Artificial intelligence (AI) has been at the forefront of the packaging industry for decades, and each year, it seems there’s a new process being revolutionized by AI developments. From streamlining monotonous sorting work to precisely applying traceable labels onto product packaging, automated systems are continuously improving production line efficiency and forcing us to rethink what’s possible. Right now, for example, many are looking towards artificial intelligence when considering possible solutions to the global supply chain crisis that’s currently causing scarcities of everything from breakfast cereal to computer chips.
There are many benefits of AI in the industrial coding industry, and line operators of all industries rely on it to keep operations running smoothly. For instance, if there’s a derailment or intrusion on the line, AI systems can raise an alarm to help avoid damages or complete line shutdown. In another example, today’s high-end continuous inkjet printers use AI to perform self-diagnostic tests and create on-screen instructions to communicate how to maximize uptime.
Here, we take a look at three of the most high-profile ways that businesses are currently using AI in packaging and palletizing to enhance their operations. We will also look at how tech advancements are promising to improve packaging efficiency in the future.
Some of the current technologies that utilize AI in packaging and palletizing include validation systems, automated line inspection platforms, and depalletizing systems. Below, we detail each technology and how it’s impacting the industry.
Successful supply chain networks are built on traceability and quality assurance. Traceable codes and markings, such as lot codes, bar codes, and date stamps, enable distributors to fully map out:
With this information, manufacturers can more effectively perform quality assurance, distributors can maintain highly accurate inventory, and regulators can ensure that products are safe for use by the public. In fields where companies are required to follow strict government regulations and/or industry safety guidelines (e.g. agricultural production, food packaging, pharmaceutical development, and aerospace/automotive manufacturing), these traceability systems are invaluable. However, if product codes are impossible to scan or read, traceability gaps begin to appear, increasing the risk of supply chain complications. Validation systems help ensure this doesn’t happen.
Validation systems come in different forms to help companies streamline specific aspects of the packaging process. For example, vision systems use cameras, sensors, and lights to ensure products and packages are free of defects and aligned with specified dimensions. Code validation systems can also be positioned along production lines to automatically verify that codes such as barcodes or data matrices are machine-scannable. Similarly, optical character recognition (OCR) systems ensure that all applied text is legible to the human eye.
These systems provide packaging companies with the tools necessary to minimize the risk of human error and accelerate the quality assurance process, thus leading to increased productivity overall.
To improve the effectiveness of validation systems and line performance in general, packaging software manufacturers have been developing AI platforms that automatically map out lines to detect points of low productivity. Utilizing deep learning technology, these platforms continually work to predict potential problem sources, crunch data, and suggest points for improvement.
These analytics not only help plant managers and line operators more intelligently rework their setups, but they also boost the effectiveness of validation systems by lowering false rejection rates. For added convenience, all of this data can be accessed remotely via computers connected to the larger enterprise network.
Much of the product packaging process involves work associated with either palletizing (e.g. placing products in boxes/cases, uniformly stacking the boxes/cases, and wrapping the pallet) or depalletizing (e.g. removing the products from their cases, organizing them, counting inventory, and performing quality assurance). While palletizing systems have maintained a high profile for decades, depalletizing systems have traditionally made fewer waves in mainstream discourse. However, many recent AI advancements have shown how depalletizing technology can improve daily operations by working alongside validation systems.
A good example of how AI can be used in depalletizing processes is in food can depalletizing. The latest depalletizing hardware can take shipments of cans, extract them from the shipment, and stack them four layers high on a conveyor belt. There, a vision system will scan the barcodes on the cans to verify that the inventory count is correct. Meanwhile, an OCR accounts for elements like date codes and lot numbers to maintain front-to-back traceability.
Through these measures, the depalletizing system does more than just expedite unloading steps—it works with the vision systems installed along the production line to ensure traceability and that all products are present, accounted for, and free of defects.
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