Stationary plant and machine supervisor/general foreman
AI Exposure Rank
27/100
Range 21–39/100 across source-weight sensitivity checks
Stationary plant and machine supervisor/general foreman has an AI Exposure Rank of 27/100, meaning its work is more exposed to current AI capabilities than approximately 27% of Singapore occupations. The evidence currently points to limited direct change; this is a relative rank, not a probability of job loss.
Plant & Machine Operators & Assemblers·SGD 4,705/mo (3,343–7,753)·~1.0K workers in SG·Updated 2026-07-17
Relative AI exposure, not a prediction of job loss. Hiring, wages and role design depend on many forces this rank does not forecast.
How This Rank Is Built
The source percentiles are combined using the displayed reliability weights, then ranked against all 562 Singapore occupations. This is not a percentage of tasks and not a job-loss probability.
Likely job pathway
Limited direct change
Current demand context
Mixed current demand
Pathway and demand are reported beside the exposure rank. Current demand does not change the rank.
The evidence behind this occupation's AI exposure, with human-work and demand context shown separately. How this works
Tasks AI can handle
With 31% AI task overlap (based on Felten AIOE, Anthropic Economic Index, and Eloundou GPT exposure), the Stationary plant and machine supervisor/general foreman tasks most exposed include: predictive maintenance scheduling, safety checklist automation, inventory management, and remote monitoring via sensors.
- • Identify or classify species of insects or allied forms, such as mites or spiders.
- • Communicate research or project results to other professionals or the public or teach related courses, seminars, or workshops.
- • Develop methods of conserving or managing soil that can be applied by farmers or forestry companies.
O*NET tasks for this occupation with the most observed Claude usage (Anthropic task data).
What AI can't do here
At 88% human bottleneck protection, the tasks that remain hardest to automate for Stationary plant and machine supervisor/general foreman include: physical dexterity on job sites, real-time environmental adaptation, operating heavy equipment safely, and handling unexpected on-site conditions.
Main insulation channels: Accountability for others + Relational work — the work-context dimensions behind this occupation's human bottleneck.
Skills to focus on
Sources: Felten AIOE (2021), Anthropic Economic Index (2026), Eloundou GPT Exposure (Science, 2024), Pizzinelli et al. bottleneck model. Full methodology.
From exposure to employment
What could change the employment outcome?
AI capability is only the starting point. Adoption, task design, human oversight, demand, mobility and evidence quality determine how exposure may resolve in practice.
Workplace adoption
Occupation-level adoption unknown
MOM does not publish a directly mappable adoption rate for the listed industries.
Task structure
Task evidence available
100% weighted task match · 6% effective coverage
Human advantage
Coordination and judgment still matter
At 88% human bottleneck protection, the tasks that remain hardest to automate for Stationary plant and machine supervisor/general foreman include: physical dexterity on job sites, real-time environmental adaptation, operating heavy equipment safely, and handling unexpected on-site conditions.
Hiring and demand
2.3% vacancy rate · 2026 Q1
Production and transport vacancies fell back from the Q4 spike and were slightly below a year earlier. Hiring remained net positive, retrenchments declined, and six-month re-entry improved. In 2025, this broad occupation group had 1.7% time-related underemployment and 10.3% non-permanent employment. Across the published male and female series, CPI-adjusted median income changed -4.1% to +5.0% from 2018 to 2023.
Career transitions
Metal heat treating plant operator
moderate modeled transition; outcomes still depend on skills, wages and openings.
Evidence strength
medium confidence
3 exposure sources · sensitivity 21–39/100 across source-weight sensitivity checks.
These lenses are reported separately and do not alter AI Exposure Rank. Labour-market figures describe a broad official occupation cluster, not this SSOC occupation alone.
Singapore Now
Current labour market conditions and how they affect this role.
Cooling, but not collapsing. Vacancies are softer, yet retrenchment remains low and hiring still exceeds resignations.
Vacancy
2.3%
↓ 4.2% YoY
Hiring
1.7%
vs 1.2% resign
Retrenchment
0.3
per 1,000 · low
Re-entry
76.9%
find work in 12mo· -1.2pp
Production & Transport Operators, Cleaners & Labourers · 2026 Q1
Top Industries
Industry vacancy overlays use the latest published detailed cross-tab, which can lag the main labour monitor.
What You Can Do
Stationary plant and machine supervisor/general foreman still has credible offset paths. Demand persists, adjacent moves look viable, and enough of the work appears reorganizable around AI.
Published transition support
Related roles you could transition to
Similarity-basedMetal heat treating plant operator →
Excavating/Trench digging machine operator →
Crane/Hoist operator (excluding port) →
See 5 more
Building caretaker/Watchman →
Office, commercial and industrial establishments multi-skilled cleaner cum machine operator →
Metalworking machine setter-operator →
Precision grinding machine setter-operator →
Aircraft loader (e.g. airport baggage/cargo handler) →
Compare within Plant & Machine Operators & Assemblers
See how this compares to similar occupations
Compare with... →Classification
More exposed than approximately 27% of occupations · V8 AI Exposure Rank· Polytechnic / ITE Diploma
Raw scores
AIOE -0.085 · θ 0.751 · C-AIOE -0.062
Stability
stable · Optimistic 1% · Pessimistic 4%
Score range (best/worst case)
Exposure sensitivity 20–43% · Rank sensitivity 21–39/100 across source-weight sensitivity checks
Scoring basis
V8 AI Exposure Rank. A relative Singapore occupation index. It ranks AI task exposure; it is not a probability of job loss or a percentage of tasks.
Wage range (SGD/mo)
25th 3,343 · Median 4,705 · 75th 7,753
Evidence & sources
Data matching
direct · SSOC 81000
Real-world AI usage: -19% vs estimated
Data quality
medium evidence · 3 exposure sources · direct mapping
100% weighted task match · 6% effective coverage
AI overlap by data source
Weights: aioe 32% · anthropic 35% · eloundou 33%
Conflicting data signals
Tools & offset factors
What helps
- Nearby moves and published transition support look reasonably strong.
- A meaningful share of the work can likely be reorganized around AI rather than removed outright.
Worker profile & local context
- Vacancy rate is 2.3% and fell by 0.5 points from last quarter.
- Hiring read: recruitment is running above resignation (1.7% vs 1.2%).
- Retrenchment was low at 0.3 per 1,000 employees.
- 76.9% of retrenched workers re-entered employment within 12 months.
- Employer pressure is low, based on 1 captured Singapore-relevant company signals through 2025-10-06.
Worker profile
Gender mix
47% male / 53% femalePublished Singapore worker composition for the detailed occupation family 81 Stationary Plant & Machine Operators.
Employment structure
More self-employed49% employees, 51% employers or self-employed workers.
Work arrangement
Mostly full-time11% part-time and 89% full-time in 2025.
Age profile
Older-skewing4% aged 15 to 29, 27% aged 30 to 49, and 70% aged 50 or older.
Qualification mix
Non-degree heavyBelow secondary 35%; Secondary 28%.
Gross wage by sex
Female median 37% lowerPublished June 2024 gross wage medians: male $5,382, female $3,380.
Where this work is concentrated
Top planning areas
Jurong West, Woodlands, Tampines26% of workers in this occupation group live in these three planning areas.
Residential concentration
More concentrated39% live across the top five planning areas in the 2020 Census.
Commute pattern
Shorter commutesEstimated average commute 21.5 minutes. 14% take 46 minutes or more.
Role profile
How this role's work breaks down across key dimensions. This is a general profile, not an individual measurement.
General role profile on a 0–100 scale. These are modelling inputs, not measurements of an individual job or worker.
How this changes by career stage
Career stage can change the task mix and human context. These directional profiles are illustrative, not occupation-level forecasts of hiring or displacement.
Frequently asked questions
Will AI replace Stationary plant and machine supervisor/general foreman?
Stationary plant and machine supervisor/general foreman has an AI Exposure Rank of 27/100, meaning its work is more exposed to current AI capabilities than approximately 27% of Singapore occupations. The evidence currently points to limited direct change; this is a relative rank, not a probability of job loss. AI Exposure Rank: 27/100 (Low). Median wage: SGD 4,705/month.
What is the AI exposure rank for Stationary plant and machine supervisor/general foreman?
Stationary plant and machine supervisor/general foreman has an AI Exposure Rank of 27/100, rated Low. It ranks higher than approximately 27% of Singapore occupations for exposure to current AI capabilities; it is not a job-loss probability.
What career transitions are available for Stationary plant and machine supervisor/general foreman?
Stationary plant and machine supervisor/general foreman has modeled transition pathways to related occupations. The strongest adjacent pathway is Metal heat treating plant operator, based on skill and wage similarity (model-estimated). Transition scoring accounts for wage preservation, training ease, and destination quality.
How does Stationary plant and machine supervisor/general foreman salary compare in the live market?
Stationary plant and machine supervisor/general foreman earns a median gross wage of SGD 4,705/month in the live market (25th-75th percentile: SGD 3,343-7,753). This is 5% above median across all 562 scored occupations, and 76% above group median within Plant & Machine Operators & Assemblers occupations.