD-04	OP-0033	INSP-0018	2024-06-17T15:46:39	INS-2178	blank	0	Factory-G9	No defects found. Exceptional quality.
T7612-20240618	morning	99.9	false	blank	2024-06-18	PRD-03	OP-4178	INSP-0019	2024-06-18T10:15:04	INS-5961	blank	0	Factory-Y7	Batch meets highest standards.
U6045-20240619	afternoon	82.3	false	scratch	2024-06-19	PRD-01	OP-0001	INSP-0020	2024-06-19T15:02:12	INS-2231	1	2	Factory-E1	Light scratch detected, no action needed.

What the 200 rows show

from the 200-row sample

Night shift stands out: 47 of its 52 rows have rework_required = true, against 13 of 148 for the rest.

30%rework_required = true
81.2median quality_score
4product codes
6factory locations
10operators
27inspectors

Rework required rate by shiftrework_required = true

Night shift's mean quality_score is 49.0, against 89.9 for morning and 81.0 for afternoon.

quality_score200 rows, in bands of 10

Every row with quality_score under 70 has rework_required = true. None above it.

defect_type111 rows with a value · 89 left blank

scratch15
contamination14
cracking14
misalignment12
marking11
major_crack11
discoloration10
mislabel9
dent9
foreign_object6

15 columns by typefrom the column list below

string 9
integer 2
float 1
date 1
datetime 1
boolean 1

Columns

15 columns in four groups

blueprint · 15 columns
column	type	description	example
Text 9 columns
`batch_id`	string	Unique identifier for the production batchunique	A1234-20240601
`product_code`	string	Code identifying the product type manufactured in the batch4 codes	PRD-02
`shift`	string	Production shift during which the batch was manufacturedmorning · afternoon · night	afternoon
`operator_id`	string	Unique identifier for the operator responsible for the batch10 operators	OP-5691
`inspection_id`	string	Unique identifier for the inspection recordunique	INSP-0001
`inspector_id`	string	Unique identifier for the inspector who performed the inspection	INS-2231
`defect_type`	string	Type of defect found during inspection (if any)10 types · optional	scratch
`comments`	string	Additional comments or notes from the inspectoroptional	Batch passed all tests.
`factory_location`	string	Name or code of the factory where the batch was produced6 locations	Factory-E1
Numbers 3 columns
`quality_score`	float	Overall quality score assigned to the batch during inspection (0-100)0 to 100	99.7
`defect_severity`	integer	Severity level of the defect (1=minor, 2=moderate, 3=major, 4=critical)1 to 4 · optional	1
`defect_count`	integer	Number of defects of the specified type found in the batch0 or more · optional	0
Dates and times 2 columns
`production_date`	date	Date when the batch was produced	2024-06-01
`inspection_date`	datetime	Date and time when the inspection was performed	2024-06-01T15:22:17
True or false 1 column
`rework_required`	boolean	Indicates if the batch requires rework due to defects	false

Use it for

A manufacturing dashboard
The rework_required rate, quality_score by shift and a breakdown of defect_type. Excel, Power BI or Tableau.
Why do 60 of 200 rows have rework_required = true?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Batches200
A1234-2024060199.7afternoon
F5921-2024060561.4afternoon
G7423-2024060659.2night
A software demo
Believable batches with production_date, product_code and shift to fill a screen in front of a buyer.

This dataset200 rows15 columns

Yours10,000 rows15 columnsfactory_location: UK only

blueprint · manufacturing-quality-control-dataset

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 15 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

Batch and product info logged
Inspection date and type recorded
Defect types annotated
Quality score assigned per batch

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

Open the blueprint in Data Factory

1 credit per row. New accounts start with 25 free credits.

Exports: CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence: yours to use, including commercially
API slug: manufacturing-quality-control-dataset

What should your data show?

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Sample rows

What the 200 rows show

Columns

Use it for

A manufacturing dashboard

A root-cause class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Manufacturing

What should your data show?