How Logistics can Profit from OCR Technology

The logistics space is always recognized for its high-volume data, time pressure, and no room for errors. The goal of faster and more accurate data capture is a back-office challenge that most logistics companies have in common. Given that the largest firms may process upwards of one million pages of multilingual forms per day, the return on an effective data capture automation solution is enormous. Because the market is segmented into many small operations and operators, it’s hard to digitize and standardize. That’s why many processes in logistics still involve a lot of paperwork and manual processing.

The rise in e-commerce, online selling, and ordering of commodities and various other delivery services have massively increased the logistics segment’s dependency. A properly functioning logistics organization would encounter fewer issues relating to delayed delivery, the wrong item delivered, or delivered at the incorrect address. To ensure that such problems don’t occur, the logistics segment is heavily investing in Optical Character Recognition (OCR) technology. The global OCR Market was valued at US$ 6.8 Bn in 2019 and is expected to reach US$ 16.60 Bn by 2027, at a CAGR of 13.60 % during a forecast period.

(image source: www.maximizemarketresearch.com)

OCR is a technology that enables the conversion of different types of the document, such as scanned paper documents, PDF files, or images captured by a digital camera. OCR is used to convert non-editable soft copies into editable text. Therefore, you don’t need to retype the text in the images, BOLs, or PDFs manually to make the required changes. OCR is a tool that quickly converts such files into editable text versions.

How can OCR technology transform your business?

When we talk about OCR, it opens 4 critical benefits in the Logistics sector. Here’s an explanation of how it works.

Easy BOLs/Documents Search:

In logistics, if high volume data is stored in a physical document, mage, or pdf format, then it becomes complicated to search those Bill of Ladings files when you need them the most.  This kind of information affects your ability to search for something specific quickly. OCR technology enables you to convert that frozen text into machine-readable data so that it is searchable.

Make Easy Edits:

Your business needs OCR to convert those PDFs into editable text documents on the go. That means you don’t have to prepare a document from scratch when any changes need to be made. Instead, you can only change the part that requires updating.

Avoid Human Errors:

OCR is the best way to prevent human errors. It not only helps you to search and edit documents but also detects incorrect or misprinted data in your documents. Thus, most of the human errors can be proactively resolved using ORC technology.

Save Time and Money:

Most businesses are still hugely reliant on hard copy documents. OCR technology reduces not only your time but also your expenditure by reducing paperwork. Even if you want to transform your business now, you can quickly scan all your BOLs documents and images and store them into a digitalized format.

Key metrics in OCR technology

OCR technology for data capture automation removes nearly all manual input while also minimizing errors. Here are a few impressive numbers to back OCR technology in the logistics sector.

  • Cut data capture time and costs by up to 90% vs. a manual workflow
  • Virtually eliminate costly errors, with approximately 99.9% accuracy
  • Overall operating costs decreases as much as 28%
  • Decreased data entry FTEs by 90%

Conclusion

Automation is a no-brainer; let people do what they do best – and systems take care of the process and data. In this way, humans can then supervise the intelligent analytics and information they provide to make a real difference to customers. After all, if automation exists, it should make human life easier!

Would you like to learn more about how we can help your business run more efficiently?  Please contact us via email: marketing@winjit.com.

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