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Applying and Reading Labels

Almost half of manufacturers – and many logistics providers – experience costly downtime due to labelling disruptions.

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In logistics, accurate labeling plays a crucial role, ensuring the smooth flow of goods from point of origin to destination. Labels serve as critical markers, providing essential information about products, packages, and shipments – have they cleared customs yet, do they meet certain standards, or do they contain dangerous goods, as examples.

But there are several challenges to labeling processes which can cause inefficiencies and errors in the supply chain.

Firstly, manual labeling is a time-consuming and error-prone task. Today, many labels are affixed onto packages and products, from smartphone boxes to glass vials, by human operators. This can result in slower processing and possible mistakes, further leading to misplaced shipments and increased costs due to the need for manual intervention and rework – or worse, recalls.

Secondly, there are challenges associated with reading and interpreting labels accurately. Factors such as poor label quality, damaged or obscured labels, variations in label design, and inconsistencies in labeling standards can hinder the automatic detection and reading of labels. This may cause errors in inventory management, order fulfillment, and transportation planning, negatively impacting the overall supply chain performance. 

As such…

We Wonder...

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How can technology help automate the labeling process on various materials like cardboard or curved glass, reducing human error and improving supply chain efficiency?

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Which solutions based on image recognition or scanning technologies can accurately detect and read labels even with poor label quality, damaged labels, and other challenges?

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Are there any next-generation alternatives to physical/sticker labels for various use cases? 


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