A 20 kg cobot palletising cell can remove one of the least attractive jobs on a production floor: repeatedly lifting finished cases, bags or trays at the end of a line. But the 20 kg figure alone does not tell you whether the application will work. The real decision depends on the combined load, pallet pattern, required output, available floor space and the way people need to work around the cell.
For many manufacturers, palletising is a strong first automation project because the process is repetitive, physically demanding and easy to measure. It also exposes a common mistake: selecting a robot based on carton weight rather than the total load at the robot flange. A sound assessment starts there.
A cobot rated for a 20 kg payload can handle up to 20 kg at its wrist under specified operating conditions. That limit must cover more than the product. The end-of-arm tooling, gripper fingers or vacuum plate, fittings, cables and any intermediate layer sheets all count towards payload.
Consider a 14 kg case handled by a 5 kg gripper. The nominal load is already 19 kg before allowing for a pallet sheet, product variation or a sensible operating margin. This is not a good design simply because the numbers technically fit. In production, case weights change, packaging formats evolve and grippers may need modification. Running permanently close to the rating can constrain speed and leave little room for a practical upgrade.
The load also changes with reach. A heavy case held far from the robot creates greater torque than the same case close to the base. Its centre of gravity matters, particularly with long cartons, bags that shift internally or a gripper that holds a product off-centre. A feasibility review should therefore use the actual product, proposed tool and final pallet position, rather than a catalogue payload number.
The pallet pattern determines nearly every key engineering decision. Before choosing equipment, document the case dimensions, case weight range, layer configuration, pallet type, maximum stack height, infeed orientation and whether sheets, caps or corner protection are required.
The highest and furthest placements are usually the critical points. A robot might lift the product comfortably at a low level but struggle to place the top layer at the required pallet depth. Reach must be checked through the full working envelope, including clearance around conveyors, guarding and the pallet itself.
There is also a choice between a fixed pallet position and a pallet that is moved during stacking. A fixed position can simplify mechanics, but it asks more of robot reach as the stack grows. A lift table or pallet conveyor may reduce the robot's vertical travel and improve cycle time, though it adds equipment, controls and maintenance. The best layout is not always the one with the fewest components. It is the one that meets output reliably without making changeovers difficult.
Vacuum tooling is often effective for sealed cartons with stable, non-porous surfaces. It can be light and quick, but it is less forgiving of dusty board, uneven tops, perforations or poorly sealed cases. Mechanical grippers provide a positive hold for many difficult products, although they add weight and can require more clearance between cases.
For lightweight products, handling two cases at once may be the route to the required throughput. This reduces the number of robot cycles but increases tool complexity and makes product variation more consequential. For heavier loads, single-case handling is generally the more realistic starting point. A tooling trial with genuine packaging is worth more than assumptions based on dimensions alone.
Palletising performance should be calculated from line demand, not guessed from a robot's maximum speed. Start with cases per minute, then convert that requirement into the available time per pick-and-place cycle. Include pick, lift, transfer, placement, gripper actuation, pallet transitions and any pauses caused by the upstream line.
A line producing 10 cases per minute gives six seconds per case in principle. That is not the same as giving the robot six seconds of motion time. If a pallet change takes two minutes, the system needs enough buffering or planned downtime to absorb it. If cases arrive inconsistently, the cell needs a queue and a control strategy that prevents missed products.
Collaborative robots are often a strong fit for low-to-medium throughput lines, mixed product runs and applications where floor space or operator access matters. They are less suitable where the process demands very high, uninterrupted rates with heavy products and long reaches. In those cases, dedicated high-speed palletising equipment may provide the more economical answer, even if its initial cost is higher.
This is not a weakness to disguise. A correctly sized 20 kg cobot palletising solution can deliver predictable output and a faster deployment path. A mismatched one can become a bottleneck that operators must continually support.
A cobot does not remove the need for a proper risk assessment. A moving 20 kg payload has meaningful kinetic energy, and a palletising application introduces pinch points around conveyors, pallet loads and tooling. Safe operation depends on the complete workstation, its speeds, access routes, detection devices and control logic.
In some layouts, reduced-speed collaborative operation can allow people to load consumables or remove finished pallets nearby. In others, safeguarded operation with scanners, interlocked doors or controlled access will be the better option. The answer depends on the task and the risk assessment, not on the robot label.
Designing the operator's role is equally important. If an operator must bend into the cell to place pallets, clear faults or replenish sheets, the project may merely move the ergonomic problem. Good workstation design considers pallet delivery, fork-lorry access, label scanning, material replenishment and fault recovery from the outset.
The business case should not rely solely on replacing a headcount. Palletising is often difficult to staff consistently, especially across late shifts, repetitive packing operations or seasonal peaks. The value may come from stabilising output, reducing manual handling exposure and allowing skilled operators to focus on quality checks, machine tending or changeovers.
A useful ROI model includes labour coverage by shift, actual operating hours, expected uptime, packaging waste caused by poor stacking, floor-space changes, integration cost and planned maintenance. It should also include the cost of manual fallback during faults and product changeovers. Honest modelling produces a more credible payback than an optimistic labour-saving figure.
For operations with frequent SKU changes, the savings can depend heavily on how quickly staff can select a new pallet recipe. A practical interface should make this controlled and repeatable, with protected parameters for safety-critical settings. Operators should not need to edit robot paths to change from one approved pallet pattern to another.
Begin with a short application study using actual cases, pallets and production data. Measure the heaviest product, the worst-case dimensions and the true line rate rather than the target rate. Then assess reach, tool mass, stack geometry, safety requirements and the expected pallet-change process.
Where possible, test the gripper and motion sequence with real packaging before committing to a workstation. FAIRINO Europe can support this through technical feasibility work, showroom testing, ROI calculation and workstation design, helping teams establish whether an FR20-based cell is appropriate before integration begins.
The final design should leave room for normal production variation. It should tolerate small differences in case condition, cope with planned changeovers and provide a clear method for recovering from a stopped line. The goal is not an impressive demonstration cycle. It is a cell that continues to palletise reliably on an ordinary Wednesday afternoon.
A 20 kg cobot palletising project pays off when the payload margin, reach, cycle time and operator workflow all agree. Bring the real product and real production data into the assessment early, and the resulting system is far more likely to earn its place on the line.
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