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Automated makelines can reduce repetitive handling, raise sustained output, and make frequent product changes practical—but they do not produce a universal labor percentage or payback period. A modern line coordinates robots, conveyors or mobile robots, machine vision, programmable controls, safety systems, feeders, and packaging or assembly equipment. The result depends on the product, the slowest station, feeding and gripping, inspection rules, staffing model, and integration quality.
The most useful way to evaluate one is to measure the complete flow: labor hours by task, sustained throughput at the constraint, overall equipment effectiveness (OEE), rejects, recovery time, changeover effort, and the capital and support required to keep the line running.
What an automated makeline is—and what it is not
An automated makeline is an integrated production or packaging line in which software-controlled equipment moves, handles, inspects, assembles, packs, or palletizes products with limited manual intervention. Typical elements include:
- Robots such as delta, parallel, articulated, or palletizing robots.
- Conveyors, accumulation zones, or autonomous/mobile robots that balance material flow.
- Feeders, orienters, hoppers, dispensers, and custom grippers.
- Machine vision for presence, orientation, seal, label, or package checks.
- PLC, motion, safety, and human-machine-interface controls.
- Case packers, baggers, tray sealers, palletizers, and other downstream machines.
- Recipe management and data collection for multiple products or pack formats.
It is not simply a fast robot placed beside an existing machine. If the feeder starves the robot, inspection takes longer than the pick cycle, or the case packer cannot accept product at the robot’s rate, the line will not achieve the robot’s advertised speed. Throughput is a property of the balanced system.
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Will automation reduce labor?
Usually, it reduces the number of people performing repetitive packing, inspection, transfer, and palletizing tasks. It does not eliminate every role. Operators still load materials, monitor the line, clear jams, verify recipes, and perform quality checks. Maintenance technicians, controls specialists, sanitation staff, supervisors, and changeover personnel remain important—often with higher skill requirements.
What published installations show
| Installation and date | Reported labor result | Operating context |
|---|---|---|
| Mrs. Gerry’s case study, FANUC America, 2026 | Process labor fell from up to 12 employees to two or three | Approximately 36 million pounds annually across three shifts; full capacity was reached within 10 days of installation |
| AFA Group case study, OMRON Robotics | 90% reduction in direct labor | Report also states measurable ROI within two years and less downtime during product changes |
| TePe customer reference, OMRON Europe | Labor impact not stated | Automated toothbrush-pack line focused on throughput, recipes, and mixed packs |
| Pretzel packing and palletizing customer story, ABB, 2023 | Labor impact not stated | Productivity was reported as double that of another company plant |
These are case-specific outcomes, not a staffing formula. A line that removes three packing positions may still need an operator on every shift if the equipment cannot run unattended or if frequent manual replenishment is required. Conversely, a highly integrated line may consolidate several tasks under one operator while adding scheduled maintenance coverage.
How to build a realistic staffing plan
- List every current task by shift. Separate loading, feeding, picking, inspection, packing, palletizing, material replenishment, cleaning, quality checks, and paperwork.
- Mark tasks the automation actually performs. Do not count a task as removed if a person must still intervene for orientation, jam recovery, or product verification.
- Add the new work. Include recipe selection, first-piece approval, changeover tooling, preventive maintenance, sanitation, spare-parts handling, and data review.
- Model normal and abnormal conditions. A staffing plan based only on a running line will fail when a feeder jams, a vision check rejects product, or a downstream machine stops.
- Define skills and coverage. State which roles need mechanical, electrical, controls, food-safety, or quality training and who covers vacations and breakdowns.
How much throughput can an automated line add?
Published examples span very different products and line designs, so their figures should be used as benchmarks rather than promises.
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|---|---|---|
| TePe toothbrush packs, OMRON Europe | 300 packs per minute | Reported as nine times the previous line’s throughput; recipes can be changed in seconds |
| CP Foods UK tray line, OMRON Robotics, 2025 | 120 sealed trays per minute | 85% OEE and less than 1% rejects; two Hornet parallel robots with a custom multi-stack gripper |
| Mrs. Gerry’s line, FANUC America, 2026 | Approximately 36 million pounds annually | Three-shift operation; reached full capacity within 10 days |
| Inland end-of-line system, DEVELOP, 2026 | Product arrives about every 4.2 seconds | Four SKU configurations and reported SKU changes in under four minutes |
| Pretzel packing and palletizing, ABB, 2023 | Double the productivity of another company plant | Used IRB 360 FlexPicker delta robots and an IRB 660 palletizing robot |
Nameplate speed versus sustained output
A robot’s pick rate is only one input. Sustained output is constrained by the slowest effective station after accounting for micro-stops, rejects, changeovers, replenishment, and planned downtime. Evaluate the line at its constraint, not at the fastest robot.
OEE is a useful common measure because it combines availability, performance, and quality. A line that runs at 120 trays per minute for a short demonstration is different from one that maintains that rate through a shift while delivering 85% OEE and less than 1% rejects. Ask suppliers to state the conditions behind every rate: product and package format, number of operators, shift length, accepted-reject criteria, planned stops, and whether the figure is a design target or an observed result.
Questions that expose bottlenecks
- Can the feeder present product at the required rate and orientation?
- Does the gripper handle the full range of weights, shapes, surfaces, and packaging materials?
- How much accumulation is available when an upstream or downstream machine pauses?
- What happens when vision rejects a product or loses confidence?
- How long does it take to clear a jam and restart without creating hidden scrap?
- Can utilities, sanitation, guarding, and access support the planned cycle rate?
Can one makeline handle multiple SKUs?
Yes, when the line is designed for recipe-based variation. A recipe can select motion parameters, feeder settings, vision thresholds, pack patterns, label data, and case or pallet configurations. Software alone is not enough: the product must feed reliably, the gripper must tolerate the range, and change parts must be quick to install and validate.
What the case studies demonstrate
TePe’s line makes mixed or customer-specific toothbrush packs by changing recipes in seconds. That capability turns a short-run or personalized order from a disruptive manual exercise into a controlled production option. The Inland end-of-line system handles four SKU configurations and reports changes in less than four minutes. ABB’s pretzel installation changes bag sizes in a few minutes through a few HMI button presses.
Those results depend on the equipment around the robot. A new package size may require a different infeed guide, gripper fingers, carton pattern, film setting, vision recipe, or pallet layer. If any of those changes require extensive manual adjustment, the nominal recipe change will not equal a production-ready changeover.
Flexibility checklist
- Product envelope: define minimum and maximum dimensions, mass, temperature, fragility, surface friction, and allowable orientation.
- Feed presentation: verify that every SKU arrives singulated and within the robot’s pickup window.
- Tooling: prefer common tooling or quick-change components where possible; document every manual swap.
- Vision: maintain separate validated recipes for appearance, orientation, print, and seal differences.
- Pack logic: test mixed packs, partial layers, case counts, and customer-specific patterns.
- Controls: protect recipes with permissions, revision history, backups, and a controlled approval process.
- Validation: run first-piece checks and a defined quantity at production speed before releasing a new recipe.
How fast can changeovers be?
Changeover is a measurable production capability, not a marketing adjective. A useful measurement starts when the last good unit of the previous SKU leaves the line and ends when the first good unit of the next SKU is accepted at the planned rate.
Measure the entire changeover
| Metric | What to record |
|---|---|
| Elapsed minutes | Time from last good unit to first good unit at target conditions |
| Operator actions | Recipe selection, tooling swaps, guide adjustments, cleaning, and confirmations |
| Validation time | First-piece inspection, vision verification, label or print approval, and quality release |
| Restart scrap | Units rejected while settings stabilize or materials are purged |
| Repeatability | Variation across operators, shifts, and SKU pairs |
Rockwell and Yaskawa materials connect changeover performance with OEE and throughput, which is why a short nominal adjustment is not enough. A four-minute mechanical change followed by 20 minutes of recipe verification and restart scrap is a 24-minute changeover for capacity planning.
Practical ways to shorten the event
- Store validated recipes and require role-based approval before edits.
- Use keyed or error-proofed change parts so guides and grippers return to known positions.
- Move cleaning, material staging, and tool inspection outside the production stop when safety rules permit.
- Display a step-by-step HMI checklist with confirmation of each critical setting.
- Capture the actual start, first-good, and stable-rate timestamps automatically.
- Review the two longest steps after every changeover and improve those first.
Will automation pay for itself?
It can, but the payback period is specific to the site. OMRON’s AFA Group case study reports measurable ROI within two years after a 90% direct-labor reduction. That result cannot be transferred directly to a different product, wage structure, utilization rate, or implementation scope.
Build the business case from site data
Annual benefit should include only savings and additional contribution that the installation can actually deliver. A basic model is:
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Annual net benefit = labor reduction value + additional gross margin from saleable output + avoided scrap and downtime costs − added operating costs.
Added costs can include maintenance contracts, spare parts, utilities, software support, sanitation time, training, inspection consumables, and temporary labor during ramp-up. Capital cost should include robots, conveyors, guarding, controls, vision, tooling, integration, installation, validation, facility changes, and contingency.
Then test the model under conservative, expected, and optimistic cases. Change the assumptions that matter most: actual OEE, sustained rate, shift count, labor coverage, reject rate, ramp-up duration, and the percentage of demand that can use the new capacity. Treat a case-study payback as evidence that a design can work, not as a forecast for your plant.
Evidence to request before approval
- A station-by-station cycle-time model showing the constraint.
- Current and projected labor by shift, including maintenance and quality coverage.
- OEE definitions, baseline data, and the method used to calculate projected improvement.
- Reject, rework, and restart-scrap assumptions for every SKU.
- Changeover trials covering the hardest SKU pair, not only the easiest one.
- Ramp-up milestones, acceptance tests, training, and support response times.
- Utility loads, footprint, guarding, sanitation, and access requirements.
Quality and safety: where integration matters most
Integrated vision and controls can make inspection more consistent and can stop a line before a defect propagates. The CP Foods UK example reports less than 1% rejects alongside 85% OEE. TePe’s reference describes recipe-driven production for mixed packs. Neither figure proves that every product will achieve the same result; inspection lighting, defect definitions, product variability, and upstream presentation determine performance.
Safety must be engineered with the whole cell in mind. Risk assessment should cover robot reach, conveyors, pinch points, stored energy, access doors, interlocks, emergency stops, restart behavior, and manual intervention during jams or sanitation. A line that is fast but difficult to access safely will lose availability whenever a fault occurs.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Common failure modes and how to prevent them
The robot is faster than the feeder
Symptom: the robot waits for product or receives poorly oriented items. Fix: measure feeder availability and presentation at the robot, add accumulation where justified, and redesign singulation before increasing robot speed.
Changeover time is understated
Symptom: the advertised recipe change is quick, but operators spend much longer on guides, tooling, cleaning, or quality release. Fix: time the complete event through first good product at stable rate.
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Flexible tooling damages product or causes rejects
Symptom: one SKU works while another slips, scuffs, collapses, or arrives out of position. Fix: test the full product envelope and validate gripper force, contact surfaces, and release timing at production speed.
Operators bypass the automation
Symptom: staff remove guards, disable checks, or manually finish packs to keep output moving. Fix: design safe recovery procedures, provide accessible diagnostics, and include operators in trials and training.
Data cannot explain downtime
Symptom: the line reports low OEE but cannot distinguish feeder starvation, blocked discharge, vision rejects, changeover, or mechanical faults. Fix: define a downtime taxonomy, expose events in the HMI, and review losses by station and cause.
A practical decision framework
- Define the demand: volumes, shifts, SKU mix, pack formats, seasonality, and required service levels.
- Map the current line: labor by task, cycle times, bottlenecks, OEE, rejects, changeover minutes, and downtime causes.
- Run a feasibility trial: test the hardest products, smallest orders, worst-case materials, and required inspection conditions.
- Specify the future cell: robots, feeders, grippers, vision, controls, guarding, utilities, sanitation, data, and operator access.
- Validate capacity: use sustained production trials rather than a short demonstration.
- Validate flexibility: perform repeated SKU changes and measure every step and restart loss.
- Approve the economics: compare conservative, expected, and optimistic payback cases.
- Plan the operating model: training, spares, preventive maintenance, support, recipe governance, and continuous improvement.
The bottom line for labor, throughput, and customization
Automated makelines are most valuable when they coordinate the entire flow rather than automate one isolated motion. The strongest published examples show labor falling from up to 12 people to two or three, direct labor falling 90%, throughput reaching 300 packs or 120 trays per minute in specific applications, and SKU changes taking seconds to under four minutes. Those figures are credible reference points, not guarantees.
For a purchase decision, insist on a site-specific model that ties staffing, sustained constrained-station throughput, OEE, rejects, changeover validation, maintenance, safety, and ramp-up time to your actual products. That is the difference between buying a faster robot and building a line that delivers reliable capacity.
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