Robot Density & Job Displacement Simulator (2D)

Robot Density (per 10,000 workers)

Density 274

Country / region preset

Substitution rate σ 80%

Timeline 2026

Factory Readouts

Automation share A

21.5%

Output multiplier

1.32×

Jobs displaced

17.2%

Technician jobs created

4.3%

Floor mix (of 144 stations)

31 robots · 113 workers

How it works

Robot density d is the International Federation of Robotics metric — installed industrial robots per 10,000 manufacturing workers. Real 2023–24 benchmarks: South Korea 1,000, Singapore 670, Japan 400, Germany 370, USA 274. The slider (or a country preset) sets d directly; the timeline instead grows d(t) toward the selected target with a logistic adoption curve, the same S-shaped pattern real installation-rate data follows:

d(t) = d_target · 1 / (1 + e^(-k·(t - t0)))
Automation share:  A(d)  = d / (d + k_sat),   k_sat = 600
Output multiplier: O(A)  = 1 + β·A,           β = 1.5
Jobs displaced:     Disp(A) = σ·A
Jobs created:        New(A)  = (1-σ)·A · γ,     γ = 0.35

A — automation's saturating share of the workforce's task capacity; it never reaches 100% even at very high density, because some tasks resist automation.
σ (substitution rate) — of the capacity a robot takes over, what fraction is a straight 1-for-1 job loss versus work that still needs a human (quality checks, exceptions). Sliding it right makes the same density hurt employment more.
Floor mix — 144 stations on the factory grid are assigned round(A·144) to robot arms and the rest to human workers, purely for visualization; real factories rarely reach A near 1 because full density (1,000+) still only implies A ≈ 0.63 under this saturating model.
Adoption curve strip — the bottom strip plots d(t) across 2015–2035 for the active target and moves a marker to the current year, so you can see where "now" sits on the S-curve while you scrub or play it.

Calibration note: β, k_sat, σ default and γ are simplified, order-of-magnitude assumptions consistent with published automation-economics ranges (McKinsey, IFR, Frey & Osborne) — not a precise macro-econometric forecast. Move σ to see how sensitive job outcomes are to that one assumption.

Drag the floor view to pan and scroll/pinch to zoom into individual stations.

Robot arm station Human worker station Output units on belt

Drag to pan · scroll to zoom