Factory Digital Twin Performance Metrics
This dataset provides daily, shift-level operational metrics from digital twins of SME factory lines, including efficiency, downtime, throughput, and predictive failure indicators. It enables operational managers to monitor equipment health, benchmark performance, and support data-driven decisions for maintenance and supply chain optimization. The rich, flat structure supports advanced analytics, predictive modeling, and continuous improvement initiatives in manufacturing environments.
Sample rows
preview · 8 of 84 rows · all 18 columns| twin_idstring | shiftstring | actual_failuresinteger | maintenance_performedboolean | factory_idstring | factory_line_idstring | datedate | operating_hoursfloat | downtime_hoursfloat | throughput_unitsinteger | planned_production_unitsinteger | efficiency_percentagefloat | predicted_failure_riskfloat | mean_time_between_failures_hoursfloat | mean_time_to_repair_hoursfloat | maintenance_typestring | operator_idstring | notesstring |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| DTW-1001 | morning | 0 | false | FAC-001 | LINE-01 | 2024-04-01 | 7.5 | 0.5 | 420 | 450 | 93.33 | 0.14 | 120 | 0 | blank | OPR-013 | Smooth operations, minor delay due to material check. |
| DTW-1001 | afternoon | 1 | true | FAC-001 | LINE-01 | 2024-04-01 | 7 | 1 | 405 | 440 | 92.05 | 0.19 | 100 | 1 | corrective | OPR-008 | Corrective maintenance performed on conveyor. |
| DTW-1002 | night | 2 | true | FAC-001 | LINE-02 | 2024-04-01 | 6.5 | 2.5 | 380 | 400 | 95 | 0.35 | 60 | 1.5 | predictive | OPR-021 | Predictive maintenance scheduled, two minor stoppages. |
| DTW-1003 | morning | 0 | false | FAC-002 | LINE-01 | 2024-04-02 | 8 | 0 | 500 | 500 | 100 | 0.07 | 180 | 0 | blank | OPR-015 | No issues, full production achieved. |
| DTW-1004 | afternoon | 1 | true | FAC-002 | LINE-02 | 2024-04-02 | 7.2 | 0.8 | 470 | 480 | 97.92 | 0.13 | 110 | 0.8 | preventive | OPR-017 | Preventive lubrication maintenance. |
| DTW-1005 | night | 3 | true | FAC-003 | LINE-03 | 2024-04-02 | 5.5 | 3.5 | 350 | 400 | 87.5 | 0.41 | 40 | 2 | corrective | OPR-025 | Multiple minor failures, corrective action taken. |
| DTW-1006 | morning | 0 | false | FAC-003 | LINE-01 | 2024-04-03 | 7.9 | 0.1 | 490 | 500 | 98 | 0.09 | 200 | 0 | blank | OPR-018 | Near perfect run, no downtime. |
| DTW-1007 | afternoon | 1 | true | FAC-003 | LINE-02 | 2024-04-03 | 7.4 | 0.6 | 460 | 470 | 97.87 | 0.18 | 95 | 0.6 | preventive | OPR-014 | Preventive maintenance - filter change. |
| DTW-1008 | night | 2 | true | FAC-004 | LINE-01 | 2024-04-04 | 6 | 2 | 370 | 400 | 92.5 | 0.32 | 60 | 2 | corrective | OPR-023 | Downtime caused by sensor fault. |
| DTW-1009 | morning | 0 | false | FAC-004 | LINE-02 | 2024-04-04 | 7.6 | 0.4 | 480 | 490 | 97.96 | 0.12 | 150 | 0 | blank | OPR-019 | Good performance, no failures. |
| DTW-1010 | afternoon | 1 | true | FAC-005 | LINE-03 | 2024-04-04 | 6.8 | 1.2 | 410 | 430 | 95.35 | 0.21 | 80 | 1.2 | predictive | OPR-029 | Predictive maintenance - replaced worn belt. |
| DTW-1011 | night | 3 | true | FAC-005 | LINE-01 | 2024-04-05 | 5.7 | 3.3 | 330 | 390 | 84.62 | 0.46 | 35 | 2.2 | corrective | OPR-033 | Frequent failures, corrective repairs needed. |
| DTW-1012 | morning | 0 | false | FAC-005 | LINE-02 | 2024-04-05 | 7.8 | 0.2 | 495 | 500 | 99 | 0.08 | 175 | 0 | blank | OPR-012 | Excellent shift, minimal downtime. |
| DTW-1013 | afternoon | 1 | true | FAC-006 | LINE-01 | 2024-04-06 | 7.3 | 0.7 | 460 | 480 | 95.83 | 0.17 | 88 | 0.7 | preventive | OPR-016 | Preventive maintenance completed. |
| DTW-1014 | night | 2 | true | FAC-006 | LINE-02 | 2024-04-06 | 6.2 | 1.8 | 400 | 430 | 93.02 | 0.27 | 70 | 1.8 | corrective | OPR-028 | Downtime due to jammed feeder. |
| DTW-1015 | morning | 0 | false | FAC-007 | LINE-03 | 2024-04-07 | 8 | 0 | 520 | 520 | 100 | 0.05 | 200 | 0 | blank | OPR-011 | Optimal throughput, no issues reported. |
| DTW-1016 | afternoon | 0 | false | FAC-007 | LINE-01 | 2024-04-07 | 7.6 | 0.4 | 480 | 490 | 97.96 | 0.09 | 160 | 0 | blank | OPR-010 | No downtime except brief inspection. |
| DTW-1017 | night | 2 | true | FAC-007 | LINE-02 | 2024-04-07 | 5.9 | 2.1 | 370 | 400 | 92.5 | 0.31 | 62 | 2.1 | predictive | OPR-026 | Predictive maintenance - vibration anomaly. |
| DTW-1018 | morning | 0 | false | FAC-008 | LINE-01 | 2024-04-08 | 7.7 | 0.3 | 495 | 500 | 99 | 0.12 | 175 | 0 | blank | OPR-022 | High output, no failures. |
| DTW-1019 | afternoon | 1 | true | FAC-008 | LINE-02 | 2024-04-08 | 7.1 | 0.9 | 440 | 450 | 97.78 | 0.16 | 90 | 0.9 | preventive | OPR-020 | Preventive maintenance - replaced filter. |
What the 84 rows show
from the 84-row sampleNight shift stands out: mean actual_
- 60%maintenance_
performed = true - 1median actual_
failures - 3maintenance types
- 12factory lines
- 7.5median operating_
hours - 0.80median downtime_
hours
Every row with actual_
- string 7
- integer 3
- float 6
- date 1
- boolean 1
Columns
18 columns in four groups| column | type | description | example |
|---|---|---|---|
| Text 7 columns | |||
twin_id | string | Unique identifier for the digital twin representing a specific factory line or equipment. | DTW-1001 |
factory_id | string | Unique identifier for the factory where the digital twin is deployed. | FAC-001 |
factory_line_id | string | Identifier for the specific production line within the factory.12 lines | LINE-01 |
shift | string | Production shift during which metrics were captured (e.g., 'morning', 'afternoon', 'night').morning · afternoon · night · optional | morning |
maintenance_type | string | Type of maintenance performed (e.g., 'preventive', 'corrective', 'predictive').preventive · corrective · predictive · optional | corrective |
operator_id | string | Identifier for the operator or supervisor responsible during the shift.optional | OPR-013 |
notes | string | Additional notes or comments regarding the day's operations, anomalies, or observations.optional | Flawless shift. |
| Numbers 9 columns | |||
operating_hours | float | Total number of hours the equipment or line was operational during the day.0 to 24 | 7.5 |
downtime_hours | float | Total number of hours the equipment or line was down (not operational) during the day.0 to 24 | 0.5 |
throughput_units | integer | Number of units produced during the day.0 or more | 420 |
planned_production_units | integer | Number of units planned to be produced during the day.0 or more · optional | 450 |
efficiency_percentage | float | Operational efficiency as a percentage (actual throughput vs. planned throughput).0 to 100 | 93.33 |
predicted_failure_risk | float | Predicted risk of equipment failure (0-1 scale, where 1 is highest risk).0 to 1 | 0.14 |
actual_failures | integer | Number of actual equipment failures recorded during the day.0 or more | 0 |
mean_time_between_failures_hours | float | Mean time between failures (MTBF) in hours, calculated for the equipment or line.0 or more · optional | 120 |
mean_time_to_repair_hours | float | Mean time to repair (MTTR) in hours for the equipment or line.0 or more · optional | 0 |
| Dates and times 1 column | |||
date | date | Date for which the operational metrics are recorded. | 2024-04-01 |
| True or false 1 column | |||
maintenance_performed | boolean | Indicates if any maintenance was performed on the equipment or line during the day. | false |
Use it for
A manufacturing dashboard
The maintenance_
performed rate, actual_ failures by shift and a breakdown of factory_ id. Excel, Power BI or Tableau. Why do the 28 night rows have a mean actual_
failures of 2.1? A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
- Twins84DTW-10010morningDTW-10011afternoonDTW-10022night
A software demo
Believable twins with factory_
id, factory_ line_ id and date to fill a screen in front of a buyer.
This dataset84 rows18 columns
Yours1,000 rows18 columns
blueprint · factory-digital-twin-performance-metrics
Behind this dataset
Same schema. As many rows as you need.
These 84 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
- Each row represents one factory line per day.
- Includes downtime incidents, throughput rate, and predictive failure scores.
- Failure scores calculated using simulated sensor data patterns.
- Downtime linked to either maintenance events or supply interruptions.
- Maximum 7 unique factory lines represented.
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
- factory-digital-twin-performance-metrics