Manufacturing Line Defect Counts
This dataset provides detailed, shift-level defect counts for each product type and manufacturing line, enabling granular quality control analysis and predictive modeling. With fields for defect categories, production volumes, and calculated defect rates, it supports both operational monitoring and advanced AI-driven analytics to improve manufacturing processes.
Sample rows
preview · 8 of 200 rows · all 9 columns| record_idstring | shiftstring | defect_ratefloat | product_typestring | datedate | defect_typestring | defect_countinteger | production_linestring | total_units_producedinteger |
|---|---|---|---|---|---|---|---|---|
| AB12-CD34 | Morning | 0.01 | Widget-A | 2024-05-02 | Surface | 3 | Line-1 | 300 |
| EF56-GH78 | Afternoon | 0 | Widget-B | 2024-05-03 | Mechanical | 0 | Line-2 | 120 |
| IJ90-KL12 | Night | 0.12632 | Widget-C | 2024-05-04 | Electrical | 12 | Line-3 | 95 |
| MN34-OP56 | Morning | 0.01481 | Widget-D | 2024-05-05 | Surface | 8 | Line-4 | 540 |
| QR78-ST90 | Afternoon | 0.0025 | Widget-E | 2024-05-06 | Mechanical | 1 | Line-5 | 400 |
| UV12-WX34 | Night | 0.04167 | Widget-F | 2024-05-07 | Electrical | 25 | Line-6 | 600 |
| YZ56-AB78 | Morning | blank | Widget-G | 2024-05-08 | Surface | 0 | Line-7 | 0 |
| CD90-EF12 | Afternoon | 0.11 | Gizmo-S | 2024-05-09 | Electrical | 99 | Line-8 | 900 |
| GH34-IJ56 | Night | 0.09444 | Gizmo-T | 2024-05-10 | Mechanical | 17 | Line-1 | 180 |
| KL78-MN90 | Morning | 0.02 | Widget-A | 2024-05-12 | Surface | 5 | Line-2 | 250 |
| OP12-QR34 | Afternoon | 0.03846 | Widget-B | 2024-05-13 | Mechanical | 2 | Line-3 | 52 |
| ST56-UV78 | Night | 0.09 | Widget-C | 2024-05-14 | Electrical | 27 | Line-4 | 300 |
| WX90-YZ12 | Morning | 0.03333 | Widget-D | 2024-05-15 | Surface | 4 | Line-5 | 120 |
| AB34-CD56 | Afternoon | 0.07222 | Widget-E | 2024-05-16 | Mechanical | 13 | Line-6 | 180 |
| EF78-GH90 | Night | 0.09259 | Widget-F | 2024-05-17 | Electrical | 50 | Line-7 | 540 |
| IJ12-KL34 | Morning | 0.01 | Widget-G | 2024-05-18 | Surface | 1 | Line-8 | 100 |
| MN56-OP78 | Afternoon | 0.1 | Gizmo-S | 2024-05-19 | Electrical | 1000 | Line-1 | 10000 |
| QR12-ST34 | Night | 0.08421 | Gizmo-T | 2024-05-21 | Mechanical | 8 | Line-2 | 95 |
| UV56-WX78 | Morning | 0.055 | Widget-A | 2024-05-22 | Surface | 22 | Line-3 | 400 |
| YZ12-AB34 | Afternoon | 0.024 | Widget-B | 2024-05-23 | Mechanical | 6 | Line-4 | 250 |
What the 200 rows show
from the 200-row sampleNight shift stands out: mean defect_
- 0.03median defect_
rate - 3defect types
- 8production lines
- 5median defect_
count - 225median total_
units_ produced
Median 0.03, from 0.0 to 0.53.
- string 5
- integer 2
- float 1
- date 1
Columns
9 columns in three groups| column | type | description | example |
|---|---|---|---|
| Text 5 columns | |||
record_id | string | Unique identifier for each defect count recordunique | AB12-CD34 |
shift | string | Production shift during which defects were counted (e.g., Morning, Afternoon, Night)Morning · Afternoon · Night | Morning |
product_type | string | Type or model of the product manufactured9 types | Widget-A |
defect_type | string | Category or classification of the defect (e.g., Surface, Mechanical, Electrical)3 types | Surface |
production_line | string | Identifier or name of the manufacturing line where the product was produced8 lines | Line-1 |
| Numbers 3 columns | |||
defect_count | integer | Number of defects of the specified type found during the shift for the product type0 or more | 3 |
total_units_produced | integer | Total number of units produced for the product type during the shift on the production line0 or more | 300 |
defect_rate | float | Calculated rate of defects per unit produced (defect_count / total_units_produced)0 to 1 · optional | 0.01 |
| Dates and times 1 column | |||
date | date | Date when the defect count was recorded | 2024-05-02 |
Use it for
A manufacturing dashboard
Defect_
rate by shift and a breakdown of product_ type. Excel, Power BI or Tableau. Why do the 58 Night rows have a mean defect_
rate of 0.11? A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
- Records200AB12-CD340.01MorningEF56-GH780AfternoonIJ90-KL120.12632Night
A software demo
Believable records with date, shift and product_
type to fill a screen in front of a buyer.
blueprint · manufacturing-line-defect-counts
Behind this dataset
Same schema. As many rows as you need.
These 200 rows came out of a blueprint — 9 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.
- Each entry includes shift and product type.
- Defect count per shift required.
- Line supervisor ID optionally included.
- Defect reason categories flagged.
- Zero defects allowed.
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-line-defect-counts