Automation index
How much of a Logistics analyst’s job can AI already do?
A cost dashboard comes earlier. Finding the saving without breaking the network do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Logistics analyst
28%
AI can already do 28% of this job.
#79 / 623
View rankingLogistics analyst
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 |
|---|---|---|---|
Costs, OTIF and the dashboard.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 52% | +9.4 |
Simulate network, warehouses and the what-if.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Ops, dirty data and going down to the dock.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Report, the board and the cut that hurts.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The haulier, the contract and not breaking the service.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
A real saving, and not dressing OTIF.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 28% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- logistics costs
- OTIF
- simulate network
- what-if
- dirty data
- haulage contracts
- dashboards
- network cuts
Sources for this job· O*NET 13-1081.00· ESCO/ISCO 2421
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