IS WORK OVER?

Automation index

How much of a Parts clerk’s job can AI already do?

An OEM code comes earlier. Giving the part that fits, not the lookalike, do not.

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

Task comparison

Parts clerk

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
Catalogue, equivalents and the code.
Estimate

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

18%52%+9.4
Counter, the waiting shop and the doubt.
Estimate

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

16%22%+3.5
Stock, urgent orders and the one that is late.
Estimate

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

16%36%+5.8
Returns, warranties and the wrong part.
Estimate

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

16%28%+4.5
The mechanic, the blurry photo and not selling blind.
Estimate

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

18%14%+2.5
The one that fits, not the lookalike.
Estimate

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

16%12%+1.9
Total100%28%

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

Skills and activities

  • parts catalogue
  • OEM equivalents
  • shop counter
  • urgent orders
  • returns
  • part warranties
  • parts stock
  • part codes

Sources for this job· O*NET 41-2022.00· ESCO/ISCO 4321

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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