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Production helpers

Official title Helpers, Production Workers (SOC 51-9198)

Also called Manufacturing helper, Factory helper, Assembly line helper, Production line worker, Material handler

In brief

In May 2025, BLS counted 165,700 US production helpers, with median pay of $39,070 a year. Of the BLS counts since 2003, the highest was 539,350, in May 2006. The earliest figure here is a Census count of 190,000 from 1870, not measured the same way as today's count. The last full technology era for production helpers: Collaborative robots and autonomous mobile robots, 2010 to 2023.

What the sources say

Official outlook

BLS projects employment to decline 8.2% from 2025 to 2035.

BLS Employment Projections 2025-35

AI exposure studies

  • 5.6% of this job's tasks are exposed to large language models. Not a forecast of job losses. Eloundou and others, 2023
  • 0.66 estimated probability that the whole occupation could be computerised, over an unspecified number of years. Not a forecast of job losses. Frey and Osborne, 2013

Since the 2013 prediction

In 2013, Frey and Osborne put this job's probability of computerisation at 0.66. BLS counted 426,670 of these jobs in May 2013 and 165,700 in May 2025 (-61%, a change derived by Future History).

Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. BLS publishes these counts as yearly survey snapshots and cautions against reading them as a precise trend. In May 2021 BLS changed how it estimates these counts (a model-based method using three years of survey data) and moved fully to the 2018 occupation codes, so part of a change across 2021 can come from the method rather than the work.

Last updated .

Cite this page
APA

Future History. (2026, October 11). Production helpers: jobs and pay since 1870 [Data set; data as of BLS OEWS May 2025; BLS Employment Projections 2025-35]. https://futurehistory.earth/timeline/51-9198

Chicago

Future History. "Production helpers: jobs and pay since 1870." Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. Last modified October 11, 2026. https://futurehistory.earth/timeline/51-9198.

BibTeX

@misc{futurehistory_51_9198, author = {{Future History}}, title = {Production helpers: jobs and pay since 1870}, year = {2026}, month = oct, howpublished = {\url{https://futurehistory.earth/timeline/51-9198}}, note = {Last updated 2026-10-11. Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35}, }

The access date is today's date on your device.

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18251850187519001925195019752000now
2026
Known then as Factory Hand / Mill Hand / Manufacturing Laborer (early factory era)
Known then as Factory Helper / Shop Helper (early mechanization)
Known then as Assembly-Line Helper / Production Helper (Ford system era)
Known then as Production Helper / Production Laborer (post-war deskilling)
Known then as Helpers--Production Workers (BLS SOC 51-9198)
Known today as Helpers--Production Workers (BLS SOC 51-9198)
Projected to still be called Helpers--Production Workers (BLS SOC 51-9198)
Data from the May 2025 survey
US Employment
166K

Stated in source: BLS OEWS

Official statistic
Report an error
Median Annual Wage
$39,070
Est.

≈ $38,068 in 2024 prices (derived by us, CPI-adjusted)

Official statistic
Report an error
Beat · 1814

The Boston Manufacturing Company opens its integrated cotton mill at Waltham, Massachusetts, gathering spinning and weaving under one roof and powering them from a single water source. The integrated factory creates a permanent class of unskilled hands whose job is to keep the machines fed and cleared: the structural origin of the production helper.

Beat · 1913

Henry Ford introduces the moving assembly line at Highland Park. Its power is not the belt itself, which existed before, but the system: interchangeable parts plus powered conveyor plus deskilled, low-wage labor synchronized to machine rhythm. The line creates millions of production-helper jobs and fixes the modal manufacturing-worker profile as low-skill, high-volume, and interchangeable.

Beat · 1961

Unimate, the first industrial robot, goes to work on a General Motors die-casting line in Ewing Township, New Jersey, lifting and handling hot metal parts. It is the first machine to do a production helper's lifting-and-placing work without a human, foreshadowing the automation that would reshape the role over the next sixty years.

Beat · 1979

US manufacturing employment peaks at roughly 19.5 million workers. From here it enters a long structural decline, pushed by recessions, automation, and offshoring. Production-helper employment, tied directly to manufacturing headcount, begins its long descent and never returns to its postwar relationship with the broader workforce.

Beat · 1994

NAFTA takes effect, accelerating a shift of labor-intensive production to Mexico and, over the following decade, to China after its 2001 WTO accession. Production-helper employment, concentrated in exactly the low-value-added manufacturing most exposed to offshoring, enters visible secular decline.

Beat · 2009

The Great Recession drives the sharpest manufacturing contraction since the Second World War; output and employment fall hard and many labor-intensive plants close permanently. Production-helper employment drops steeply and does not recover to its pre-2008 level, leaving survivors concentrated in higher-value, more-automated plants.

Beat · 2015

Collaborative robots reach meaningful commercial scale, with affordable cage-free arms (Universal Robots and others) deployed in automotive Tier-1 suppliers and mid-size shops for machine tending and palletizing. For the first time, automation of the helper's core tasks is cheap enough for the small manufacturers the 1960s-90s automation wave had skipped, opening an era of broad-based labor replacement.

How to read this number

OEWS is a point-in-time survey snapshot, not a continuous time series; BLS advises against using it for year-over-year trend comparison.

Each dot is a cited figure over time; the dotted line only links them (values between aren't measured). Hollow dots are estimates.

Official statistic: Copied by a script from a statistics agency table or a published study's table, not retyped by hand. BLS figures and the forecast and exposure tables are also re-checked against that table on every data run. Researched context: Researched from the cited source rather than copied from it by a script, so it is not machine-checked yet. Spot a mistake? Each figure has a “Report an error” link.

Cited forecasts · present → 2035

Will AI replace production helpers? What the research says

Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, we show the disagreement rather than an average.

Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, the spread is the honest signal.

What published forecasts say
Forecast change in the number of people doing this work, as published by each named source. Bars share one scale on every role, centred on zero and reaching the end at ±30%. Grey marks a small change, 3% or less either way.
BLS Employment Projections 2025-35
→ 2035
-8.2%
BLS National Employment Matrix, 2025-35 cycle, line for SOC 51-9198 (Helpers--production workers): 167.4 thousand jobs in 2025, projected 153.6 thousand in 2035, a change of -13.8 thousand (-8.2%). Matrix employment counts all jobs in the occupation, including self-employed workers (1.7% of this occupation in 2025). The average for all occupations in this cycle is +3.5%.
Official statisticReport an error
How exposed is this work to AI (research measures)
Scores as published by each study. Each row says what it measures. These measure what AI could technically do, not forecasts of jobs lost.
Frey & Osborne (2013), "The Future of Employment"
2013 study · Probability of computerisation (2013 model)
66%
probability
What this measures

Published appendix value for SOC 2010 51-9198 Helpers–Production Workers: probability of computerisation 0.66, rank 365 of 702 (rank 1 is least computerisable). Frey and Osborne estimated in 2013, with a Gaussian-process classifier on O*NET variables, how likely the whole occupation is to be technically automatable over "some unspecified number of years, perhaps a decade or two". It is a probability for the occupation, not a share of tasks and not a forecast of jobs lost.

Official statisticReport an error
Eloundou et al. (2023), "GPTs are GPTs"
2023 study · Share of tasks exposed to LLMs (β, human-rated)
5.6%
of tasks
What this measures

Published occupation-level exposure from the paper's occ_level.csv for O*NET-SOC 51-9198.00 Helpers--Production Workers. Shown: human-rated β = 0.056, the share of the occupation's tasks an LLM could do in at least half the time at equal quality, counting tasks that need LLM-powered software at half weight (β = E1 + 0.5 × E2). Other published measures for this occupation: human-rated α (LLM alone) 0 and γ (all LLM-assisted) 0.111; GPT-4-rated β 0.089. This measures how much of the work an LLM could touch, not how many jobs disappear.

Official statisticReport an error
Today, in this role

What's changing day to day for production helpers

Three parts of your work where AI is already doing real lifting, and what stays yours.

AI is taking this onStack and palletize finished goods for shipment, aligning stacking patterns to weight limits and transport stability requirements.

Stack and palletize finished goods for shipment, aligning stacking patterns to weight limits and transport stability requirements.[6],[7]

Tools picking this up
Where your edge is

Shift to supervising palletizing cells: learn to configure stacking patterns in robot teach pendants and troubleshoot gripper jams so you can maintain throughput across product changeovers.

AI is taking this onLoad raw materials into production machinery and unload finished or semi-finished parts, coordinating with machine cycle times while following lockout/tagout safety protocols.

Load raw materials into production machinery and unload finished or semi-finished parts, coordinating with machine cycle times while following lockout/tagout safety protocols.[1],[6]

Where your edge is

Learn to program and monitor cobot loading cells: certify in cobot setup (Universal Robots UR Academy or equivalent) so you become the person who deploys the robot rather than the one it replaces.

AI is taking this onTransfer materials, parts, and tools between storage areas and workstations using hand trucks, pallet jacks, and powered lift trucks, often guided by paper picklists or verbal instruction.

Transfer materials, parts, and tools between storage areas and workstations using hand trucks, pallet jacks, and powered lift trucks, often guided by paper picklists or verbal instruction.[1],[8]

Where your edge is

Qualify as a fleet operator for AMR systems: learn to set up routes, handle exceptions, and perform first-line maintenance so material flow stays moving when the robots encounter edge cases.

Where production helpers go next

Natural next steps for someone with your foundation: not exits, evolutions.

What each move takes

A direction you could grow

Industrial Machinery Mechanics

Industrial Machinery Mechanics maintain and repair the same production equipment production helpers operate daily; helpers who learn mechanical fundamentals and earn a maintenance apprenticeship move into a role with stronger wage growth and lower automation risk.

What you'd add
  • · Hydraulics, pneumatics, and mechanical drive systems fundamentals
  • · Electrical safety (NFPA 70E) and basic PLC troubleshooting
  • · Predictive maintenance concepts and vibration analysis
  • · Apprenticeship program enrollment (NIMS or employer-sponsored)
What it takesA real upskill, but a natural one
Tools of the era

How has the work of production helpers changed since 1815?

The tools that defined the work, one line per era. Select an era to see how it reshaped the work.

  • Muscle, hand tools, and water power (early factory era)

    In the first factory century the helper's tools were the body, simple hand tools (shovels, buckets, hand trucks, pry bars), and occasional animal power. The machines, driven by water wheels and later steam, did the transforming; humans did all the carrying, feeding, and clearing. Raw material was moved by hand or barrow, finished goods were stacked by hand, and the helper's value was steady physical labor at a pace the machine set. Nothing mechanical assisted the material handling itself.

  • Powered conveyor and the moving assembly line (Ford, Highland Park, 1913)

    Henry Ford's moving assembly line, run at Highland Park, Michigan from 1913, brought the machine to the helper instead of the helper to the machine. Work moved past a worker fixed at one station, performing one small motion in time with the belt. This standardized the helper's work cycle to a fixed rhythm and stripped it of discretion: the same task, the same duration, repeated identically across the shift. It was deskilling in its purest form, and it made the assembly-line helper the modal American factory worker. Conveyor technology did not shrink the role; it multiplied it, because faster lines needed more hands feeding and clearing them.

    Effect on the work

    Ford's line cut the time to assemble a Model T chassis from over twelve hours to about ninety minutes and let the company drop the price while raising output massively; rather than reduce headcount, the productivity gain and the resulting demand drove a large expansion of low-skill line labor through the 1910s and 1920s.

  • Forklift and powered lift trucks (postwar material handling)

    After the Second World War, the forklift became the dominant material-handling tool, much of it built on lift-truck designs refined for wartime logistics. Instead of carrying loads of fifty to a hundred pounds by hand across the floor, a helper could move a pallet of a thousand pounds or more with a powered truck. The forklift did not eliminate the helper; it shifted the work from raw strength toward operator coordination, and it created new helper tasks: building pallets for pickup, breaking them down for storage, and clearing lift platforms. It reduced the number of people needed to move a given tonnage even as rising postwar production volume kept aggregate helper employment growing.

    Effect on the work

    Powered lift trucks cut the labor-hours required to move a given tonnage of material sharply compared with hand carrying, but the postwar manufacturing boom expanded total output fast enough that aggregate production-helper employment kept climbing through the 1950s and 1960s.

  • Automated storage/retrieval systems (AS/RS) and the first industrial robots

    The 1960s through the 1980s brought the first technologies that automated helper tasks outright rather than just easing them. Automated storage and retrieval systems used computer-controlled cranes to move and stack pallets without human hands. Industrial robots, beginning with Unimate on a General Motors line in 1961, took over repetitive load, weld, and place operations, though they were large, fast, dangerous, fenced behind safety cages, and programmable only by specialists. Conveyor routing grew sensor-driven and self-sorting. The helper's role narrowed toward exception handling, troubleshooting, and cleanup, and in the plants that could afford this capital, production-helper headcount per unit of output began to fall. Crucially, only large manufacturers could afford it, which split the manufacturing economy into automated plants with few helpers and labor-intensive shops with many.

    Effect on the work

    AS/RS and caged industrial robots reduced helper labor per unit of output substantially in the automotive, electronics, and appliance plants that deployed them, but adoption was concentrated among large capital-rich manufacturers; smaller job shops and contract manufacturers, unable to justify the cost, retained large helper workforces, producing a bifurcation that still shapes the occupation.

  • ERP, barcode scanning, and shop-floor data systems (SAP, RFID)

    Enterprise resource planning systems and shop-floor data collection turned the helper into a node in an information system. Paper picklists gave way to handheld barcode scanners that verified where each part was and where it was going; RFID tags let warehouse-management software track pallets in real time; the work to be done next was determined by a central system rather than by a foreman's voice. This did not replace the helper, but it changed the job: the helper now confirmed every material move into the digital record, which raised throughput per worker and also raised the cost of a single mistake, since a misscanned part could stall production downstream.

  • Collaborative robots (cobots) and autonomous mobile robots (AMRs)

    Around 2010, with Universal Robots shipping its first cage-free collaborative arm in 2008 and the market scaling through the 2010s, automation finally reached the price point of the small shop. Cobots work directly alongside people without safety cages, and their teach pendants are simple enough for a floor worker to program, so they took over precisely the helper's machine-tending, pick-and-place, and palletizing tasks. Autonomous mobile robots, navigating factory floors without fixed tracks, did the same for material transport that the forklift once eased, but without needing a trained driver. Where the AS/RS era of the 1960s to 1990s only touched large manufacturers, cobots and AMRs are cheap enough for the whole ecosystem, which is why this is the first tool era that displaces the helper rather than reorganizing the work around them.

    Effect on the work

    A single cobot machine-tending cell can cover the loading and unloading work of one to two helpers per shift, and a small AMR fleet can absorb the transport work of a much larger group of material movers, with payback periods reported in the low single-digit years; because the technology is affordable to small and mid-size shops as well as large ones, the displacement reaches across the entire manufacturing base at once.

  • AI machine vision and predictive maintenance (sensor-based condition monitoring)

    Alongside the physical robots, AI vision systems built on neural networks began taking over the helper's inspection task from 2023, scanning every part coming off the line at high speed and flagging scratches, dimensional drift, and missing components far faster than a human eye can. Predictive-maintenance systems using vibration, temperature, and acoustic sensors began to formalize the helper's informal role as the person who notices when a machine is about to fail, turning that tacit judgment into a sensor reading and a model alert. As of 2026 the highest-capital manufacturers have deployed these systems and adoption is spreading into the mid-market; for most shops the tools still augment helpers rather than replace them outright, but they steadily erode the inspection and monitoring work that gave the role some shelter from pure material-handling automation.

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Helpers--Production Workers employment and pay by year

On record since1815
Latest US employment165,700 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Latest US median pay$39,070 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Outlook-8.2% by 2035Source: BLS Employment Projections 2025-35Official statisticReport an error
View all 28 years of cited US data, each linked to its source
YearUS employmentAnnual pay (median unless marked)SourceReport
1870190,000n/aDecennial Census Researched contextReport
1910620,000n/aDecennial Census Researched contextReport
19501,350,000$2,700Decennial Census (employment) Researched contextEst., University of Missouri Libraries (pay) Researched contextReport
1975n/a$8,300Est., University of Missouri Libraries Researched contextReport
2000533,720$18,000BLS OEWS 2000 Official statisticReport
2003452,700$19,590BLS OEWS May 2003 Official statisticReport
2004480,430$20,180BLS OEWS May 2004 Official statisticReport
2005528,610$20,390BLS OEWS May 2005 Official statisticReport
2006539,350$20,740BLS OEWS May 2006 Official statisticReport
2007524,440$21,090BLS OEWS May 2007 Official statisticReport
2008499,870$21,790BLS OEWS May 2008 Official statisticReport
2009433,370$22,370BLS OEWS May 2009 Official statisticReport
2010394,270$22,450BLS OEWS May 2010 Official statisticReport
2011420,910$22,520BLS OEWS May 2011 Official statisticReport
2012419,840$22,810BLS OEWS May 2012 Official statisticReport
2013426,670$23,160BLS OEWS May 2013 Official statisticReport
2014420,520$23,610BLS OEWS May 2014 Official statisticReport
2015439,000$23,960BLS OEWS May 2015 Official statisticReport
2016429,890$24,830BLS OEWS May 2016 Official statisticReport
2017402,140$26,070BLS OEWS May 2017 Official statisticReport
2018350,410$27,730BLS OEWS May 2018 Official statisticReport
2019303,030$29,100BLS OEWS May 2019 Official statisticReport
2020239,340$30,500BLS OEWS May 2020 Official statisticReport
2021202,860$30,000BLS OEWS May 2021 Official statisticReport
2022190,680$34,670BLS OEWS May 2022 Official statisticReport
2023181,810$36,700BLS OEWS May 2023 Official statisticReport
2024167,490$38,220BLS OEWS May 2024 Official statisticReport
2025165,700$39,070BLS OEWS May 2025 Official statisticReport
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