The short answer
Cobot ROI for medical device manufacturers comes mainly from hours of machine tending and packing that a robot takes over from people, not from cutting headcount. The savings range depends on how much of each shift is repetitive loading and packing, the lease cost, and how often parts and fixtures change. A free site assessment turns those inputs into a precise figure for your plant.
Key takeaways
- Most of the return comes from repetitive loading and packing hours that you measure on your own floor.
- The labor side is priced on the government wage data for laborers and material movers, not on your payroll.
- Changeovers, inspection steps and uptime decide whether the savings range lands high or low.
- Published cases come from vendors and integrators, so treat their payback figures as a starting point only.
- A lease quote comes after a free site assessment, with service and training scoped in the same plan.
Run the numbers for your site
Pick your city or state for a real regional number - we use the real BLS wage there. (On the live site this auto-detects from your location.)
What do you want to automate?
The cobot stacks the repetitive end-of-line pallets; your team handles changeover and QA.
What is cobot ROI for medical device manufacturers?
Cobot ROI for medical device manufacturers is the return a collaborative robot arm produces when it takes over repetitive work on the line, set against the lease cost and the labor it offsets. In this setting the work is usually machine tending, where a robot loads and unloads a machine, and packing, where finished parts go into trays, bags or cartons. Both tasks are light and repetitive, and a cobot can work beside operators without fencing, which keeps the people who already run the cell in the loop.
The return is seldom a headcount cut. Medical device shops run to tight quality standards, and the people on the line also inspect parts, handle changeovers, catch unusual defects and keep records. The useful question is how many hours per shift of tending and packing the robot can take, and whether the people who remain can spend that time on work that needs judgment.
Expect the answer to be a range, not a single figure. The range rests on cycle times, part mix, inspection requirements and how often the cell stops. Those are the facts a site assessment collects, and they matter more than any generic payback claim.
How many medical device manufacturers are there in the US, and how big are they?
The count depends on the source and the year. According to a third-party NAICS 3391 industry profile (year not stated, not checked against Census), medical equipment and supplies manufacturing has 9,009 establishments and about 304,707 employees across 7,997 firms. Trade.gov cites approximately 8,500 medical device firms in the United States, on a page that is undated, so the two figures should be read as rough orders of magnitude.
Size matters more than count for a cobot decision. According to the AdvaMed 2025 annual report, 94 percent of medtech companies are startups and 82 percent have fewer than 20 employees. AdvaMed says its members represent 80 percent of U.S. medical technology firms, so the association's view is broad but not a complete census of the market. In a small shop, one tending cell or one packing station can cover most of the repetitive work, which is why a single unit is often the practical first step.
No published count of the people who tend or pack at one medical device site was found for this page. The typical headcount used in our planning model is four people on this work, which is an estimate to confirm during the site assessment, not a sourced statistic.
Median hourly wage, Laborers and Freight, Stock, and Material Movers, Hand
$4,380
Monthly cost of one full-time worker, at the median wage plus payroll costs (an estimate, not a quote).
Where does the labor side of the return come from?
The labor side is priced on the hours of laborers and freight, stock, and material movers, hand type of work the robot takes over, not on what any named person earns. According to the Bureau of Labor Statistics Occupational Outlook Handbook, about 2,988,900 people held jobs in that group in 2024. The government wage data on this page shows the wage for that occupation nationally, and the savings range applies it to the hours the robot takes.
Tending and packing hours have to be measured, not assumed. Time how long a person spends loading parts, unloading finished pieces and packing each unit, then multiply by cycles per shift and shifts per week. Only the share of that time a robot can realistically take counts toward the return, and that share comes from the process you run.
Keep the labor hours separate from the people who stay on the line. If a worker moves from loading to inspection, that is a redeployment, not a saving, and the model should show it that way. Only hours that come off the schedule, or work that would otherwise need another hire, should count as offset.
How does the savings math work?
The math has four inputs: weekly hours of tending and packing, the share of those hours a cobot can realistically take, the wage for the labor occupation on this page, and the lease cost. Hours offset equals weekly hours multiplied by the realistic share. Labor offset equals those hours multiplied by the wage with a standard allowance for benefits and payroll costs, and the net return is that offset minus the lease payment and any service or training cost in the plan.
The realistic share is where most estimates go wrong. A cobot can take the repetitive load and most pack cycles, but changeovers, fixture swaps, first-article checks and defect handling usually stay with people. We model the share from the process you actually run, then show the savings as a low to high range, because one optimistic assumption can move the whole result.
Our team presents the lease as a fixed robot lease payment after the site assessment. Lease financing programs spread the cost over the term, so the net return is judged against a known schedule of payments rather than a guess. The payment amount itself appears only in the quote, not on this page.
What changes the number up or down?
Cycle time, part mix and uptime do most of the work. Long machine cycles with long runs of one part favor a cobot, while frequent changeovers, many part numbers and tight tolerance checks shrink its share. Uptime matters because a stopped robot offsets no hours, so service response belongs in the model from the start.
Published cases show the range without guaranteeing your result. An integrator application note for a machine tending job at a medical device maker estimated payback in roughly one year, counting only burdened labor cost. That estimate leaves out lease terms and changeover losses, so add those and test it against your own floor before relying on it.
Use the checklist below before you accept any savings range. It flags the inputs that most often move the number, and each one can be checked during the site assessment.
What about injuries, quality and safety on the line?
Injury data gives context for why automating repetitive handling is a safety argument as well as a cost argument. The Bureau of Labor Statistics reported 2.5 million nonfatal injuries and illnesses in private industry in 2024, down 3.1 percent from 2023, according to a trade news summary of that data. No injury rate specific to medical device manufacturing was found for this page, so do not treat the national figure as your plant's rate.
A collaborative robot still needs a risk assessment before it runs beside people. The benefit is that the operator can stay at an inspection station or a changeover point instead of repeating the same reach all shift. Quality standards do not relax because a robot is present, so the cell should be validated, its settings recorded, and inspection kept with the people who own quality.
Ask the integrator how the cell handles faults, sensor stops and manual recovery. Those answers matter for both safety and uptime, and they should be written into the plan before go-live.
What are the risks to the return, and how do I avoid them?
Adoption is growing, which lowers the risk of being early. According to A3, the 2025 robot orders report, 36,766 robot units were ordered in North America in 2025, up 6.6 percent in units and 10.1 percent in revenue over 2024. Collaborative robots were 7,212 units, or 19.6 percent of units ordered for the full year, and reached 28.6 percent of units in Q4 2025.
A market research press release reports that 13 percent of surveyed manufacturers had collaborative systems in operation, and another 15 percent expected them within 12 months. Those figures describe a market still early in adoption, so the bigger risk is picking the wrong cell, not missing a trend. Avoid that by starting with the process that has the most stable cycle, and by writing your own hour counts into the plan before you commit.
Other common risks are overestimating the share of work the robot takes, underestimating changeover time, and treating vendor payback claims as guaranteed. Each one shows up in the comparison below, where the person still wins on judgment and flexibility.
- Repeatable cycle timing
Cobot
Runs the same motion on every cycle without drift
Staff
Steady, but pace slips over a long shift
- Shift coverage
Cobot
Runs the cycles it is set up for, including unattended runs where safe and permitted
Staff
Covers breaks, handoffs and unplanned work
- Inspection and judgment
Cobot
Checks only what it is programmed to check
Staff
Catches unexpected defects, odd parts and visual problems
- Changeovers
Cobot
Needs a programmed or fixture change and adds stop time
Staff
Handles most changeovers quickly and with judgment
- Flexibility
Cobot
Stays on the task it is set up for
Staff
Moves across tasks and learns new parts
What is a realistic timeline from assessment to payback?
A realistic path starts with a free site assessment, which maps the cell, times the cycles and counts the hours. The next steps are a pilot on one cell, integration with the existing line, staff training and go-live support. The quote comes before any installation, so the cost is known before the cell is built.
Payback is measured from steady output, not from the first week. Early runs tend to include tuning, operator learning and program adjustments, so the first full months of running give the first honest read. Track the actual hours offset against the model, and revisit the share assumption if the process turns out other than planned.
The integrator estimate of roughly one year, counted on burdened labor cost alone, is a useful reference point for a single cell and not a promise. Lease terms, changeovers and uptime all push the real figure up or down.
How do I get a precise figure from a free site assessment?
A free site assessment turns the estimate into a precise figure. We visit the floor, map the work cell, time the machine cycles and packing steps, and record the changeovers and inspection points that limit the robot's share. From that we pick the right robot for the site, a vendor neutral choice, and produce the savings range and a fixed robot lease payment quote.
Bring recent shift schedules, cycle times, the part list and the inspection steps. Those four inputs let the assessment produce a range you can check against your own labor records. Ask for the assumptions in writing, including the share of hours the robot takes, so each one can be tested before you decide.
The service side is part of the same plan. Lease options can include maintenance, on-site dispatch and remote triage, plus staff training and go-live support, and the exact terms are confirmed in the quote. We deploy and service nationwide, so the plan works for a single cell or a phased rollout across several lines.
Frequently asked questions
Want a cobot at your medical device manufacturer?
Tell us about your site. We will recommend the robot, quote the lease, and keep it serviced.
Sources
- US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (OEWS May 2024)
- NAICS 3391 industry profile (third-party aggregator)
- Trade.gov medical device market overview
- AdvaMed 2025 annual report
- Bureau of Labor Statistics Occupational Outlook Handbook, hand laborers and material movers
- BLS 2024 injury data summary (trade news)
- A3 2025 robot orders report
- Market research press release on collaborative robot adoption
- Integrator application note on machine tending for a medical device maker