One significant trend is the emergence of mixed-SKU depalletizing. As 3D vision systems continue to improve, robots will become increasingly capable of identifying and handling pallets containing different products, making this type of application more common.
The State of Robotic Depalletizing
Q&A with Luc Vanden-Abeele, Director of Marketing | NuMove Robotics & Vision
Tell us about yourself and your role with NuMove Robotics & Vision.
My role at NuMove is Director of Marketing. I oversee all marketing and communication activities (web site, blog articles, trade shows, LinkedIn posts, etc.). I help the Sales team to prepare proposals in peak periods and handle all IP related tasks (patents, trademarks, etc.). My background is mechanical engineering, coupled with an MBA. I have been working in the manufacturing world for now 37 years, and I started with NuMove in 2019.
NuMove Robotics & Vision is a manufacturer and integrator of industrial robotics solutions for material handling tasks like depalletizing, end-of-line palletizing, case packing and mixed-SKU palletizing. NuMove is active in various industries like food & beverage, warehousing & distributing, folding carton and consumer goods.
Why do you feel robotic depalletizing should be seriously considered in 2026?
In 2026, robotic depalletizing should deserve serious consideration because it addresses several challenges facing warehouses and distribution centers. Labor availability remains a major concern, and depalletizing is one of the most labor-intensive tasks in material handling. It is also physically demanding, requiring repetitive movements leading to health issues for workers as it increases the risk of musculoskeletal injuries.
At the same time, robotics technology continues to advance. Depending on the product and packaging, robotic depalletizing can handle multiple products at each robot cycle, reaching rates that humans cannot meet. Combined with the consistency of robotic performance and improvements in machine vision and AI, depalletizing automation is becoming an increasingly practical way to improve productivity, safety, and operational resilience.
Can you breakdown the pros and cons of single unit depalletizing, row depalletizing and layer depalletizing?
Unit depalletizing is the most economical option, offering the lowest cost, smallest footprint, simplest end-of-arm tool, and the ability to handle a wide variety of packaging when paired with the correct end-of-arm tool. Its main limitation is throughput.
Row depalletizing provides a middle ground. It delivers higher throughput than unit depalletizing while maintaining packaging flexibility and eliminating the need for downstream unscrambling. However, it remains slower than layer depalletizing and products still require turning to be single filed.
Layer depalletizing offers fastest throughput. The trade-off is a larger investment, requiring a bigger footprint, a larger robot, more complex tooling, and additional downstream equipment such as an unscrambler and turning device. It may also have limitations with certain packaging types.
What is semi-automatic depalletizing and in which context is this solution valuable?
Semi-automatic depalletizing is a practical alternative when full automation is either technically difficult or economically challenging to justify. In many distribution centers, not every SKU can be depalletized automatically. Some odd-shaped products or hard to handle packaging (like pet food pouches) cannot be depalletized with a robot using a “generic” end-of-arm tool. In these situations, a semi-automatic approach can provide the flexibility needed without relying on complex or impractical automatic tool changes.
It can also be a strong fit for smaller customers that might not have the required initial investment capabilities to afford a fully automated system. By combining operator involvement with automatic pallet handling, semi-automatic depalletizing keeps up front cost down but helps the operators increase the rate and reduce physical strain.
Should the robot always be guided with machine vision and why?
While a robot can operate without machine vision, it is always preferable to use it for depalletizing. One reason is that products often shift during forklift handling. Machine vision allows the robot to identify the actual position of products rather than relying on their expected location, improving reliability during the depalletizing process.
Machine vision also provides an additional layer of verification by confirming that the correct product is being presented to the cell. This helps ensure the robot is interacting with the intended load and can reduce the risk of processing errors. As a result, machine vision contributes to both operational robustness and product validation within the depalletizing system.
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What are the financial benefits of robotic depalletizing?
The financial benefit of robotic depalletizing is more than just direct labor savings. While reducing manual labor is often an important driver, organizations can also benefit from lower workforce injuries, particularly in a task that involves repetitive and physically demanding movements.
Robotic depalletizing can also help reduce damaged products and reduce human errors, which can have a direct impact on operational costs. In addition, the consistent performance of robotic systems supports productivity gains and greater operational stability as robots can maintain a predictable level of output. Taken together, these factors can contribute to a stronger business case and improve the overall economics of warehouse and distribution center operations.
What is on the horizon for robotic depalletizing? What are some of the roadblocks that need to be overcome for mass adoption?
One significant trend is the emergence of mixed-SKU depalletizing. As 3D vision systems continue to improve, robots will become increasingly capable of identifying and handling pallets containing different products, making this type of application more common. AI is also expected to play a larger role in image processing and robot path planning. At the same time, more flexible end-of-arm tools are being developed to accommodate a wider variety of packaging types and formats. Together, these improvements are expanding the range of depalletizing applications that can be automated.
One challenge is SKU proliferation, which increases the variety of products and packaging formats that systems must handle. At the same time, packaging quality is changing, with cases, trays, and wrapping materials getting smaller, thinner and of lower quality, creating handling challenges. Adoption among small and mid-sized operations is another factor, as these facilities may face greater barriers when evaluating automation investments. Greater standardization across inbound supply chains, including pallet dimensions, layer patterns, and wrapping methods, would also simplify deployment. Finally, organizations will need the technical skills within the warehouses and distribution centers to operate and maintain the technologies implemented.
The content & opinions in this article are the author’s and do not necessarily represent the views of RoboticsTomorrow
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