75
/100

SOC 51-4022

Forging Machine Setters, Operators, and Tenders, Metal and Plastic

High RiskFrey/Osborne: 93.0%

Risk Score

โš ๏ธ

75/100

High Risk

US Employment

๐Ÿ‘ฅ

8,760

Total workers

Median Wage

๐Ÿ’ฐ

$49K

$35K โ€“ $70K

Projected Growth

๐Ÿ“ˆ

-18.9%

2023-2033 (BLS)

GenAI Exposure

๐Ÿค–

10/100

Low exposure

How we calculate these numbers โ†’

๐Ÿ’ก Forging Machine Setters, Operators, and Tenders, Metal and Plastic face a risk score of 75/100 โ€” 31 points above the national average of 44. With only 10/100 GenAI exposure, most core tasks remain resistant to current AI capabilities. See our methodology โ†’

๐Ÿ’ก Workers in this field earn $49K ($3K above the national median). 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 75/100, Forging Machine Setters, Operators, and Tenders, Metal and Plastic faces significant automation pressure. Key threats include smart factory scheduling and production optimization and cobots handling repetitive material handling tasks. The 8,760 Americans in this role should actively develop skills in setup and calibration of custom production runs 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 Forging Machine Setters, Operators, and Tenders, Metal and Plastic?

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

โš ๏ธ Top Risk Factors

1

Smart factory scheduling and production optimization

2

Cobots handling repetitive material handling tasks

3

Predictive maintenance reducing manual inspection roles

4

Automated CNC programming and machine operation

5

AI quality inspection via computer vision systems

๐Ÿ›ก๏ธ Tasks Safe from Automation

โœ“

Setup and calibration of custom production runs

โœ“

Coordinating workflow across diverse production teams

โœ“

Quality judgment requiring tactile and visual inspection

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

Read work orders or blueprints to determine specified tolerances and sequences of operations for machine setup.

โ€ข

Position and move metal wires or workpieces through a series of dies that compress and shape stock to form die impressions.

โ€ข

Measure and inspect machined parts to ensure conformance to product specifications.

โ€ข

Set up, operate, or tend presses and forging machines to perform hot or cold forging by flattening, straightening, bending, cutting, piercing, or other operations to taper, shape, or form metal.

โ€ข

Turn handles or knobs to set pressures and depths of ram strokes and to synchronize machine operations.

๐Ÿ’ป Technology Skills

โ€ข

Electronic mail software

โ€ข

Inventory management software

โ€ข

Industrial control software

๐ŸŽ“ Key Knowledge Areas

โ€ข

Production and Processing

โ€ข

Mathematics

โ€ข

Education and Training

โ€ข

Mechanical

โ€ข

Engineering and Technology

๐Ÿ“Š vs National Average

Median Wage$49K
+$3K

National avg: $46K

Risk Score75/100
+31

National avg: 44/100

GenAI Exposure10/100
-28

National avg: 38/100

Projected Growth-18.9%
-22.6%

National avg: 3.7%

๐Ÿ”„ Career Transition Paths

OccupationRiskWageOverlap
Engineers20$106K55%
First-Line Supervisors of Transportation and Material Moving Workers, Except Aircraft Cargo Handling Supervisors25$62K65%
Fabric and Apparel Patternmakers33$68K84%