IS WORK OVER?

Automation index

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

A planogram comes earlier. A shelf that sells without looking empty do not.

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

Task comparison

Merchandiser

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
Planogram, facings and the shelf.
Estimate

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

16%40%+6.4
Store, replenish and the one who will not follow the plan.
Estimate

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

18%14%+2.5
Promo, the extra and the one that falls.
Estimate

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

16%28%+4.5
Data, sell-out and not inflating facings.
Estimate

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

16%22%+3.5
The store manager, the space and fighting for the gap.
Estimate

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

18%18%+3.2
That the shelf sells, and not empty.
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

  • planograms
  • facings
  • the shelf
  • sell-out
  • promos
  • replenish
  • store space
  • gaps

Sources for this job· O*NET 41-9011.00· ESCO/ISCO 5223

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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