Skip to content
man bot

Plant and machine operators, and assemblers · code 8159

Textile, fur and leather products machine operators not elsewhere classified

This unit group covers textile, fur and leather products machine operators not classified elsewhere in Minor Group 815: Textile, Fur and Leather Products Machine Operators. For instance, the group includes those engaged in operating and monitoring machines which make hats, tents, mattresses or miscellaneous articles such as braids or other trimmings. In such cases tasks would include - (a) operating and monitoring machines which form and make hats out of textiles, fur or leather; (b) operating and monitoring machines which make miscellaneous articles such as braids or other trimmings; (c) operating and monitoring machines which fold cloth into measured length; (d) operating and monitoring machines which wind thread, twine or yarn into balls preparatory to shipping or further processing; (e) operating and monitoring machines to measure size of pieces of leather. Examples of the occupations classified here: - Braid making machine operator - Hat making machine operator - Tent making machine operator - Textile patternmaking machine operator

None of this job's tasks scored high enough to count. It ranks 83 of 427 for holding up against AI.

Also known as

  • textile pattern making machine operator
  • mattress making machine operator
  • braiding textile technician
  • nonwoven staple machine operator
  • nonwoven textile technician
  • braiding machine operator
  • leather measuring operator
  • canvas goods assembler

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

84.2
Holds up
against AI Not Exposed

AI could take on 15.8 of 100, on average
so it holds up at 100 − 15.8 = 84.2

That's the whole sum, and you can check it against the ILO's study.

This number is probably too high

This score only looks at AI tools like chatbots. It doesn't look at robots at all — and this is hands-on work, which is exactly where robots come in.

Have a look at the tasks below and judge for yourself. The ILO scored them low because a chatbot can't do them. Whether a machine with hands could is a different question, and we can't answer it yet.

The tasks behind the score

The score is the average of these. A job is really a bundle of tasks, and an average can hide how uneven that bundle is.

The task AI could do most of scores 16.8, while the average is 15.8. A big gap means some of this job is wide open to AI and some of it barely at all. That's quite different from a job where everything sits somewhere in the middle, even when the averages match.

  1. 16.8

    Operating and monitoring machines to measure size of pieces of leather.

    Very Low
  2. 16.5

    Operating and monitoring machines which form and make hats out of textiles, fur or leather

    Very Low
  3. 16.5

    Operating and monitoring machines which wind thread, twine, or yarn into balls preparatory to shipping or further processing

    Very Low
  4. 15.5

    Operating and monitoring machines which make miscellaneous articles such as braids or other trimmings

    Very Low
  5. 13.5

    Operating and monitoring machines which fold cloth into measured length

    Very Low

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

from the source Working Paper 140, Generative AI and Jobs 2025 index data 0.7-work-setting

Helped, or replaced?

The score above can't tell a tool that makes you faster from one that does the task instead of you. Jobs and Skills Australia looked at those two things separately, and this is their result, matched to this job.

One Australian job matches this one closely, so this is about as reliable as it gets here.

AI helps you
56
AI does it for you
24

Helping wins by 32 points.

estimated Our Gen AI Transition (Gen AI Capacity Study) September 2025 detailed data release data 0.7-work-setting matched to Australian jobs 7117

Compared with similar jobs

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