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Pick and Place Machine Tending Is Helping to Combat Supply Chain Chaos

Due to historically low unemployment rates, businesses are having problems filling jobs across the board, making automation not just a luxury, but a necessity for many firms. The UKG Workforce Institute think tank has found that 54 percent of businesses are presently having trouble finding qualified workers, up from 38 percent prior to the coronavirus pandemic. Given these facts, the use of vision-guided robots (VGRs) for bin selection has several benefits.

Because autonomous mobile robots (AMRs) and flexible bin-picking collaborative robots (cobots) are able to manage dirty or dangerous material movements, they make many jobs easier and safer to do. Manufacturing and storage processes can benefit from robots, since they free up employees to focus on higher-value tasks. In general, goods producers can benefit from robotic machine tending in the face of labor shortages, increased order volume, and disruptions in the supply chain.

Issues with Labor

At present, there are broader labor issues than just a shortage of available workers. Companies in the manufacturing and warehousing sectors have significant turnover and constantly need to hire and train new workers.

Production can be adversely affected by a lack of workers. Accidents involving overworked teams in understaffed businesses result in lost time and money due to equipment damage and employee injuries. These labor issues cause delays, which cost money and slow down production.

In addition to issues with labor, current supply chains are disorganized and volatile. Challenges include rising demand, carrier rate increases, fluctuating inventories, and evolving customer service expectations.


Manufacturing and warehousing are impacted by higher order volumes, an unpredictable inventory and product mix, and shifting customer service desires. Businesses may need to produce and stock more goods to keep up with rising demand, which puts a strain on resources and increases costs.

Managing product mix changes and variations in inventory availability while adjusting production and storage to meet fluctuating demand is a time-consuming and costly process. In the meantime, customers desire faster service, individualized attention, and other value-added services, all of which call for streamlined and flexible operations.

These problems can increase production and storage expenses, slow down output, and reduce efficiency. In order to combat these challenges, businesses can employ bin picking and other strategies to improve inventory management, operations, and business agility.

Disruptions in the Supply Chain

Online business is growing. When it comes to item-level fulfillment, companies need to handle a higher volume of smaller orders, which can be more difficult and time-consuming to manage. This extends lead times for manufacturing and inventory. Automation, bin selection, analytical data, and other quality, robust systems are all being utilized by businesses to keep up with customer demand.

Firms need to adapt to the changing ways of working necessitated by the introduction of new technological tools and systems. Production and storage facilities need to be more nimble, efficient, and flexible in order to keep up with market and customer demands. Therefore, businesses need to train workers on the latest tools and techniques and invest in cutting-edge infrastructure.

Three Styles of Bin Picking

Robotic bin picking, in which a robot uses sensors and cameras to identify and choose an item from a bin or container, can help factories and warehouses circumvent these challenges.

With robotic bin picking, an item is located, grasped, and transferred by a robotic arm to another device or location for further treatment or packaging. The system can be adjusted to precisely and effectively choose from a wide variety of items and containers in a manufacturing, industrial, or logistical environment.

VGR bin-picking satisfies all requirements for use in a commercial setting. Bin selection can be structured, semi-structured, or unstructured:

  • In a structured selection process, bins with orderly, predictable contents are utilized. Since items are arranged in a uniform pattern, a robot only needs to locate them and choose them.
  • Semi-structured bin selection uses randomly placed objects that have an inherent similarity in orientation or shape. The robot can then use preset motions to pick up objects it’s located.
  • In unstructured bin picking, object shapes, sizes, and orientations can be random. This is the most challenging type of bin selection, since the robot has to use sophisticated sensors and algorithms to locate and differentiate objects. In these instances, the robot may have to turn an object around completely before it can be chosen.

The Demand for Picking Robots Is Expected to Rise by 2030

UK-based marketing intelligence firm Interact Analysis predicts that by 2030, shipments of warehouse inventory-picking robots will increase from over 2,000 per year as of 2022 to more than 50,000 annually. Rising labor expenses and decreasing robot prices have led to predictions that 150,000 such robots will be deployed in the United Kingdom by 2030. This number will likely be further influenced by factors such as labor shortages, wage inflation, and developments in AI and machine vision.

Due to labor issues, bottlenecks, uncertainties, and disruptions in the supply chain, manufacturers and warehouses need to be agile and responsive to changes in demand.

Keeping inventory on hand can be time-consuming and costly. Quick, reliable, and individualized service is what customers desire from companies. Businesses need to be able to speed up production and shipping without sacrificing precision.

The complexity and expense of production and storage are increasing as a result of all these variables. In the ever-growing e-commerce sector, businesses that rise to these challenges will have a leg up on the competition. AMRs and bin-picking cobots will allow businesses to adapt to constant production shifts and promptly fulfill a high volume of small requests.

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