Solutions
Yango Robotics Picker (Kitting, PutWall, Induction and G2P solutions)
The complexity of managing diverse inventories, which can change daily, is a significant challenge for many warehouses.
Our AI-driven robotics pick-and-place solution is designed to grasp and relocate objects efficiently. Picker solutions are tailored for navigating warehouse environments and moving goods, aiding in tasks such as inventory organization and preparation for shipping.
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Kitting
2
Putwall
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Induction
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G2P
About Kitting
Our Robotic Kitting Solution offers a highly efficient and structured approach to the identification, selection, and packaging of subscription kit items.
Designed to cater to a variety of items, including boxes, envelopes, food containers, and more.
Adaptable configurations enable you to achieve higher production rates while simultaneously reducing operational costs and dependency on manual labor.
Item Versatility
The system is capable of handling a wide range of items, from standard boxes to delicate envelopes and food containers, ensuring that your subscription kits are assembled with precision and care
About Putwall
Yango Robotics picker transforms warehouse operations by seamlessly integrating advanced automation into the traditional put wall process, enhancing efficiency and accuracy in item handling and order fulfillment.
Intelligent Item Identification
Leveraging cutting-edge computer vision technology, our system accurately identifies a vast array of items, from various shapes and sizes to products with intricate features. This capability ensures a smooth and precise picking process, accommodating the diverse inventory found in modern warehouses.
Optimized Item Allocation
Our system intelligently allocates picked items to the appropriate put wall compartments based on predefined criteria. This ensures a streamlined organization of products, facilitating an efficient packing and shipping process. Its flexibility allows it to adapt to different packaging requirements and order specifications seamlessly.
Seamless Integration and Scalability
Designed for effortless integration into existing warehouse environments, our piece picking solution is scalable to meet the evolving demands of your business. Whether your facility is a small operation or a large distribution center, our system can be customized to enhance your operations without causing significant disruptions.
Boosted Operational Precision
By reducing the reliance on manual labor, our automation solution significantly decreases the likelihood of errors, thereby enhancing overall operational precision. This leads to fewer order discrepancies, heightened customer satisfaction, and a marked increase in the efficiency of your fulfillment processes.
About Induction
About Induction
Manual induction, a task marked by repetition, is vulnerable to injuries and high turnover rates, posing staffing challenges. Moreover, inaccurate item placement on sorters often causes frequent jamming, disrupting throughput.
Our robotic induction system employs AI-powered robots to autonomously handle the task of inducting items onto sorters, eliminating the need for humans to perform this repetitive and injury-prone task. Through our Induction solution we empower robots to execute this task with exceptional precision, speed, and accuracy consistently.
Picking enabled by AI for any
type of SKU
type of SKU
In the realm of robotic piece-picking within warehouses, the extensive array of SKUs and items presents an array of variations: differing shapes, sizes, deformability, transparent and mixed-material packaging, among others
Adaptable Induction across Sorters
For any robot integrated into the induction process, it's vital to adjust placement strategies to align with existing operational systems
Speed and Accuracy
In both manual and robotic operations, sorter jams due to inaccurate item placement can lead to downtime. Addressing this with robotic systems demands purposeful and innovative solutions that match human speed while improving accuracy
Seamless Integration
A robotic induction system should seamlessly integrate into existing operations and workflows without necessitating disruptive floor plan changes and aligning with existing systems
About G2P
This technology is ideal for split case order fulfillment. There are different types of Goods-to-Person (G2P) technology, but the principle is the same: an automated storage system delivers SKUs to a stationary pick station, where the operator fills discrete orders.
Our state-of-the-art Piece Picking system seamlessly integrates into this process, ensuring unparalleled efficiency and accuracy.
Smart Item Identification
Our system employs advanced computer vision technology to recognize a wide variety of items, regardless of shape, size, or detail. This ensures a smooth and precise picking process, handling diverse inventory with ease.
Efficient Induction Workflow
By automating item selection, Yango Robotics picker streamlines the induction workflow. Items are quickly and accurately picked and placed in the induction area, allowing the rest of the fulfillment process to proceed without delays or errors. This results in a more organized and efficient induction phase, boosting overall warehouse productivity.
Easy Integration and
Scalability
Scalability
Our Robotics picker is designed to fit seamlessly into existing warehouse setups. Its scalability means it can grow with your business, whether you have a small facility or a large distribution center, enhancing your operations without causing major disruptions.
Improved Accuracy
Our automation solution reduces the need for manual work, minimizing errors during the induction process. This leads to fewer order mistakes, happier customers, and greater efficiency in your warehouse operations.
Dynamic Sorting
Our system not only picks items accurately but also sorts them based on predefined criteria. This ensures that items are correctly organized during the induction process, making packing and shipping faster and more efficient. It can handle different packaging requirements and order specifications with ease.
Mon Sep 16 2024 12:45:15 GMT+0300 (Moscow Standard Time)