Plant and machine operators, and assemblers · code 8142

Plastic products machine operators

The moulding machine runs itself. Someone still checks what comes out.

Checking output for defects is mostly out of AI's reach. It barely touches six of the seven tasks, including laminating plastics and making glasses frames and contact lens discs.

What they actually do (and what else they get called)

Plastic products machine operators monitor and operate machines which knead and blend compounds to obtain plastic materials and which make various plastic components and articles. Tasks include - (a) operating and monitoring machines which knead and blend compounds to obtain plastic materials; (b) operating and monitoring machines which shape plastic materials by moulding, extrusion, blowing, cutting and other means; (c) operating and monitoring machines which laminate plastics and plastic-impregnated materials or produce fibreglass; (d) encasing uncoated wire, cord, cable and optic fibre in plastic; (e) examining outputs for defects and conformity to specifications; (f) recycling waste plastic materials; (g) making artificial eyes and contact lens discs, making and repairing spectacle frames and plastic parts of orthopaedic appliances. Examples of the occupations classified here: - Laminated press operator (plastics) - Optic fibre drawer - Plastic cable making machine operator - Plastic extrusion machine operator - Plastic moulder - Plastics boat builder - Plastics bottle blowing machine operator - Plastics products machine operator

fiberglass laminator
plastic rolling machine operator
fibreglass machine operator
plastic furniture machine operator
record press operator
pultrusion machine operator
injection moulding operator
plastic heat treatment equipment operator
filament winding operator
cake press operator
blow moulding machine operator
vacuum forming machine operator
compression moulding machine operator
insulating tube winder

Job titles from the EU's ESCO list. Search for any of them on the jobs page and you'll end up here.

83 / 100

Strongly holds up against AI

Most of this job still needs an actual human. AI would mostly be making the tea.

Not the odds you keep your job. It's how much of the everyday work still needs a person, out of 100.

← Robot's turf Human's turf →

The ILO's official verdict: Not Exposed

105 of 427 for holding up against AI

Why this score?

The ILO rated each of this job's 7 tasks for how much of it AI could do. They average 17.5 out of 100, so it holds up at 100 − 17.5 = 82.5.

That's the whole sum. No black box, no secret sauce: you can check it against the ILO's study. ILO group “Not Exposed”: None of this job's tasks scored high enough to count.

from the source Working Paper 140, Generative AI and Jobs 2025 index data 0.8-observed-use

Pinch of salt: this number is probably too rosy

It only asks what AI software could do, and this job is 68 out of 100 hands-on. A chatbot can't do that part. A robot might, one day, and nobody measures how close that is.

Man v Bot · compare

Who holds up better?

Plastic products machine operators 83

Go on, try your partner's job. Or your sibling's. We won't tell.

Man v Bot

Where the bot helps, and where the human wins

Bot

What AI takes

The routine stuff. You may not miss it.

Automated defect inspection.
Machine settings and monitoring.
Production records.

Man

What people still do better

Judgement, trust and skilled hands. Hard to download.

Setting up and adjusting machines.
Fixing faults.
Handling materials safely.

Both

What's arriving

New work that only exists because the AI does.

Recycled plastics processing.
Supervising automated lines.
3D printing for small runs.

Written by a human, from the ILO's task ratings below. Yes, we see the irony. Read it as one person's take.

Meet your two rivals

They're not the same machine. AI software can only reach the parts of a job done on a screen or in words. A chatbot can't climb a ladder. A robot might, one day, if it doesn't fall off.

The AI

Lives in a screen. Never had to fix a boiler.

Threat level: Low

24 could do without you

It could take over very little of the work on its own. It could help with 59 of 100.

The robot

The wildcard. Has hands, still working out stairs.

How much it matters here: Higher

68 how hands-on

Most of this job needs hands, a body or turning up. That's robot territory, and nobody measures how far robots have got well enough to give you a number. We won't make one up.

So could a robot do the physical part?

More fine handwork, and more time in cramped or awkward spots, than a typical hands-on job. That is exactly what machines find hardest: they do best in tidy, predictable places.

Robots hate fiddly work in awkward spaces. On that measure, this job scores 41 out of 100. The higher it is, the longer the robot waits.

68 How
hands-on

41 How fiddly
and cramped

Kind of hands-on work Hands-on, fiddly work in tight spaces

The small print

They aren't a robot score, and nothing on this page is. Nobody measures how far robots have got well enough to put a number on it. These are the closest thing we have: how hands-on the job is, and how much fiddly, cramped work it takes, which is what Frey and Osborne found hardest for machines.

From O*NET, averaged across 12 US jobs , so treat it as rough.

finger skill 44.3 · hand skill 44.5 · cramped spaces 34.8 · averaged · rule: hands-on 50+ · fiddly 39.4+

estimated O*NET 31.0 BLS occupational crosswalk chain ISCO-08 to 2010 SOC (Aug 2012, rev. Jun 2015); 2010 to 2018 SOC (Nov 2017); O*NET-SOC 2019 taxonomy data 0.8-observed-use measures chosen by Frey & Osborne (2017) · the grouping is a sorting, not a measurement

The job, task by task

Every job is really a bundle of tasks. Here's this one unpacked, with how much of each AI could do, out of 100. The score is the average, and averages can hide a lot.

AI's favourite task here scores 29, against an average of 18. The bigger the gap, the more lopsided the job: some of it wide open to AI, some of it barely touched.

28
Examining outputs for defects and conformity to specifications

Low
19
Operating and monitoring machines which laminate plastics and plastic-impregnated materials or produce fibreglass

Very Low
17
Operating and monitoring machines which knead and blend compounds to obtain plastic materials

Very Low
17
Operating and monitoring machines which shape plastic materials by moulding, extrusion, blowing, cutting and other means

Very Low
15
Recycling waste plastic materials

Very Low
14
Making artificial eyes and contact lens discs, making and repairing spectacle frames and plastic parts of orthopaedic appliances.

Very Low
13
Encasing uncoated wire, cord, cable and optic fibre in plastic

Very Low

7 tasks · the High to Very Low labels are the ILO's

from the source Working Paper 140, Generative AI and Jobs 2025 index data 0.8-observed-use

Sidekick or replacement?

A tool that makes you faster is a very different thing from one that does the task while you watch. Jobs and Skills Australia measured the two separately, which is handy.

AI helps you: 59
AI does it for you: 24

Sidekick wins, by 35 points.

How good a match is this?

2 Australian jobs match this one, and we've simply averaged them. Treat the numbers as a rough guide.

estimated Our Gen AI Transition (Gen AI Capacity Study) September 2025 detailed data release data 0.8-observed-use matched to Australian jobs 3991, 7115

So, should plastics machine operators be worried?

Short answer: not really. Put the kettle on.

Not about AI software. The most exposed task is examining products for defects, rated Low; running moulding, laminating and extrusion machines rates Very Low. Camera inspection and automation are spreading, so operators supervise lines and fix problems.

Worth remembering: we score tasks, not jobs. When AI takes on a task, the job usually reshuffles around it rather than vanishing.

Where this job could lead

None of the new jobs we've written up start from here yet. Could be you're the first. See the jobs AI is creating →

How are the neighbours doing?

There are 39 jobs in the “Plant and machine operators, and assemblers” family, averaging 80. This one is above the family average.

Pit this against any other job →