Companies are investing heavily in automation, but the information feeding those systems is not always ready for the job.
A GS1 US study released November 3 found that nearly 60% of surveyed organizations have automated most of their supply-chain processes or adopted artificial intelligence and machine learning. Only 38% reported having centralized systems that collect and integrate data in real time. Another 32% still combine information from separate sources manually.
Those gaps create problems wherever digital records must be connected to physical products. Variable-data printing is one of the places where the consequences become visible.
An inkjet system can print exactly what it receives. If the production file contains the wrong GTIN, serial number, lot code, expiration date or shipping information, the system may produce a sharp, scannable code that identifies the wrong product. Faster equipment only allows the error to spread through the run more quickly.
A dependable workflow starts with control over the source data. GS1 US recommends assigning clear ownership of product information, following standardized identification rules and auditing digital records against finished physical products. Without that control, multiple departments may maintain separate versions of the same information. A production team can receive an outdated record even though a correct version exists elsewhere in the company.
The next risk appears during job setup. Fields from an enterprise resource planning system, warehouse system or customer database must be mapped correctly to the print layout. A serial-number field can be shifted into the wrong position. Leading zeros can disappear during a file conversion. Dates may arrive in a format that does not match the barcode specification. Duplicate records can also enter the queue when files are combined manually.
These are data-preparation failures, not print-quality failures.
Reconciliation provides another layer of control. The number of records received, printed, rejected and completed should agree at the end of the run. If the file contains 100,000 unique serial numbers but production reports 99,998 accepted pieces, the missing records need to be identified before the job is released. Reconciliation also helps prevent the same serial number from being printed twice.
Inline inspection addresses what happens on the press. A camera or scanner can confirm that a code is present and readable, then compare its contents with the expected production record. This can catch missing codes, duplicates, incorrect sequences and mismatches between printed text and encoded data.
Barcode verification serves a related but different purpose. A standards-compliant verifier grades physical characteristics such as contrast and symbol construction. That testing can determine whether a barcode is likely to scan reliably. It does not automatically know whether the encoded GTIN or serial number belongs to the product underneath it.
That distinction matters. Print quality answers whether the code can be read. Data validation answers whether the code says the right thing.
GS1 US found that 62% of surveyed organizations consider fragmented data a major barrier to innovation. For variable-data production, the answer is not simply more automation. Companies need controlled source records, accurate job configuration, production reconciliation and inline inspection working together. When one part is missing, a technically successful print run can still create a serious supply-chain problem.