OSARO®, a global leader in machine-learning-enabled robotics for e-commerce, has launched the OSARO® Robotic Depalletization System. This latest advanced warehouse automation system from OSARO takes aim at one of the most dangerous and labor-intensive jobs in the e-commerce fulfillment and warehousing: the daily unloading of hundreds of heavy and cumbersome boxes and packages that arrive at a warehouse on a standard pallet.
“Depalletizing is one of the most labor-intensive workflows in a warehouse,” explained Ash Sharma, managing director at research firm Interact Analysis. “More than three billion pallets will be depalletized this year globally, requiring one-quarter of a million FTEs.”
The OSARO Robotic Depalletization System is equipped with OSARO SightWorks™ Perception Software, which enables the robot to recognize, select and successfully grasp the varied sizes and materials of unevenly stacked packages commonly found on mixed-case pallets that arrive at a loading dock. OSARO’s precise perception technology can recognize foreign objects or damaged boxes, and alert workers so the hazard can be removed before injury or further damage. The system increases efficiency, improves safety, reduces labor costs, and minimizes inventory shrinkage.
The OSARO Robotic Depalletization System is available immediately for unloading of mixed-case pallets in 3PL operations, distribution centers, and e-commerce fulfillment warehouses. The system’s baseline features include:
- Mixed-case box recognition on complex pallets — identifies center, dimensions, and orientation; determines damage score or error conditions.
- Foreign object detection — identifies foreign objects such as box cutters, tools, and tape dispensers.
- Damaged box detection — identifies damaged or structurally unsound boxes to avoid risky picks.
- Protruding box detection — identifies boxes that are within a defined distance beyond the pallet coordinates.
- Flexible deployment — built on skids that can easily be moved with a forklift within or between facilities.
OSARO enables fully automated mixed-case depalletization
Pallets are a universal necessity in warehouses but pose serious injury risks, mainly because of their size, weight, handling procedures, and potential for structural failure. The task of unloading them is slow, hard work that can be dangerous and cause damage to sometimes fragile boxed products that are stacked unpredictably. OSARO’s proven AI-powered vision system quickly determines case angles, which cases are on top, and which should be picked first. The system then selects the appropriate end-of-arm tool to successfully grasp each package without interfering with adjacent cases.
“While automated depalletization for uniform pallets has been available for some time, OSARO’s new intelligent robotic system addresses an increasingly common scenario at the receiving dock: palettes stacked with an unpredictable variety of boxes and shrink-wrapped packages of different shapes, sizes, and weights,” said OSARO CEO Derik Pridmore. “Our robotic depalletization system can safely unload these pallets and deliver up to 40 percent cost savings when compared to manual labor. And, as more shifts are added, savings increase significantly.”
Makes warehouses safer and reduces physical toll on workers
Another compelling incentive driving the automation of depalletization is the need to reduce reliance on manual labor in the face of a persistent worker shortage – made even more severe by the undesirable nature of depalletization jobs, which are dangerous and increasingly require overtime or weekend work. Overall, depalletization is the function on which the greatest amount of human labor is expended in the warehouse and at the highest risk for injury. The U.S. Occupational Health and Safety Administration (OSHA) has estimated the total economic burden of such injuries — as measured by compensation costs, lost wages, and lost productivity including ancillary costs, such as the need to pay other workers overtime to cover missing workers — is between $45 and $54 billion annually.