Production Batch Yield Records
This dataset provides detailed records of production batches, including batch characteristics, yield metrics, timing, and quality indicators. It enables manufacturers to analyze and optimize batch processes, identify inefficiencies, and improve overall production yield and quality. The dataset is ideal for process optimization, quality control, and operational analytics.
Sample rows
preview · 8 of 200 rows · all 15 columns| batch_idstring | shiftstring | batch_sizefloat | product_codestring | production_datedate | product_namestring | actual_yieldfloat | yield_percentagefloat | start_timedatetime | end_timedatetime | production_line_idstring | operator_idstring | defect_countinteger | scrap_quantityfloat | remarksstring |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| BATCH-001A | morning | 3200 | PRD-001 | 2022-11-02 | High Purity Resin | 3161.6 | 98.8 | 2022-11-02T07:15:00 | 2022-11-02T11:10:00 | LINE-A1 | OP-101 | 4 | 1.5 | Routine batch |
| BATCH-002B | evening | 1200 | PRD-002 | 2022-11-03 | Polymer Blend | 1164 | 97 | 2022-11-03T16:25:00 | 2022-11-03T20:30:00 | LINE-B2 | OP-102 | 6 | 2 | High yield |
| BATCH-003C | morning | 150 | PRD-003 | 2022-11-05 | Adhesive Compound | 142.5 | 95 | 2022-11-05T05:10:00 | 2022-11-05T08:05:00 | LINE-C3 | OP-103 | 3 | 1 | Batch completed without issues |
| BATCH-004D | evening | 4500 | PRD-004 | 2022-11-07 | Industrial Paint | 4410 | 98 | 2022-11-07T18:30:00 | 2022-11-07T22:54:00 | LINE-D2 | OP-104 | 7 | 2.7 | Routine production |
| BATCH-005E | morning | 75 | PRD-005 | 2022-11-09 | Thermal Paste | 71.25 | 95 | 2022-11-09T06:48:00 | 2022-11-09T10:09:00 | LINE-E5 | OP-105 | 2 | 1 | blank |
| BATCH-006F | evening | 2500 | PRD-006 | 2022-11-11 | Epoxy Resin | 2375 | 95 | 2022-11-11T16:00:00 | 2022-11-11T21:00:00 | LINE-F4 | OP-106 | 10 | 4.5 | Routine batch |
| BATCH-007G | morning | 500 | PRD-007 | 2022-12-01 | Silicone Gel | 495 | 99 | 2022-12-01T05:50:00 | 2022-12-01T09:20:00 | LINE-G6 | OP-107 | 0 | 0 | Perfect batch |
| BATCH-008H | morning | 0.5 | PRD-008 | 2022-12-03 | Conductive Filler | 0.5 | 100 | 2022-12-03T06:20:00 | 2022-12-03T06:30:00 | LINE-H7 | OP-108 | 0 | 0 | Edge case: minimal batch size |
| BATCH-009I | night | 3800 | PRD-009 | 2022-12-05 | Synthetic Rubber | 3420 | 90 | 2022-12-05T22:10:00 | 2022-12-06T03:00:00 | LINE-I8 | OP-109 | 20 | 8 | Minor issues during run |
| BATCH-010J | evening | 600 | PRD-010 | 2022-12-08 | Insulation Foam | 585 | 97.5 | 2022-12-08T17:10:00 | 2022-12-08T19:40:00 | LINE-J9 | OP-110 | 2 | 0.7 | blank |
| BATCH-011K | morning | 210 | PRD-011 | 2022-12-10 | Nano Additive | 168 | 80 | 2022-12-10T05:30:00 | 2022-12-10T07:20:00 | LINE-K1 | blank | 15 | 5 | Unattended operator |
| BATCH-012L | afternoon | 1800 | PRD-012 | 2022-12-14 | Flame Retardant | 1710 | 95 | 2022-12-14T13:10:00 | 2022-12-14T17:20:00 | LINE-L2 | OP-111 | 8 | 3 | blank |
| BATCH-013M | morning | 70 | PRD-013 | 2022-12-16 | UV Stabilizer | 66.5 | 95 | 2022-12-16T05:40:00 | 2022-12-16T08:10:00 | LINE-M3 | OP-112 | 1 | 0.5 | blank |
| BATCH-014N | night | 10000 | PRD-014 | 2022-12-18 | Catalyst Mix | 7000 | 70 | 2022-12-18T23:10:00 | 2022-12-19T07:30:00 | LINE-N4 | blank | 500 | 100 | Automated run, investigation pending |
| BATCH-015O | morning | 300 | PRD-015 | 2023-01-03 | Elastomer Sheet | 294 | 98 | 2023-01-03T05:05:00 | 2023-01-03T08:15:00 | LINE-O5 | OP-113 | 1 | 0.5 | blank |
| BATCH-016P | evening | 1500 | PRD-016 | 2023-01-06 | Antifoam Agent | 1350 | 90 | 2023-01-06T18:40:00 | 2023-01-06T22:50:00 | LINE-P6 | OP-114 | 15 | 6.5 | Minor quality issues |
| BATCH-017Q | morning | 3500 | PRD-017 | 2023-01-09 | Pigment Powder | 3325 | 95 | 2023-01-09T05:15:00 | 2023-01-09T09:10:00 | LINE-Q7 | OP-115 | 8 | 3.2 | Routine batch |
| BATCH-018R | night | 1000 | PRD-003 | 2023-01-11 | Adhesive Compound | 900 | 90 | 2023-01-11T22:00:00 | 2023-01-12T03:30:00 | LINE-C3 | OP-103 | 25 | 9.5 | Night run, minor issues |
| BATCH-019S | evening | 2000 | PRD-004 | 2023-01-14 | Industrial Paint | 1900 | 95 | 2023-01-14T16:10:00 | 2023-01-14T20:10:00 | LINE-D2 | OP-104 | 9 | 4 | blank |
| BATCH-020T | morning | 4800 | PRD-001 | 2023-01-17 | High Purity Resin | 4704 | 98 | 2023-01-17T05:35:00 | 2023-01-17T12:25:00 | LINE-A1 | OP-101 | 5 | 2 | Routine batch |
What the 200 rows show
from the 200-row sampleNight shift stands out: mean batch_
- 1,500median batch_
size - 1,364median actual_
yield - 95.0median yield_
percentage - 6median defect_
count - 2.0median scrap_
quantity
Median 1,500, from 0.01 to 10,000.
- string 7
- integer 1
- float 4
- date 1
- datetime 2
Columns
15 columns in three groups| column | type | description | example |
|---|---|---|---|
| Text 7 columns | |||
batch_id | string | Unique identifier for each production batchunique | BATCH-001A |
product_code | string | Code representing the product manufactured in the batch | PRD-001 |
product_name | string | Name of the product manufactured in the batch | High Purity Resin |
production_line_id | string | Identifier for the production line or equipment used | LINE-A1 |
operator_id | string | Identifier for the operator or team responsible for the batchoptional | OP-101 |
shift | string | Shift during which the batch was produced (e.g., morning, evening, night)morning · evening · night · optional | morning |
remarks | string | Additional notes or comments about the batchoptional | Routine batch |
| Numbers 5 columns | |||
batch_size | float | Total quantity of product intended to be produced in this batch (in units or kilograms)0.01 or more | 3200 |
actual_yield | float | Actual quantity of product produced in this batch (in units or kilograms)0 or more | 3161.6 |
yield_percentage | float | Percentage of actual yield compared to batch size0 to 100 | 98.8 |
defect_count | integer | Number of defective units identified in the batch0 or more · optional | 4 |
scrap_quantity | float | Quantity of product scrapped or wasted during production0 or more · optional | 1.5 |
| Dates and times 3 columns | |||
production_date | date | Date when the batch was produced | 2022-11-02 |
start_time | datetime | Date and time when batch production started | 2022-11-02T07:15:00 |
end_time | datetime | Date and time when batch production ended | 2022-11-02T11:10:00 |
Use it for
A manufacturing dashboard
Batch_
size by shift and a breakdown of product_ code. Excel, Power BI or Tableau. Why do the 27 night rows have a mean batch_
size of 4,120? A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
- Batches200BATCH-001A3200morningBATCH-002B1200eveningBATCH-003C150morning
A software demo
Believable batches with production_
date, product_ code and product_ name to fill a screen in front of a buyer.
This dataset200 rows15 columns
Yours1,000 rows15 columns
blueprint · production-batch-yield-records
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
- Include batch ID and production date
- Record yield quantity and defect rate
- Flag below-threshold yields
- Exclude pilot or test batches
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
- production-batch-yield-records