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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteAutomation can help manufacturers manage labor and cost pressures when it addresses a defined, repetitive bottleneck—not simply because a new machine is available. Machine tending, parts handling, and visual inspection can increase capacity or consistency, reduce exposure to repetitive tasks, and free employees for other work. Results depend on the process, integration costs, product mix, maintenance, and what the business does with the capacity it gains.
How can automation help manufacturers manage labor and cost pressures?
Automation can shift repeatable work from people to equipment, or help people complete it more safely and consistently. The practical value is not limited to reducing labor hours. A well-chosen project may relieve a production constraint, keep a machine productive for longer, improve inspection consistency, reduce ergonomic strain, or give workers time for tasks that require judgment and flexibility.
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NIST’s Manufacturing Extension Partnership (MEP) identifies machine tending, autonomous mobile robots for material handling, machine vision for visual inspection, and collaborative robots working alongside people as relevant applications. These are different ways to solve different problems: a robot arm loading a machine does not address the same constraint as a vision system detecting defects or a mobile robot moving material between work areas.
Automation is not a guaranteed cost reduction. Equipment can add capital, integration, programming, maintenance, training, and downtime costs. A system may also be a poor fit if products or processes change frequently, the task is not sufficiently repeatable, or the operation lacks the upstream and downstream capacity to use extra output. The first question should be what measurable problem the project is meant to solve.
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How do manufacturers use automation to address labor shortages?
Manufacturers can automate a task that is difficult to staff, repetitive, physically demanding, or holding back equipment utilization. The purpose may be to keep production moving with a smaller available workforce, rather than to eliminate positions. Employees may be reassigned to other production needs, trained to operate or maintain an automated cell, or moved to work that cannot be automated as readily.
Two NIST MEP case studies illustrate possible workforce outcomes, but they are individual company experiences, not typical results or controlled comparisons.
| Manufacturer and task | Reported result | What the case does and does not establish |
|---|---|---|
| AMG Industries: a collaborative robot handled a repetitive exhaust-tip production task. | NIST MEP reports an average of 276 parts per hour with the robot versus 200 manually, a 38% increase. Workers were redeployed to other needed areas. After a three-month trial, AMG bought a UR10e for the role. The company’s reported payback was six and a half months, compared with an initial forecast of 11.5 months. | These are outcomes reported for AMG’s task and implementation. The case does not establish a typical productivity gain or payback for other plants. |
| Area 419: automated parts handling on a fifth-axis machine. | The company reported that it kept a single staffed shift while the machine ran during the remaining 16 hours of the day. Within eight months, it bought another machine with the same automation and hired an operator to manage that cell. | This is a company-specific account of staffing and machine use, not proof that every automated cell can run unattended or that the same schedule is safe or suitable elsewhere. |
AMG’s engineering director, Jes Salmons, said the MEP robot loaner program let the company try the technology before making the upfront investment. NIST MEP reports that the loaner program saved AMG $15,000. That arrangement shows how a trial can reduce uncertainty in one case; it does not establish that the same program is available to every manufacturer.
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Area 419’s operations vice president, Justin Watson, described the automation as a way to keep one shift while allowing the machine to run for the rest of the day. NIST MEP’s case page lists $750,000 in cost savings, $500,000 in increased or retained sales, $2,530,000 in new investment, and two increased or retained jobs. Those figures belong to that case and should not be treated as typical savings, job effects, or an independently measured causal estimate.
What kinds of manufacturing work are plausible automation candidates?
Start with the work, not the machine category. A candidate task is more promising when it is repetitive and stable, its performance can be measured, and solving it addresses a real production or safety need. NIST MEP recommends assessing operations, prioritizing opportunities, building a business case aligned with company strategy, connecting with vendors or integrators, and measuring outcomes.
| Application | Potential problem to investigate | Key fit question |
|---|---|---|
| Machine tending | Workers repeatedly load, unload, or transfer parts at a machine, or equipment waits between cycles. | Are part presentation, machine interfaces, and cycle timing consistent enough for reliable loading and unloading? |
| Automated parts handling | Material movement or manual loading limits machine use, creates ergonomic concerns, or ties workers to repetitive handling. | Can the cell safely manage the actual range of parts, fixtures, and process interruptions? |
| Machine-vision inspection | Visual checks are repetitive, difficult to perform consistently, or a potential source of missed defects. | Can the system detect the relevant defects under real production conditions, and how will exceptions be handled? |
| Autonomous mobile robots | Moving materials between locations consumes time or competes with other production duties. | Are routes, traffic, handoffs, and material presentation sufficiently predictable for the proposed movement? |
| Collaborative robots | A worker and robot could divide a repetitive task, with the person handling variable or judgment-based work. | Has the complete work cell been assessed for safe interaction, task allocation, and process changes? |
These questions are practical screening prompts, not a substitute for engineering and safety assessment. A task can be repetitive and still be a poor candidate if the workpiece varies too much, the machine is unreliable, changeovers dominate the schedule, or an automated system would create a new bottleneck elsewhere.
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How should a plant evaluate an automation project?
Compare candidate projects against the same business and operating criteria. The following checklist builds on NIST MEP’s assessment, business-case, integration, and measurement guidance; it is a decision aid, not a published NIST scoring system.
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- Repetition and stability: How often does the task occur, and how much does the work vary by part, operator, or shift?
- Constraint to be relieved: Is the issue labor availability, machine waiting, material movement, quality variation, ergonomic exposure, or another defined problem?
- Output and quality measures: Can you track throughput, downtime, yield, defects, or another outcome that reflects the problem?
- Worker impact: What work will employees do after the change? What training, redeployment, or new cell-operating responsibilities will be needed?
- Full cost: Include equipment, integration, programming, safeguards, training, maintenance, and operating costs—not just the purchase price.
- Product mix and change: How often do products, tooling, fixtures, or process steps change, and what would those changes require of the system?
- Measured payback: What baseline will be used, what benefits count, and how long will it take for those measured benefits to cover the project’s costs?
Use a process walk to observe the actual task across relevant shifts and product types. Record where time is spent, when equipment waits, what causes interruptions, and how often workers handle exceptions. A baseline should reflect normal operating conditions rather than a particularly good or bad day; otherwise, a before-and-after comparison can exaggerate or hide the effect.
Next, identify the business goal and the measure that would show progress. For a machine-utilization problem, that could include time the machine is producing versus waiting for an operator or material. For an inspection project, it could include defect detection and rework. For a labor constraint, quantify the task’s staffing demand and decide in advance how released time would be used. More output is not automatically more value if there is no demand for it or another process cannot handle the additional production.
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Build a business case that includes the costs of making the system work in the real plant: equipment and integration, changes to the cell, training, maintenance, operating expense, and time lost during installation or ramp-up. Compare those costs with benefits the company can actually measure, such as additional saleable production, reduced scrap or rework, or capacity released for other work. Avoid counting the same benefit twice—for example, treating both the value of extra output and the labor hours associated with making that output as separate savings if they represent the same economic gain.
A simple payback calculation is project cost divided by net benefit per period. For example, if a manufacturer uses a monthly net benefit estimate, the result is an estimate of months to recover the project cost, not a guarantee. State which costs and benefits the calculation includes, whether benefits recur, and what assumptions it relies on. A forecast should be kept distinct from realized results, as the difference between AMG’s initial 11.5-month forecast and its reported six-and-a-half-month payback illustrates.
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Before committing, discuss the proposed application with qualified integrators or vendors and make sure the solution fits the machine, workpiece, safety requirements, and production process. Where feasible, a pilot or trial can test assumptions before a full purchase. Define in advance what success means and how the trial will be measured. NIST MEP says its experts can help manufacturers assess operations, prioritize opportunities, develop a strategy-aligned business case, connect with integrators and vendors, and measure results. Service availability and local capabilities should be confirmed directly.
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After installation, compare operating results with the baseline and investigate differences rather than attributing every change to automation. Record downtime, exceptions, quality, maintenance needs, staffing, and output over a period that represents the operation. If the system misses its target, determine whether the cause is the application, integration, process variation, training, or a constraint elsewhere in the line before expanding it.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What do recent U.S. productivity and labor-cost figures show?
The U.S. Bureau of Labor Statistics (BLS) reported that manufacturing labor productivity increased 2.0% in 2025, while manufacturing unit labor costs increased 2.3% and hourly compensation increased 4.3%. In BLS’s definition, labor productivity is output per hour. Unit labor costs are hourly compensation divided by labor productivity: compensation growth tends to raise them, while productivity growth tends to lower them.
Those annual figures do not show whether automation caused a change. They describe economy-wide manufacturing outcomes, not the impact of a particular technology or the share of productivity growth attributable to automation. A plant should use its own process baseline and project measures to evaluate an investment.
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Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →The same BLS release reported a different pattern for the fourth quarter of 2025: manufacturing labor productivity decreased 1.9%, and unit labor costs increased 8.3%, both at seasonally adjusted annual rates. These quarterly rates compare the quarter with the previous quarter and are annualized; they are not year-over-year changes or full-year figures.
Industry-level BLS estimates show variation within manufacturing. In 2025, unit labor costs increased in all 20 covered three-digit NAICS manufacturing industries, at an average rate of 4.5%, and in 71 of 80 covered four-digit industries. These are separate industry-level series, not the same figures as BLS’s all-manufacturing annual productivity and cost estimates. BLS has suspended industry measures for some series because of source-data changes, so industry-table figures should be read with the release notes. The all-manufacturing and industry-level numbers provide context, not evidence that automation caused the reported movements.
What can go wrong, and how can manufacturers reduce the risk?
- The project targets a symptom rather than a constraint. Observe the process first; if a machine is waiting because material is late, automating a different task may not improve throughput.
- The business case counts theoretical capacity as realized savings. Distinguish extra potential output from saleable production, reduced cost, or time actually redeployed to useful work.
- Integration and operating costs are underestimated. Include the work needed to connect the equipment to the existing process, maintain it, train people, and manage interruptions.
- Product or process changes undermine the setup. Test the expected range of parts and changeovers, not just the easiest production run.
- Employees are not prepared for the new workflow. Plan training, responsibility for exceptions, and redeployment before launch so that released time has a defined purpose.
- Safety is treated as a property of the robot alone. Assess the full work cell and the way people, machines, materials, and tasks interact.
- Results are not measured consistently. Choose baseline and post-installation measures in advance, and track quality and downtime alongside output.
Automation is most defensible as a response to a specific operational need with an explicit workforce plan and a measured business case. The NIST MEP examples show that a company may redeploy workers, extend machine operation beyond a staffed shift, or create an operator role; they do not predict which outcome another plant will achieve. Match the system to the task, account for the full cost of integration and operation, and judge the result against the plant’s own baseline.
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