March 7, 2020 10 min read

How to Improve Barcode Scanning Accuracy


Asset Tracking

Delivery Tracking

Inventory Audit

Process Tracking

Track & Trace

Warehouse Operations


What’s the Problem? It’s Collecting Bad Data.

According to an article published by the MIT Sloan Business Review, IBM estimates the cost of poor data quality at $3.1 trillion annually in the US alone (IBM Infographic). Also, it states that the “cost of bad data is an astonishing 15% to 25% of revenue for most companies”.

We can’t comment on the accuracy of those figures. However, it’s clear that bad data is a big problem.

Inaccurate Data Capture Contributes to the Problem

According to the GS1 organization, globally, people make 5 billion barcode scans every day. If just 1% of those barcodes are misread, there are at least 50 million bad scan values daily.

What does that mean relative to the cost of bad data? The real cost is difficult to estimate but a leading barcode label company, NiceLabel, wrote a blog article offering some interesting perspectives. In summary, they point out that as a business grows, the cost of bad data can spiral upward exponentially.

Add to that the full impact on enterprises.

For example, consider the real cost of using the wrong component in an assembly. Not only is the cost to correct this mistake very high but also, people can be injured or die from mistakes made. For instance, you can imagine cases from medical or aerospace assemblies. Thus, it adds a human cost on top of a monetary one.

Does the Lack of Data Contribute to the Problem?

It does. In fact, for some applications, the lack of data could be an even bigger factor than inaccurate data.

For example, one of our customers, Lily Transportation, was contracted by one of the world’s largest grain companies to implement a tracking system for deliveries. Why? Because a driver delivered the wrong grain which killed $250,000 worth of livestock. To stop that from happening again, they implemented a barcode tracking system to not only collect data but also to validate that the right grain is loaded into the right silo.

Use CodeREADr to Scan, Collect, Validate Data, and more

Please look here for an overview of the CodeREADr platform. The most important steps include the following improvements in your data collection efforts.

  1. Collect MORE data. Apply barcode labels and RFID/NFC tags wherever practical. Scan them with smartphones, tablets, or mobile computers. Batch scan 100 barcodes in seconds. The possibilities are endless.
  2. Know the who, what, when, where, how, and why an asset was scanned.  They are all standard data points with every scan. Additionally, collect comments, photos, GPS location, signatures, answers to prompts, etc. with drag and drop simplicity. Also, create Custom Questions for complex validation options.
  3. Minimize scan engine misreads with SD PRO which uses the built-in camera of smartphones and tablets.
  4. Use a database to validate every scan – online or offline.
  5. When a database isn’t available, use pattern validation with regex (we can help you write the regex script).
  6. Enable Alter Scan technology to match scans to the data formats you use.
  7. Use “Smart Scan” technology to make rules on what to capture and what NOT to capture.
  8. Apply Artificial Reality (AR) to target the right barcode.
  9. Add duplicate checking to stop scanning the same barcode twice. Auto-reset as needed.
  10. Select start dates, end dates, and the number of scans to allow.
  11. Apply Custom On-Device (COV) scripts to create rules specific to virtually any application.
  12. Deploy A/B Compare to match barcode scan to barcode scan.
  13. Enable digital list validation for kitting, picking, packing, assembly, etc.
  14. Deploy Table Builder to track and trace assets through the entire life cycle.

All this starting at $14.99/month.

Bulk Scan Serial Number Barcodes

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