IS WORK OVER?

Automation index

How much of a Section supervisor’s job can AI already do?

A section planogram comes earlier. The shelf being there and the shift running do not.

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

Task comparison

Section supervisor

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
Section, planogram and the order.
Estimate

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

16%40%+6.4
Shift, the gap and the one who is late.
Estimate

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

18%14%+2.5
Customer, the complaint and the one who cannot find it.
Estimate

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

16%28%+4.5
Shrink, dates and not hiding them.
Estimate

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

16%22%+3.5
The store manager, the peak and not leaving the shelf empty.
Estimate

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

18%18%+3.2
The shelf, and the shift standing.
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

  • section
  • planogram
  • shrink
  • dates
  • store shifts
  • complaints
  • section orders
  • gaps

Sources for this job· O*NET 41-1011.00· ESCO/ISCO 5222

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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