Obvious Barcode Mistakes
As a barcode test lab, we tend to focus on more exotic or subtle mistakes we encounter. However, the obvious mistakes, maybe less interesting, occur surprisingly often. Here are some we have seen recently.
Excessively long linear codes
Just because you can add data fields to linear barcodes does not mean you should. There is a limit to how long a barcode a scanner can capture within its field of view. Successful scanning is a balance of minimum bar width (AKA X dimension) and scanner resolution. The more data in a linear barcode, the further from the barcode the scanner must be positioned to take it all in—and do not forget the quiet zones. That additional distance effectively makes the narrow bars and spaces effectively smaller. We know this intuitively—an object far away looks smaller than the same object closer to us.
Here are some solutions:
- Rethink how much data your barcode really needs to contain. More is not always better. What does your supply chain require?
- Make sure you are using the chosen barcode type efficiently. Often we see GS1 compliant barcodes parsing variable data randomly in the data string. Locating all the variable data fields at end of the barcode eliminates redundant field separators.
- Use a more efficient symbology, which allows you to encode data in a smaller space.
Wrong barcode type
Yes, this is the same solution suggested above, and it can solve one problem and cause another. If your GS1-128 barcodes are getting too long, you could start using a GS1 Datamatrix code instead. This is not a unilateral decision. Make sure your trading partners—anyone who needs to scan your barcodes—is equipped to scan GS1 Datamatrix. Laser and CCD scanners can decode linear barcodes (parallel lines and spaces), but cannot decode matrix barcodes. Consult with your supply chain before changing to a different barcode type.
Inefficient Barcode Design
Recently we received Datamatrix symbols for verification. The UDI data they contained was basic: a GTIN, an expiration date and a batch number. Not a lot of data. For reasons unknown, a four quadrant form of Datamatrix was used. This caused the design software to add unnecessary “filler” data to comply with the additional data capacity of this format—and that caused the X dimension to be very small. The small X dimension then caused the barcode to be very sensitive to a host of printing issues, including gain, modulation and contrast uniformity. The verification grades were failing.
The client re-configured the design file to a single data field form of Datamatrix. Without changing the footprint, the X dimension doubled in size, increasing the ISO tolerances. Now, even with the same amount of linear gain, the verification grades were A’s and B’s.
These are just a few of the obvious barcode quality mistakes made every day. Do you have a barcode quality-related problem or question? We can probably help. Leave a comment or schedule a free 15 minute meeting here.
John helps companies resolve current barcode problems and avoid future barcode problems to stabilize and secure their supply chain and strengthen their trading partner relationships.
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