Manufacturing
16 columns
200 rows
8 formats

Manufacturing Quality Control Metrics

This dataset provides detailed records of manufacturing quality control inspections, including defect types, severity, corrective actions, and traceability information. It enables manufacturers to monitor product quality, identify recurring issues, and implement process improvements for higher efficiency and reduced defects. Ideal for quality analytics, compliance reporting, and continuous improvement initiatives.

opened 20 times
last updated 20 Aug 2025
by GoMask

The brief that made it

Monitoring and analyzing product quality trends over time

Sample rows

preview · 8 of 200 rows · all 16 columns

inspection_idstring	defect_severitystring	defect_countinteger	corrective_action_takenboolean	production_line_idstring	inspection_datedate	product_idstring	batch_numberstring	inspector_idstring	defect_typestring	inspection_resultstring	inspection_methodstring	shiftstring	locationstring	remarksstring	corrective_action_descriptionstring
INSP-00001	None	0	false	LINE-01	2024-05-01	PRD-10001	BATCH-A001	EMP-001	None	Pass	Visual	Day	Plant East, Zone 2	No issues detected during routine check.	blank
INSP-00002	Minor	1	false	LINE-02	2024-05-01	PRD-10002	BATCH-B002	EMP-002	Surface	Pass	Visual	Evening	Plant North, Zone 4	Minor scratch noted on casing; within tolerance.	blank
INSP-00003	Major	3	true	LINE-03	2024-05-02	PRD-10003	BATCH-C003	EMP-003	Assembly	Rework Required	Measurement	Night	Plant West, Zone 7	Loose screw detected in multiple units.	Units sent to reassembly for correction of loose screws.
INSP-00004	None	0	false	LINE-04	2024-05-02	PRD-10004	BATCH-D004	EMP-004	None	Pass	Measurement	Day	Plant South, Zone 3	All measurements within tolerance.	blank
INSP-00005	Minor	2	false	LINE-05	2024-05-03	PRD-10005	BATCH-E005	EMP-005	Dimensional	Pass	Measurement	Evening	Plant Central, Zone 5	Slight deviation in width; within acceptable range.	blank
INSP-00006	Critical	12	true	LINE-01	2024-05-03	PRD-10006	BATCH-F006	EMP-006	Functional	Fail	Functional Test	Night	Plant East, Zone 8	Multiple units failed power-on test.	Batch destroyed and supplier notified due to critical failures.
INSP-00007	Minor	1	false	LINE-02	2024-05-04	PRD-10007	BATCH-G007	EMP-007	Surface	Pass	Visual	Day	Plant West, Zone 1	Surface blemish detected on one unit.	blank
INSP-00008	None	0	false	LINE-03	2024-05-04	PRD-10008	BATCH-H008	EMP-008	None	Pass	Automated	Evening	Plant North, Zone 9	Inspection completed, no defects found.	blank
INSP-00009	Major	4	true	LINE-04	2024-05-05	PRD-10009	BATCH-I009	EMP-009	Dimensional	Rework Required	Measurement	Night	Plant South, Zone 2	Units outside dimensional spec, calibration checked.	Measures recalibrated and batch sent for rework.
INSP-00010	Minor	1	false	LINE-05	2024-05-05	PRD-10010	BATCH-J010	EMP-010	Assembly	Pass	Visual	Day	Plant Central, Zone 10	Assembly issue fixed on spot.	blank
INSP-00011	None	0	false	LINE-01	2024-05-06	PRD-10011	BATCH-A011	EMP-011	None	Pass	Measurement	Evening	Plant West, Zone 5	Routine check, all parameters normal.	blank
INSP-00012	Minor	2	false	LINE-02	2024-05-06	PRD-10012	BATCH-B012	EMP-012	Dimensional	Pass	Measurement	Night	Plant North, Zone 4	Minor deviation in length observed.	blank
INSP-00013	Major	5	true	LINE-03	2024-05-07	PRD-10013	BATCH-C013	EMP-013	Functional	Rework Required	Functional Test	Day	Plant South, Zone 6	Intermittent power observed on several units.	Units sent for repair of power circuitry.
INSP-00014	None	0	false	LINE-04	2024-05-07	PRD-10014	BATCH-D014	EMP-014	None	Pass	Automated	Evening	Plant East, Zone 7	All systems tested, no faults detected.	blank
INSP-00015	Critical	18	true	LINE-05	2024-05-08	PRD-10015	BATCH-E015	EMP-015	Other	Fail	Other	Night	Plant West, Zone 3	Foreign material found in multiple units.	Production halted and batch destroyed due to contamination.
INSP-00016	None	0	false	LINE-01	2024-05-08	PRD-10016	BATCH-F016	EMP-016	None	Pass	Automated	Day	Plant North, Zone 1	Automated inspection, all readings within limits.	blank
INSP-00017	Major	3	true	LINE-02	2024-05-09	PRD-10017	BATCH-G017	EMP-017	Surface	Rework Required	Visual	Evening	Plant South, Zone 5	Multiple units with paint blisters.	Surface refinishing performed on affected units.
INSP-00018	Minor	1	false	LINE-03	2024-05-09	PRD-10018	BATCH-H018	EMP-018	Functional	Pass	Functional Test	Night	Plant Central, Zone 9	Functional test passed after minor adjustment.	blank
INSP-00019	Major	7	true	LINE-04	2024-05-10	PRD-10019	BATCH-I019	EMP-019	Dimensional	Rework Required	Measurement	Day	Plant East, Zone 4	Several units outside allowed tolerance.	Batch sent for dimensional correction and retesting.
INSP-00020	None	0	false	LINE-05	2024-05-10	PRD-10020	BATCH-J020	EMP-020	None	Pass	Measurement	Evening	Plant North, Zone 8	No discrepancies found.	blank

What the 200 rows show

from the 200-row sample

Critical (defect severity) stands out: mean defect_count is 17.5, against 2.2 for the rest.

41%corrective_action_taken = true
2median defect_count
3inspection results
3shifts
5inspection methods
6defect types

Mean defect_count by defect_severity200 rows

defect_count200 rows, in bands of 5

Median 2, from 0 to 37.

production_line_id200 rows · 10 values

LINE-0135
LINE-0235
LINE-0535
LINE-0334
LINE-0434
PLINE016
PLINE026
PLINE035
PLINE045
PLINE055

16 columns by typefrom the column list below

string 13
integer 1
date 1
boolean 1

Columns

16 columns in four groups

blueprint · 16 columns
column	type	description	example
Text 13 columns
`inspection_id`	string	Unique identifier for each inspection eventunique	INSP-00001
`product_id`	string	Unique identifier for the product inspected	PRD-10001
`batch_number`	string	Production batch number for traceability	BATCH-A001
`production_line_id`	string	Identifier for the production line where the product was manufactured10 lines	LINE-01
`inspector_id`	string	Unique identifier for the inspector who performed the inspection	EMP-001
`defect_type`	string	Type/category of defect found (if any)6 values · optional	None
`defect_severity`	string	Severity level of the defect foundNone · Minor · Major · Critical · optional	None
`inspection_result`	string	Overall result of the inspectionPass · Fail · Rework Required	Pass
`remarks`	string	Additional comments or notes from the inspectoroptional	No discrepancies found.
`corrective_action_description`	string	Description of corrective actions taken (if any)optional	Units sent to reassembly …
`inspection_method`	string	Method or technique used for inspectionVisual · Measurement · Functional Test · Automated · Other	Visual
`shift`	string	Production shift during which inspection occurredDay · Evening · Night · optional	Day
`location`	string	Physical location or facility where inspection was performedoptional	Plant East, Zone 2
Numbers 1 column
`defect_count`	integer	Number of defects found during inspection0 or more	0
Dates and times 1 column
`inspection_date`	date	Date the inspection was performed	2024-05-01
True or false 1 column
`corrective_action_taken`	boolean	Indicates if corrective action was taken for defects found	false

15 SQL practice questions for the Manufacturing Quality Control Metrics

Written for this data and checked against it: 12 SQL and 3 Power BI exercises from beginner to advanced. Try the first three free; the practice kit has all 15, a starter .sql file and an answer key with every expected result.

01BeginnerFreeSELECT · WHERE · ORDER BY
Failed inspections list
Return inspection_id, inspection_date, production_line_id and defect_count for every inspection whose inspection_result is 'Fail'. Order the rows by inspection_date, then inspection_id.
Tables: manufacturing_quality_control_metrics · your result: 4 columns, 28 rows
02BeginnerFreeDISTINCT · ORDER BY
Distinct inspection methods
Return each distinct value of inspection_method as a single column named inspection_method, sorted alphabetically.
Tables: manufacturing_quality_control_metrics · your result: 1 columns, 5 rows
03BeginnerFreeGROUP BY · COUNT
Inspections per result
Count the inspections for each inspection_result. Return inspection_result and inspection_count, ordered by inspection_count descending, then inspection_result.
Tables: manufacturing_quality_control_metrics · your result: 2 columns, 3 rows

04Total defects by defect typeBeginner
05Defects by shift visualPower BI
06Lines with many failuresIntermediate
07Defect count bucketsIntermediate
08Lines above average defectsIntermediate
09Monthly inspection summaryIntermediate
10Corrective action rate measurePower BI
11Rank lines by defectsAdvanced
12Running defect total by dateAdvanced
13Day-over-day change in defectsAdvanced
14Share of defects by locationAdvanced
15Top inspectors by defects foundPower BI

Get the practice kit: the full dataset, all 15 exercises, a starter .sql and the answer key. One-time $3.99.

Get the practice kit · $3.99

Use it for

A manufacturing dashboard
The corrective_action_taken rate, defect_count by defect_severity and a breakdown of production_line_id. Excel, Power BI or Tableau.
Why do the 25 Critical rows have a mean defect_count of 17.5?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Inspections200
INSP-000010None
INSP-000033Major
INSP-0000612Critical
A software demo
Believable inspections with inspection_date, product_id and batch_number to fill a screen in front of a buyer.

This dataset200 rows16 columns

Yours1,000 rows16 columnslocation: UK only

blueprint · manufacturing-quality-control-metrics

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 16 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 inspection date and line
Track defect type and severity
Flag failed quality checks
Exclude reworked items

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: manufacturing-quality-control-metrics

What should your data show?

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Sample rows

What the 200 rows show

Columns

Failed inspections list

Distinct inspection methods

Inspections per result

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?