IATA DG AutoCheck
iPad / Economie et entreprise
Please download this application only if you have a valid DG AutoCheck subscription.
Working in consultation with freight forwarders, ground handling agents and airlines, IATA has developed a product, DG AutoCheck, that automates the verification and acceptance of dangerous goods offered as air cargo. DG AutoCheck helps operational staff at freight forwarders, ground handling agents and airlines to more efficiently process the acceptance of dangerous goods consignments while further improving safety.
DG AutoCheck works by using optical character recognition (OCR) to convert the information on the paper Shipper’s Declaration for Dangerous Goods (DGD) into data, which is then automatically validated against all relevant rules and regulations contained in the IATA Dangerous Goods Regulations (DGR), including all State Variations, Operator Variations and Special Provisions. The IATA DGR is the standard used and trusted by the industry for more than 60 years.
DG AutoCheck can also accept the information required on the Shipper’s Declaration as data in the form of the IATA XML e-DGD (XSDG) where the shipper and airline have agreed to the use of electronic data in place of the paper DGD.
Having verified the integrity and completeness of the DGD, DG AutoCheck then provides the user with a pictorial representation of the package(s) and overpack(s), showing all required marks and labels to support decision making.
Such a solution accelerates the execution of the dangerous goods acceptance check, increases the accuracy, efficiency and oversight as well as generally improve safety. With the launch of the DG AutoCheck product IATA is investing in air transport safety as well as driving efficiency across the supply chain.
Quoi de neuf dans la dernière version ?
This update is primarily necessitated as certain software components are reaching end of life, and will no longer be supported. Equally, it is to pave the way for future enhancements using Artificial Intelligence (AI) and Machine Learning (MI) technologies which requires an "always-online" model.
