Mechanical equipment/component assembler
AI Exposure Rank
25/100
Range 15–35/100 across source-weight sensitivity checks
Mechanical equipment/component assembler has an AI Exposure Rank of 25/100, meaning its work is more exposed to current AI capabilities than approximately 25% 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 2,084/mo (1,481–3,025)·~700 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 28% AI task overlap (based on Felten AIOE, Anthropic Economic Index, and ILO occupational exposure), the Mechanical equipment/component assembler tasks most exposed include: predictive maintenance scheduling, safety checklist automation, inventory management, and remote monitoring via sensors.
- • Inspect, test, and adjust completed units to ensure that units meet specifications, tolerances, and customer order requirements.
- • Position, align, and adjust parts for proper fit and assembly.
- • Assemble parts or units, and position, align, and fasten units to assemblies, subassemblies, or frames, using hand tools and power tools.
O*NET tasks for this occupation with the most observed Claude usage (Anthropic task data).
What AI can't do here
At 12% human bottleneck protection, the tasks that remain hardest to automate for Mechanical equipment/component assembler include: physical dexterity on job sites, real-time environmental adaptation, operating heavy equipment safely, and handling unexpected on-site conditions.
Main insulation channels: Non-routine work + High-stakes decisions — the work-context dimensions behind this occupation's human bottleneck.
Skills to focus on
Sources: Felten AIOE (2021), Anthropic Economic Index (2026), ILO GenAI (2025), 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 12% human bottleneck protection, the tasks that remain hardest to automate for Mechanical equipment/component assembler 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
Mechanical products quality checker and tester
moderate modeled transition; outcomes still depend on skills, wages and openings.
Evidence strength
medium confidence
3 exposure sources · sensitivity 15–35/100 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
Mechanical equipment/component assembler has some offset potential, but it depends on transition pathways holding up in practice and on workers clearing the main switching frictions.
Published transition support
Related roles you could transition to
Similarity-basedMover →
Waste recycling sorter →
Mechanical products quality checker and tester →
See 5 more
Electronic equipment/component assembler →
Aircraft loader (e.g. airport baggage/cargo handler) →
Vehicle attendant →
Tree worker/technician →
Office, commercial and industrial establishments multi-skilled cleaner cum machine operator →
Compare within Plant & Machine Operators & Assemblers
See how this compares to similar occupations
Compare with... →Classification
More exposed than approximately 24% of occupations · V8 AI Exposure Rank· GCE O-Level / Secondary
Raw scores
AIOE -1.042 · θ 0.611 · C-AIOE -0.911
Stability
watch · Optimistic 13% · Pessimistic 25%
Score range (best/worst case)
Exposure sensitivity 14–41% · Rank sensitivity 15–35/100 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 1,481 · Median 2,084 · 75th 3,025
Evidence & sources
Data matching
direct · SSOC 82110
Real-world AI usage: +41% 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 31% · anthropic 34% · ilo 35%
Conflicting data signals
Tools & offset factors
What helps
- Nearby moves and published transition support look reasonably strong.
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
38% male / 62% femalePublished Singapore worker composition for the detailed occupation family 82 Assemblers & Quality Checkers.
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 44% lowerPublished June 2024 gross wage medians: male $2,856, female $1,600.
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 Mechanical equipment/component assembler?
Mechanical equipment/component assembler has an AI Exposure Rank of 25/100, meaning its work is more exposed to current AI capabilities than approximately 25% 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: 25/100 (Low). Median wage: SGD 2,084/month.
What is the AI exposure rank for Mechanical equipment/component assembler?
Mechanical equipment/component assembler has an AI Exposure Rank of 25/100, rated Low. It ranks higher than approximately 25% of Singapore occupations for exposure to current AI capabilities; it is not a job-loss probability.
What career transitions are available for Mechanical equipment/component assembler?
Mechanical equipment/component assembler has modeled transition pathways to related occupations. The strongest adjacent pathway is Mover, based on skill and wage similarity (model-estimated). Transition scoring accounts for wage preservation, training ease, and destination quality.
How does Mechanical equipment/component assembler salary compare in the live market?
Mechanical equipment/component assembler earns a median gross wage of SGD 2,084/month in the live market (25th-75th percentile: SGD 1,481-3,025). This is 54% below median across all 562 scored occupations, and 22% below group median within Plant & Machine Operators & Assemblers occupations.