Automation index
How much of a Production engineer’s job can AI already do?
An OEE and a sequencer come earlier. Getting the shift out without stopping the line for nothing do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Production engineer
25%
AI can already do 25% of this job.
#233 / 623
View rankingProduction engineer
Where the time goes
Task weights are our estimates based on O*NET frequency data, not time shares published by O*NET.
The time saving for each task is estimated separately.
| Task | Share | AI | Points |
|---|---|---|---|
OEE, sequence and the shift plan.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Line, changeovers and the micro-stop.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Layout, times and the work standard.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 36% | +5.8 |
Supervisors, unions and the shift that turns.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
In-line quality and the batch that cannot ship.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Hit the plan, and not dressing the OEE.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 25% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- OEE
- sequence production
- changeovers
- layout
- work standards
- micro-stops
- shift plan
- in-line quality
Sources for this job· O*NET 17-2112.00· ESCO/ISCO 2141
IS WORK OVER?
ML engineer
29%
#64 / 623
View rankingML engineer
Where the time goes
Task weights are our estimates based on O*NET frequency data, not time shares published by O*NET.
The time saving for each task is estimated separately.
| Task | Share | AI | Points |
|---|---|---|---|
Train models, features and notebooks.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 48% | +10.6 |
Evaluate, document and compare runs.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 42% | +7.6 |
Deploy, monitor and retrain.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Talk to product about what a good error is.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 15% | +2.4 |
Dirty data, bias and model limits.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 18% | +2.5 |
On-call, incidents and rollback.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 16% | +2.2 |
| Total | 100% | 29% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Train models, features and notebooks
- Evaluate, document and compare runs
- Deploy, monitor and retrain
- Talk to product about what a good error is
- Dirty data, bias and model limits
- On-call, incidents and rollback
- machine learning engineer
- ingeniero ml
- mlops
Sources for this job· O*NET 15-1221.00· ESCO/ISCO 2511