Consus Fresh Launch Innovative AI based Label Verification Solution
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Consus Fresh Solutions , part of The Affinitus Group ; providing market leading business management software solutions for the food and horticulture industries , have announced the launch of a highly innovative Artificial Intelligence ( AI ) based label verification and quality control ( QC ) solution for the fresh produce pack-house . The new solution could significantly reduce packhouse QC labour costs and boost operative productivity , whilst reducing Product Withdrawal Fines and associated costs due to labelling errors .
Label Verification is a time and labourintensive process that is required at multiple points during the fresh produce packing process . A fresh produce packhouse QC operative completes an average of 200 label checks per day , using pen and paper data collection
8 FDPP - www . fdpp . co . uk methods , at approximately 2 minutes per label check . Repetition and human fatigue introduce a high risk of “ label blindness ”, causing costly label errors to be missed and miss-labelled product reaching the supermarket shelves . Each un-detected error results in hefty fines , product re-work costs , loss of reputation and sometimes loss of contracts , not to mention the food safety risks from undeclared ingredients or allergens .
The highly innovative AI-based Consus Label Verification solution takes approximately 4 seconds to perform each label check . The solution uses Microsoft Azure cloud-based Artificial Intelligence to process each label photograph and returns an immediate pass or fail result . AI never experiences fatigue or loss of productivity , performing every check with the same speed and accuracy , whilst saving time and money .
A failure result will automatically halt production and issue automated alerts to relevant production staff with details of the failure . Similarly , a pass result can automatically trigger any “ positive release ” processes and progression to dispatch .
The solution can check any labelling information against a centrally stored repository of client and product specific labelling requirements , including , country of origin , picked from location , product description , best before date , display until date , product class , destination customer , special offers and allergen advice .