IS WORK OVER?

Automation index

How much of a Order picker’s job can AI already do?

Voice picking comes earlier. Hitting the SKU without leaving a lame pallet do not.

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

Task comparison

Order picker

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
List, location and the scan.
Estimate

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

16%48%+7.7
Aisle, height and the parcel that will not fit.
Estimate

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

22%10%+2.2
Errors, the short and calling it.
Estimate

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

16%28%+4.5
Pace, the shift and the pallet that goes.
Estimate

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

16%22%+3.5
Peaks, Black Friday and not mixing orders.
Estimate

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

16%18%+2.9
Hit the SKU, and not leaving a lame pallet.
Estimate

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

14%12%+1.7
Total100%22%

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

Skills and activities

  • picking
  • scan locations
  • shorts
  • build pallets
  • warehouse aisles
  • order peaks
  • SKUs
  • voice picking

Sources for this job· O*NET 53-7062.00· 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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