Issue or distribute materials, products, parts, and supplies to customers or coworkers, based on information from incoming requisitions.[2]
Where your edge is
Time Machine
Stockers and Order Fillers
Scrub through 177 years of this role's history — from when it first emerged, through every wave of technology that reshaped it, to the cited projections for where it's heading next.
187519001925195019752000now
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2026
Known today as Stockers and Order Fillers (BLS SOC 53-7065)
US Employment
2.76M
BLS National Employment Matrix baseline employment for SOC 53-7065, 2024. Used as the authoritative baseline for the 2024-2034 projection cycle. The BLS projections matrix (accessed May 2026) gives 2,764.8 thousand. The BLS places 65.8% of this occupation in general merchandise stores and food and beverage stores (retail trade) in 2024. This represents the occupation at or near its historical high-water mark, more than a decade into Amazon's Kiva-robot deployment (750,000+ robots in Amazon FCs by 2024) and several years past Walmart's Bossa Nova shelf-scanner pilot (terminated 2020). Headcount grew, not shrank, because e-commerce volume scaled faster than per-worker productivity from automation.
Median Annual Wage
$33,270
Source: BLS-OEWS
Tool of the era · AI-vision piece-picking + Symbotic at scale + BLS +8.5% projection to 2034
By 2023, the warehouse robotics industry had moved from pod-transport (Amazon Robotics AMRs) and shelf-scanning (Bossa Nova, terminated) to active piece-picking: systems like Amazon Sparrow, Mujin, Berkshire Grey, AutoStore, and Covariant's AI-vision picking arms can grasp and process an increasing share of the SKU universe. The dexterity challenge — picking irregularly shaped, soft, extremely light, or fragile items at acceptable speed and error rates — remains unsolved for approximately 15-20% of e-commerce SKUs as of 2026. The retail floor environment (live customers, inconsistent lighting, product displacement by shoppers) is significantly harder than a controlled warehouse for robotics. BLS projects +8.5% growth for SOC 53-7065 over 2024-2034 (235,000 net new jobs, reaching 2,999.8 thousand), reflecting the continued dominance of e-commerce demand growth over automation productivity gains in the near term. The retail dimension of the occupation (Walmart ~350k stockers, Target ~125k, grocery chains broadly) is evolving differently from the fulfillment-center dimension. Retail floor stocking is being partially transformed by electronic shelf labels (ESLs), which automate price and promotions display but do not eliminate the physical task of moving product from backroom to shelf. Micro-fulfillment centers embedded inside retail stores (common at Kroger-Ocado and some Walmart formats) blur the boundary between the retail stocker and the warehouse order filler, increasingly requiring workers to do both functions.
BLS projects net employment growth of 8.5% over 2024-2034, driven by continued e-commerce expansion. Annual job openings are projected to be substantial — the occupation has high turnover from physically demanding conditions and part-time scheduling in retail. The projected net new jobs (235,000) exceed the BLS all-occupation average growth rate for this projection cycle.
Projection cone · present → 2034
What credible sources project
Scrub the slider past now to anchor each scenario on the scrubber. The spread you see below is the range of futures credible sources project for this role.
E-commerce continued-growth optimistic scenario
→ 2034
+15%
Optimistic tail of the uncertainty cone. US e-commerce reached 16.9% of total retail in Q1 2026, growing at ~10% year-over-year. If that pace continues — driven by further penetration of grocery, home improvement, auto parts, and other categories that have been slower to shift online — total e-commerce volume could reach $2.3-2.5 trillion by 2034 from a ~$1.1T (2024) base. At current fulfillment-center labor intensity, even accounting for continued robotics improvement, this volume trajectory generates employment growth in SOC 53-7065 of 12-18% above 2024 levels. BLS's +8.5% is a moderate-case scenario; this +15% represents the upper tail if e-commerce share captures grocery and other categories at the pace the omnichannel retail buildout (Walmart Neighborhood Markets, Kroger-Ocado CFCs, Target same-day) suggests.
BLS National Employment Matrix 2024-34
→ 2034
+8.5%
BLS Employment Projections 2024-34 cycle (most current). Baseline 2,764.8 thousand (2024); projected 2,999.8 thousand (2034); net change +235.0 thousand (+8.5%). This projects the occupation past 3 million workers by 2034 — more than any pre-Amazon-Kiva analyst forecast, and achieved despite 750,000+ warehouse robots already deployed. BLS projects growth as e-commerce volume continues expanding (US e-commerce reached approximately 16.9% of total retail in Q1 2026, growing ~10% year-over-year), adding demand for fulfillment-center order fillers faster than automation displaces them. Retail floor stocking continues to grow with overall store-count expansion. Annual job openings are substantially higher than net change because the occupation has high turnover from physically demanding conditions and part-time scheduling.
Eloundou et al. — "GPTs are GPTs" (2023)
→ 2028
-2%
GPT-4 task-by-task LLM exposure labeling on O*NET tasks. SOC 53-7065 scores very low on LLM exposure because the core tasks — moving boxes, rotating stock, scanning items, reading packing lists, identifying out-of-stocks — are physical and spatial tasks that a language model cannot perform. The -2% estimate is a conservative floor representing displacement from AI-assisted planning tools (AI-directed pick optimization, automated schedule generation, demand forecasting tools that reduce over-ordering and thus reduce some receiving/stocking labor) rather than from physical robot substitution. The actual automation risk for this occupation is physical robotics, not LLMs — a distinction that Eloundou's methodology correctly captures by assigning low LLM exposure, while Frey & Osborne's robotics-aware framework assigned high computerization risk.
McKinsey Global Institute — automation scenario (2017, physical activities)
→ 2030
-25%
McKinsey's 2017 "A Future That Works" study estimated that physical activities in predictable environments — a category that covers controlled warehouse picking — had approximately 78% technical automation potential, among the highest in their task-category taxonomy. Under their "rapid automation" scenario applied to this occupation's 2024 baseline, a -25% displacement by 2030 is within the modeled range. The pessimistic scenario is more aggressive than BLS projects for two reasons: McKinsey models technical potential, not observed deployment rates; and the retail floor environment (less predictable than a warehouse) reduces achievable automation penetration relative to the technical maximum. A -25% scenario would require that AI dexterity improvements accelerate well beyond current trajectory over the next four years — possible given the pace of robot arm AI improvement but not the consensus view.
Frey & Osborne (2013)
→ 2033
-50%
Gaussian-process classifier on O*NET task features. Frey & Osborne placed stock clerks and order fillers in the high-risk quartile with a probability of computerization of approximately 0.72, noting that the primary bottleneck factors defending the role were minimal — the tasks were largely routine, the environment predictable (especially in controlled warehouse settings), and manual dexterity requirements were not extreme. At 0.72 probability over 10-20 years, a -50% employment scenario (as implied by broad realization of the probability) would bring employment from ~2.3M (2012 estimate) to ~1.15M by 2033. Actual 2024 employment is 2.76M — substantially above even the 2012 baseline. The F&O prediction is among the most dramatically falsified in the dataset for this occupation, for the same reason it was falsified for SOC 53-7062: demand (e-commerce volume) grew faster than productivity gains from automation. The robots changed the job without eliminating the worker.
Today, in this role
What's shifting in the work right now
The historical view above shows how this role has moved. This is the present-day detail: which AI tools are picking up which tasks, where the edge still is, and the natural directions this work can grow.
What's changing in your day
Three parts of your work where AI is already doing real lifting — and what stays yours.
Store items in an orderly and accessible manner in warehouses, tool rooms, supply rooms, or other areas.[2]
Where your edge is
Recommend disposal of excess, defective, or obsolete stock.[2]
Where your edge is
Present-day sources
Sources
Every claim on this page traces back to one of the following. Updated 2026-05-30.
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