Manufacturing Production Defect Rates

This dataset provides detailed records of defect rates in manufacturing production, including batch-level information on plant, production line, product, inspection methods, and defect types. It enables quality managers and process engineers to monitor, analyze, and optimize production quality, identify trends, and address recurring issues for improved operational efficiency.

  • opened 34 times
  • last updated 20 Aug 2025
  • by GoMask
The brief that made it

Quality control monitoring and reporting

Sample rows

preview · 8 of 200 rows · all 18 columns
record_idstringinspection_methodstringdefect_ratefloatdefect_typestringproduction_datedateplant_idstringplant_namestringproduction_line_idstringproduction_line_namestringproduct_idstringproduct_namestringbatch_numberstringunits_producedintegerunits_defectiveintegerinspector_idstringshiftstringdefect_descriptionstringremarksstring
A12345-20230420automated0.0005cosmetic2023-04-20PLT01Midwest Assembly PlantLINE-A1Conveyor Assembly LinePROD-01GearBox XBATCH-100180004INSP-101morningMinor surface scratches on outer casing detected.Low defect rate; routine batch. No further action required.
B54321-20240211sampling0blank2024-02-11PLT02Pacific Tech PlantLINE-B2Precision SMD LinePROD-02Chipster V2BATCH-100230INSP-102nightblankPrototype batch; special inspection. No defects found.
C67890-20231215visual0.02functional2023-12-15PLT03Eastern Components FacilityLINE-C3Mechanical Drive LinePROD-03Widget MiniBATCH-1003150030INSP-103afternoonOutput speed variance exceeds tolerance in some units.Defects within expected range; review scheduled.
D24680-20221006automated0.7material2022-10-06PLT04Southern Robotics HubLINE-D4Servo Assembly LinePROD-04ServoDrive RBATCH-1004300210INSP-104nightPlastic housing failed stress test on majority of units.Batch failure; investigation initiated for supplier issue.
E13579-20210430visual0blank2021-04-30PLT05Northern Precision PlantLINE-E1PCB Print LinePROD-05PCB MatrixBATCH-10057000INSP-105morningblankRoutine batch; no defects observed.
F11223-20230525automated0.02cosmetic2023-05-25PLT06Central DriveworksLINE-F2Die Cast LinePROD-06RotorMax SBATCH-1006250050INSP-106afternoonSlight discoloration in paint finish detected.Cosmetic defects within tolerance; no recall needed.
G33445-20230621automated0blank2023-06-21PLT07Western Machining CenterLINE-G7Gear MachiningPROD-07TurboGear XLBATCH-100790000INSP-107morningblankHigh-volume batch; no defects, routine process.
A98765-20231103visual0.00033cosmetic2023-11-03PLT01Midwest Assembly PlantLINE-A2Packaging LinePROD-08Boxster LiteBATCH-100860002INSP-108afternoonSlight misprint on packaging label in two units.Minor cosmetic issue; corrective action completed.

What the 200 rows show

from the 200-row sample

Other (inspection method) stands out: mean defect_rate is 0.79, against 0.06 for the rest.

  • 0.01median defect_rate
  • 3shifts
  • 7plants
  • 34plant names
  • 42products
  • 50production lines
Mean defect_rate by inspection_method200 rows
00.510.01visual61 rows0.11automated80 rows0.06sampling38 rows0.79other21 rows
defect_rate200 rows, in bands of 0.1
0751501428274111001600.51defect_rate →

Median 0.01, from 0.0 to 1.0.

defect_type140 rows with a value · 60 left blank
  1. cosmetic47
  2. material32
  3. functional27
  4. dimensional19
  5. other15
18 columns by typefrom the column list below
  • string 14
  • integer 2
  • float 1
  • date 1

Columns

18 columns in three groups
blueprint · 18 columns
columntypedescriptionexample
Text 14 columns
record_idstringUnique identifier for each defect rate recorduniqueA12345-20230420
plant_idstringUnique identifier for the manufacturing plant7 plantsPLT01
plant_namestringName of the manufacturing plantoptionalMidwest Assembly Plant
production_line_idstringUnique identifier for the production lineLINE-A1
production_line_namestringName or description of the production lineoptionalConveyor Assembly Line
product_idstringUnique identifier for the product manufacturedPROD-01
product_namestringName or description of the productoptionalGearBox X
batch_numberstringIdentifier for the production batchBATCH-1001
defect_typestringPrimary type of defect identified (e.g., cosmetic, functional, dimensional)cosmetic · functional · dimensional · material · other · optionalcosmetic
defect_descriptionstringDetailed description of the defect(s) found in the batchoptionalMinor surface scratches o…
inspector_idstringIdentifier for the quality inspector who performed the checkoptionalINSP-101
inspection_methodstringMethod used for defect inspection (e.g., visual, automated, sampling)visual · automated · sampling · other · optionalautomated
shiftstringProduction shift during which the batch was producedmorning · afternoon · night · optionalmorning
remarksstringAdditional notes or remarks about the batch or inspectionoptionalLow defect rate; routine …
Numbers 3 columns
units_producedintegerTotal number of units produced in the batch1 or more8000
units_defectiveintegerNumber of defective units identified in the batch0 or more4
defect_ratefloatCalculated defect rate for the batch (units_defective / units_produced)0 to 10.0005
Dates and times 1 column
production_datedateDate when the production batch was completed2023-04-20

Use it for

  • median defect …0.01200 rowsmean defect rate by i…0.01visu…0.11auto…0.06samp…0.79other

    A manufacturing dashboard

    Defect_rate by inspection_method and a breakdown of defect_type. Excel, Power BI or Tableau.

  • Why do the 21 other rows have a mean defect_rate of 0.79?

    A root-cause class exercise

    Hand out the rows and one question. The answer is in the data, not in the brief.

  • A software demo

    Believable records with production_date, plant_id and plant_name to fill a screen in front of a buyer.

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This dataset200 rows18 columns
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blueprint · manufacturing-production-defect-rates

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 18 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 defects by product type
  • Segment by batch and shift
  • Include defect cause
  • Flag high-defect periods
Rows
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Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
manufacturing-production-defect-rates

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