Metal melter, caster and rolling mill operator
AI displacement risk
7%
Range 5.33–8.72%
Metal melter, caster and rolling mill operator has 18% AI task overlap and 49% human bottleneck protection — lower risk than 69% of occupations in the live market. Current AI capabilities have limited overlap with core tasks.
Plant & Machine Operators & Assemblers·SGD 2,671/mo (2,568–3,000)·~900 workers in SG·Updated 2026-06-11
Structural pressure, not a prediction of job loss. Displacement tends to arrive through slower hiring, wage compression and role redesign before layoffs.
Why This Score
18% of tasks overlap with current AI
49% human advantage from judgment & presence
46% demand buffer from the local labour market
AI usage 7pp below theoretical exposure
These factors interact with each other — the final score is not a simple sum of these bars.
How much AI overlaps with this job's tasks, offset by human advantages and local demand. Score stability: watch. How this works
Tasks AI can handle
With 18% AI task overlap (based on Felten AIOE, Anthropic Economic Index, Eloundou GPT exposure, and ILO occupational exposure), the Metal melter, caster and rolling mill operator tasks most exposed include: predictive maintenance scheduling, safety checklist automation, inventory management, and remote monitoring via sensors.
- • Pour and regulate the flow of molten metal into molds and forms to produce ingots or other castings, using ladles or hand-controlled mechanisms.
- • Read temperature gauges and observe color changes, adjusting furnace flames, torches, or electrical heating units as necessary to melt metal to specifications.
- • Remove solidified steel or slag from pouring nozzles, using long bars or oxygen burners.
O*NET tasks for this occupation with the most observed AI usage (Anthropic task data).
What AI can't do here
At 49% human bottleneck protection, the tasks that remain hardest to automate for Metal melter, caster and rolling mill operator 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 + 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), Eloundou GPT Exposure (Science, 2024), ILO GenAI (2025), Pizzinelli et al. bottleneck model. Full methodology.
Singapore Now
Current labour market conditions and how they affect this role.
Still healthy locally. Hiring remains positive and retrenchment stays low, even if demand is not accelerating.
Vacancy
2.8%
↑ 16.7% YoY
Hiring
2.4%
vs 1.5% resign
Retrenchment
1.5
per 1,000 · low
Re-entry
78.1%
find work in 12mo· -4.5pp
Production & Transport Operators, Cleaners & Labourers · 2025 Q4
Top Industries
Industry vacancy overlays use the latest published detailed cross-tab, which can lag the main labour monitor.
What You Can Do
Metal melter, caster and rolling mill operator 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-basedMetal heat treating plant operator →
Metalworking machine setter-operator →
Stationary plant and machine supervisor/general foreman →
See 5 more
Precision grinding machine setter-operator →
Building caretaker/Watchman →
Excavating/Trench digging machine operator →
Dairy and confectionery products machine operator →
Sports and recreational attendant (e.g. golf marshal, golf caddie, fun fair attendant, bowling alley attendant, swimming →
Compare within Plant & Machine Operators & Assemblers
See how this compares to similar occupations
Compare with... →Classification
Higher risk than 31% of occupations · V7 structural score
Raw scores
AIOE -0.511 · θ 0.673 · C-AIOE -0.415
Stability
watch · Optimistic 4% · Pessimistic 12%
Score range (best/worst case)
Exposure 16–21% · Net risk 5.33–8.72%
Scoring basis
V7 structural score. Uses task-concentration-weighted exposure, human bottleneck, and demand resilience. V6 baseline fields are retained for release-to-release comparison.-0.6pp versus retained V6 baseline.
Wage range (SGD/mo)
25th 2,568 · Median 2,671 · 75th 3,000
Evidence & sources
Data matching
direct · SSOC 81210
Real-world AI usage: -7% vs estimated
Data quality
87% · Matching 1.00 · Market data 0.65 · Freshness 0.84
100% weighted task match · 0% effective coverage
AI overlap by data source
Weights: aioe 24% · anthropic 26% · eloundou 25% · ilo 26%
Tools & offset factors
What helps
- Nearby moves and published transition support look reasonably strong.
Worker profile & local context
- Vacancy rate is 2.8% and rose by 0.8 points from last quarter.
- Hiring read: recruitment is running above resignation (2.4% vs 1.5%).
- Retrenchment was low at 1.5 per 1,000 employees.
- 78.1% of retrenched workers re-entered employment within 12 months.
- Employer pressure is low, based on 1 recent Singapore-relevant company signals.
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%.
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.
Workflow dimensions (0 = low, 1 = high)
How this changes by career stage
Senior workers benefit from institutional knowledge and judgment that AI cannot replicate. Entry-level roles have higher task overlap with AI.
Frequently asked questions
Will AI replace Metal melter, caster and rolling mill operator?
Metal melter, caster and rolling mill operator has 18% AI task overlap and 49% human bottleneck protection — lower risk than 69% of occupations in the live market. Current AI capabilities have limited overlap with core tasks. Net displacement risk: 7% (Low). Median wage: SGD 2,671/month.
What is the AI risk score for Metal melter, caster and rolling mill operator?
Metal melter, caster and rolling mill operator has an AI displacement risk of 7%, rated Low. AI task overlap: 18%. Human advantage: 49%. Local demand buffer: 46%.
What career transitions are available for Metal melter, caster and rolling mill operator?
Metal melter, caster and rolling mill operator 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 Metal melter, caster and rolling mill operator salary compare in the live market?
Metal melter, caster and rolling mill operator earns a median gross wage of SGD 2,671/month in the live market (25th-75th percentile: SGD 2,568-3,000). This is 41% below median across all 562 scored occupations, and near group median within Plant & Machine Operators & Assemblers occupations.