IS WORK OVER?

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

Production 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.

TaskShareAIPoints
OEE, sequence and the shift plan.
Estimate

No public source provides a precise figure. This is our estimate.

18%48%+8.6
Line, changeovers and the micro-stop.
Estimate

No public source provides a precise figure. This is our estimate.

16%22%+3.5
Layout, times and the work standard.
Estimate

No public source provides a precise figure. This is our estimate.

16%36%+5.8
Supervisors, unions and the shift that turns.
Estimate

No public source provides a precise figure. This is our estimate.

16%18%+2.9
In-line quality and the batch that cannot ship.
Estimate

No public source provides a precise figure. This is our estimate.

18%14%+2.5
Hit the plan, and not dressing the OEE.
Estimate

No public source provides a precise figure. This is our estimate.

16%12%+1.9
Total100%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

Last-mile manager

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.

TaskShareAIPoints
Zones, capacity and route cuts.
Estimate

No public source provides a precise figure. This is our estimate.

16%44%+7
KPIs, first attempt and incidents.
Estimate

No public source provides a precise figure. This is our estimate.

16%36%+5.8
The courier, the lobby and the one who will not open.
Estimate

No public source provides a precise figure. This is our estimate.

18%16%+2.9
Peaks, rain and hiring too many or too few.
Estimate

No public source provides a precise figure. This is our estimate.

16%28%+4.5
The angry customer and not dressing delivered.
Estimate

No public source provides a precise figure. This is our estimate.

18%18%+3.2
Cover the peak, and not leaving the neighbourhood orphaned.
Estimate

No public source provides a precise figure. This is our estimate.

16%14%+2.2
Total100%26%

Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low

Skills and activities

  • delivery zones
  • first attempt
  • delivery KPIs
  • last-mile peaks
  • lobbies
  • delivery incidents
  • courier capacity
  • cut routes

Sources for this job· O*NET 11-3071.00· ESCO/ISCO 1324

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