Supervisor/General foreman (metal, machinery and related trades)
AI Exposure Rank
27/100
Range 21–39/100 across source-weight sensitivity checks
Supervisor/General foreman (metal, machinery and related trades) 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.
Craftsmen & Related Trades Workers·SGD 7,712/mo (4,048–9,090)·~1.8K 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. Score stability: watch. How this works
Tasks AI can handle
With 31% AI task overlap (based on Felten AIOE, Anthropic Economic Index, and Eloundou GPT exposure), the Supervisor/General foreman (metal, machinery and related trades) tasks most exposed include: predictive maintenance scheduling, safety checklist automation, inventory management, and remote monitoring via sensors.
- • Read work orders or blueprints to determine specified tolerances and sequences of operations for machine setup.
- • Position and move metal wires or workpieces through a series of dies that compress and shape stock to form die impressions.
- • Measure and inspect machined parts to ensure conformance to product specifications.
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 Supervisor/General foreman (metal, machinery and related trades) 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 limited
100% weighted task match · 0% effective coverage
Human advantage
Coordination and judgment still matter
At 88% human bottleneck protection, the tasks that remain hardest to automate for Supervisor/General foreman (metal, machinery and related trades) 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 2.2% time-related underemployment and 9.5% non-permanent employment. Across the published male and female series, CPI-adjusted median income changed -8.0% to -6.4% from 2018 to 2023.
Career transitions
Industrial/Office machinery mechanic
stretch 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
Supervisor/General foreman (metal, machinery and related trades) has some offset potential, but it depends on task redesign holding up in practice and on workers clearing the main switching frictions.
Published transition support
Related roles you could transition to
Similarity-basedCompare within Craftsmen & Related Trades Workers
Supervisor/General foreman (food processing, woodworking, garment, leather and related trades) →
Supervisor/General foreman (precision, handicraft, printing and related trades) →
Floor/Wall tiler →
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
watch · Optimistic 1% · Pessimistic 5%
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 4,048 · Median 7,712 · 75th 9,090
Evidence & sources
Data matching
direct · SSOC 72000
Real-world AI usage: -19% vs estimated
Data quality
medium evidence · 3 exposure sources · direct mapping
100% weighted task match · 0% 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
92% male / 8% femalePublished Singapore worker composition for the detailed occupation family 72 Metal, Machinery & Related Trades Workers.
Employment structure
Employee-heavy85% employees, 15% employers or self-employed workers.
Work arrangement
Mostly full-time13% part-time and 87% full-time in 2025.
Age profile
Older-skewing7% aged 15 to 29, 25% aged 30 to 49, and 67% aged 50 or older.
Qualification mix
Non-degree heavyBelow secondary 38%; Secondary 27%.
Where this work is concentrated
Top planning areas
Woodlands, Jurong West, Yishun25% 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
Mid-range commutesEstimated average commute 35.1 minutes. 28% 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 Supervisor/General foreman (metal, machinery and related trades)?
Supervisor/General foreman (metal, machinery and related trades) 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 7,712/month.
What is the AI exposure rank for Supervisor/General foreman (metal, machinery and related trades)?
Supervisor/General foreman (metal, machinery and related trades) 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 Supervisor/General foreman (metal, machinery and related trades)?
Supervisor/General foreman (metal, machinery and related trades) has modeled transition pathways to related occupations. The strongest adjacent pathway is Excavating/Trench digging machine operator, based on skill and wage similarity (model-estimated). Transition scoring accounts for wage preservation, training ease, and destination quality.
How does Supervisor/General foreman (metal, machinery and related trades) salary compare in the live market?
Supervisor/General foreman (metal, machinery and related trades) earns a median gross wage of SGD 7,712/month in the live market (25th-75th percentile: SGD 4,048-9,090). This is 71% above median across all 562 scored occupations, and 138% above group median within Craftsmen & Related Trades Workers occupations.