72
/100

SOC 51-6064

Textile Winding, Twisting, and Drawing Out Machine Setters, Operators, and Tenders

High RiskFrey/Osborne: 96.0%

Risk Score

โš ๏ธ

72/100

High Risk

US Employment

๐Ÿ‘ฅ

20,600

Total workers

Median Wage

๐Ÿ’ฐ

$38K

$30K โ€“ $47K

Projected Growth

๐Ÿ“ˆ

-9%

2023-2033 (BLS)

GenAI Exposure

๐Ÿค–

38/100

Moderate exposure

How we calculate these numbers โ†’

๐Ÿ’ก Textile Winding, Twisting, and Drawing Out Machine Setters, Operators, and Tenders face a risk score of 72/100 โ€” 28 points above the national average of 44. With only 38/100 GenAI exposure, most core tasks remain resistant to current AI capabilities. See our methodology โ†’

๐Ÿ’ก Workers in this field earn $38K. The 3 recommended career transitions all maintain competitive wages while reducing automation exposure. Explore transition paths โ†’

๐Ÿ” AI Impact Analysis

With a risk score of 72/100, Textile Winding, Twisting, and Drawing Out Machine Setters, Operators, and Tenders faces significant automation pressure. Key threats include industrial robotics replacing manual assembly tasks and ai quality inspection via computer vision systems. The 20,600 Americans in this role should actively develop skills in handling non-standard materials and configurations and coordinating workflow across diverse production teams to remain competitive. Workers who proactively adapt will find new opportunities even as traditional tasks are automated.

Will AI Replace Textile Winding, Twisting, and Drawing Out Machine Setters, Operators, and Tenders?

Read our full analysis with verdict, risk factors, safe tasks, and career transition paths โ†’

โš ๏ธ Top Risk Factors

1

Industrial robotics replacing manual assembly tasks

2

AI quality inspection via computer vision systems

3

Automated CNC programming and machine operation

4

Cobots handling repetitive material handling tasks

5

Predictive maintenance reducing manual inspection roles

๐Ÿ›ก๏ธ Tasks Safe from Automation

โœ“

Handling non-standard materials and configurations

โœ“

Coordinating workflow across diverse production teams

โœ“

Setup and calibration of custom production runs

๐Ÿ“Š Task Automation Breakdown

Based on O*NET task analysis and GenAI exposure scoring. Shows the estimated proportion of this occupation's core tasks that are automatable by current AI, augmented by AI tools, or require essential human skills.

๐Ÿ“‹ O*NET Task Profile

โ€ข

Notify supervisors or mechanics of equipment malfunctions.

โ€ข

Thread yarn, thread, or fabric through guides, needles, and rollers of machines.

โ€ข

Start machines, monitor operation, and make adjustments as needed.

โ€ข

Inspect machinery to determine whether repairs are needed.

โ€ข

Record production data such as numbers and types of bobbins wound.

๐Ÿ’ป Technology Skills

โ€ข

Computer aided manufacturing CAM software

โ€ข

Spreadsheet software

โ€ข

Office suite software

โ€ข

Electronic mail software

โ€ข

Word processing software

๐ŸŽ“ Key Knowledge Areas

โ€ข

Production and Processing

โ€ข

Administration and Management

โ€ข

Mechanical

โ€ข

Public Safety and Security

โ€ข

Personnel and Human Resources

๐Ÿ“Š vs National Average

Median Wage$38K
$-9K

National avg: $46K

Risk Score72/100
+28

National avg: 44/100

GenAI Exposure38/100
0

National avg: 38/100

Projected Growth-9.0%
-12.7%

National avg: 3.7%

๐Ÿ”„ Career Transition Paths

OccupationRiskWageOverlap
Engineers20$106K69%
First-Line Supervisors of Transportation and Material Moving Workers, Except Aircraft Cargo Handling Supervisors25$62K59%
Fabric and Apparel Patternmakers33$68K78%