IS WORK OVER?

Automation index

How much of a Waste technician’s job can AI already do?

A waste code comes earlier. The waste going where it should, not the next bin, do not.

This percentage does not predict how many people will lose their jobs.

Task comparison

Waste technician

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
Code, characterise and the paper.
Estimate

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

16%40%+6.4
Plant, the mix and the one you cannot.
Estimate

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

18%14%+2.5
Move, the haulier and the note.
Estimate

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

16%28%+4.5
Inspection, the tip and not hiding.
Estimate

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

16%22%+3.5
The producer, the saving and not diluting.
Estimate

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

18%18%+3.2
Where it should, not next door.
Estimate

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

16%12%+1.9
Total100%22%

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

Skills and activities

  • waste codes
  • characterise waste
  • transfer notes
  • the tip
  • mixing
  • producers
  • waste papers
  • not dilute

Sources for this job· O*NET 19-4042.00· ESCO/ISCO 2143

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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