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Manufacturing & Production · Updated Aug 2026

Cleaning, Washing, and Metal Pickling Equipment Operators and Tenders

Operate or tend machines to wash or clean products, such as barrels or kegs, glass items, tin plate, food, pulp, coal, plastic, or rubber, to remove impurities.

JVS 2.0.0-phase4b
AI Exposure
41/100
Moderate

How much of this occupation’s work can be materially affected by current AI systems.

Replacement Risk
46/100
Moderate

How likely exposure is to translate into reduced human demand.

Includes provisional estimates for AI adoption pressure and labour-market resilience. How this is measured

Evidence quality
Confidence84/100
Task coverage91%

Confidence reflects task coverage, mapping and capability-evidence quality, and how much of the score rests on provisional inputs.

Task-level evidence

What is driving the score?

Occupation scores are built from the task mix—not a single prediction about a job title.

JVS 2.0.0-phase4b
TaskImportanceAI impactExposure
Set controls to regulate temperature and length of cycles, and start conveyors, pumps, agitators, and machines.High
66
Record gauge readings, materials used, processing times, or test results in production logs.Medium
69
Add specified amounts of chemicals to equipment at required times to maintain solution levels and concentrations.High
66
Observe machine operations, gauges, or thermometers, and adjust controls to maintain specified conditions.High
36
Draw samples for laboratory analysis, or test solutions for conformance to specifications, such as acidity or specific gravity.High
36
Drain, clean, and refill machines or tanks at designated intervals, using cleaning solutions or water.Medium
39
Operate or tend machines to wash and remove impurities from items such as barrels or kegs, glass products, tin plate surfaces, dried fruit, pulp, animal stock, coal, manufactured articles, plastic, or rubber.Medium
23
Adjust, clean, and lubricate mechanical parts of machines, using hand tools and grease guns.High
25
Load machines with objects to be processed and unload them after cleaning, placing them on conveyors or racks.Medium
25
Measure, weigh, or mix cleaning solutions, using measuring tanks, calibrated rods or suction tubes.Medium
25
Most exposed

Where AI can do more

Routine, digitized, and highly repeatable tasks face the greatest pressure.

  1. Record gauge readings, materials used, processing times, or test results in production logs.69
  2. Set controls to regulate temperature and length of cycles, and start conveyors, pumps, agitators, and machines.66
  3. Add specified amounts of chemicals to equipment at required times to maintain solution levels and concentrations.66
  4. Drain, clean, and refill machines or tanks at designated intervals, using cleaning solutions or water.39
Hardest to automate

Where people still matter

These tasks score lowest on automation feasibility—physical presence, judgement, accountability and real-world variability all resist end-to-end automation.

  1. Operate or tend machines to wash and remove impurities from items such as barrels or kegs, glass products, tin plate surfaces, dried fruit, pulp, animal stock, coal, manufactured articles, plastic, or rubber.01
  2. Adjust, clean, and lubricate mechanical parts of machines, using hand tools and grease guns.02
  3. Load machines with objects to be processed and unload them after cleaning, placing them on conveyors or racks.03
  4. Measure, weigh, or mix cleaning solutions, using measuring tanks, calibrated rods or suction tubes.04
  5. Set controls to regulate temperature and length of cycles, and start conveyors, pumps, agitators, and machines.05
Where else this work leads

Related occupations

Occupations O*NET links to this one. Relatedness reflects shared work, not a claim that these roles are safer.

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AI risk 49

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Closely related work

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Beyond AI capability

Adoption and labour-market outlook

Structural factors are kept separate from raw capability so you can see what actually resists automation. Adoption pressure and labour-market resilience are still provisional models—25% of this occupation’s replacement-risk weight rests on them.

Human dependency50
Physical dependency61
Adoption pressure56
Labour-market resilience59
Methodology & sources

O*NET 30.3 occupational data interpreted through the JobsVsAI capability, automation and structural-constraint models.

Confidence84/100
CalculatedAug 21, 2026
Read methodology →