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Plant and machine operators, and assemblers · code 8151

Fibre preparing, spinning and winding machine operators

The spinning frames run themselves. Someone still has to keep the yarn from snapping.

AI barely touches any of the twelve tasks, from blending fibres and recycling wool waste to waterproofing textiles and cleaning carding machines.

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

Fibre preparing, spinning and winding machine operators monitor and operate machines which prepare fibres, and spin, double, twist and wind yarn and thread from natural textile fibres. They twist two or more strands of yarn to prepare single stronger, smoother, uniform and heavier strands, and treat textiles to make them stiff and water resistant. Tasks include - (a) operating and monitoring machines for tearing woollen rags into fibre; (b) operating and monitoring machines for cleaning and turning woollen yarn waste into fluffed wool; (c) operating and monitoring machines which combine textile fibres into uniform blends; (d) operating and monitoring machines which clean and fluff textile fibres, transform them into sliver, comb them into sliver for first drawing, combine slivers into sliver lap or sliver laps into ribbon lap; (e) operating draw frames (machines) having numbers of drawing sets, each set combining several slivers into one of nearly the same weight and thickness as any of the original slivers; (f) operating and monitoring machines which spin thread and yarn from roving, wind two or more threads onto a bobbin, twist two or more strands of yarn or thread into single strands of yarn to increase strength, smoothness and/or uniformity of yarn, or wind yarn or thread from one package to another; (g) operating and monitoring spinning frames that draw out and twist roving or sliver into yarn; (h) operating and monitoring machines for drawing slivers received from drawing machines into loosely twisted strands; (i) preparing sizing for use in stiffening and finishing cloth and yarn by mixing ingredients such as starch, tallow, resins and soaps with water and boiling mixture for specified times in steam; (j) treating textiles with chemicals to make them water resistant; (k) cleaning rollers and cylinders of carding machines to remove wool waste; (l) operating and resurfacing metal drawing rolls of various spinning, combing and lapping machines with new rubber or leather covers. Examples of the occupations classified here: - Fibre combing machine operator - Fibre preparing machine operator - Thread and yarn spinning machine operator - Thread and yarn twisting machine operator - Thread and yarn winding machine operator

  • spinning textile technician
  • cotton gin operator
  • spinning machine operator
  • winding machine operator

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

85 / 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

80 of 427 for holding up against AI

Why this score?

The ILO rated each of this job's 12 tasks for how much of it AI could do. They average 15.5 out of 100, so it holds up at 100 − 15.5 = 84.5.

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 54 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?

Fibre preparing, spinning and winding machine operators 85

v

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.

  • Machine settings and monitoring.
  • Yarn quality testing.
  • Production records.

Man

What people still do better

Judgement, trust and skilled hands. Hard to download.

  • Fixing breaks and jams.
  • Maintaining rollers and machinery.
  • Judging fibre and yarn quality.

Both

What's arriving

New work that only exists because the AI does.

  • Supervising more automated mills.
  • Recycled fibres and new materials.
  • Traceability in textile supply chains.

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 56 of 100.

The robot

The wildcard. Has hands, still working out stairs.

How much it matters here: Some

54 how hands-on

A fair part of this job is hands-on, so robots would be needed for that part, not just software.

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.

54 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, matched to one US job .

finger skill 53.6 · hand skill 48.3 · cramped spaces 21 · 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 18, against an average of 16. The bigger the gap, the more lopsided the job: some of it wide open to AI, some of it barely touched.

  1. 18

    Operating and monitoring machines which combine textile fibres into uniform blends

    Very Low
  2. 17

    Operating and monitoring machines for cleaning and turning woollen yarn waste into fluffed wool

    Very Low
  3. 17

    Operating and monitoring spinning frame that draws out and twists roving or sliver into yarn

    Very Low
  4. 17

    Operating and monitoring machines which clean and fluff textile fibres, transform them into sliver, comb them into sliver for first drawing, combine slivers into sliver lap or sliver laps into ribbon lap

    Very Low
  5. 17

    Operating and monitoring machines for drawing slivers received from drawing machine into loosely twisted strands

    Very Low
  6. 16

    Operating and monitoring machines which spin thread and yarn from roving, wind two or more threads onto bobbin, twist two or more strands of yarn or thread into single strands of yarn to increase strength, smoothness and/or uniformity of yarn, or wind yarn or thread from one package to another

    Very Low
  7. 16

    Operating and resurfacing metal drawing rolls of various spinning, combing and lapping machines with new rubber or leather covers.

    Very Low
  8. 15

    Operating and monitoring machines for tearing woollen rags into fibre

    Very Low
  9. 15

    Preparing sizing for use in stiffening and finishing cloth and yarn by mixing ingredients such as starch, tallow, resins, soaps with water and boiling mixture for specified time in steam

    Very Low
  10. 15

    Operating draw frame (machine) having number of drawing sets, each set combining several slivers into one of nearly same weight and thickness as any of original sliver

    Very Low
  11. 14

    Treating textiles with chemicals to make them water resistant

    Very Low
  12. 11

    Cleaning rollers and cylinders of carding machines to remove wool waste

    Very Low

12 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
56
AI does it for you
24

Sidekick wins, by 32 points.

How good a match is this?

One Australian job matches, but only partly: not everyone in it does this job. 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 7117

So, should spinning and winding operators be worried?

Short answer: not really. Put the kettle on.

Not about AI. Every task rates Very Low, from blending fibres to spinning and winding. Textile mills are highly automated already; operators watch the machines, fix breaks and keep quality up.

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 →