Coforge’s Quasar eBOL is a technology-agnostic, cost-effective solution to digitize hand-written Bills of Lading (BOL). It serves as a front-end to an Intelligent Document Processing (IDP) toolkit that streamlines the now-digitized documents, and allows the application of AI-based tools to help further streamline the flow of paper that underpins the Retail industry.
Industry analysts and the Digital LTL Council estimate 85% of all freight under management is still handled by non-standard paper BOLs. This creates massive problems with shipment visibility, amplifies human errors, and restricts the ability to move freight and bill efficiently. Further, the Digital LTL Council has recently announced an industry-wide definition for electronic BOL (eBOL) which is critical to solving many pervasive issues in this industry.
While a standardized eBOL can provide visibility to shipment location in the supply chain at any point, the unaddressed problem resides in the paper-to-data conversion.
Coforge has launched Quasar eBOL, giving carriers the unparalleled ability to streamline and automate the entire billing process, while reducing errors and rework. Developed using open-source technology, Quasar eBOL is architected for deployment flexibility as an open API solution which works in both online (cloud) and offline (edge) scenarios.
Commenting on the eBOL solution, Sanjay Dalwani, Executive Vice President & Global Head of Transportation, Travel, and Hospitality at Coforge said, “The unstructured, highly diverse paper BOLs have created very manual processes to track, plan, and bill freight handling for decades and one of the toughest problems to solve in the logistics industry. Coforge’s Quasar eBOL is technology-agnostic, doesn’t come with high or open-ended license costs like typical software tools, and will give carriers immense value in automating and dynamically adjusting time critical decisions.”
Vic Gupta, Chief Technology Officer, Coforge, added that “Coforge Quasar is an industry agnostic platform that can be applied to many business functions with high accuracy to extract, ingest, pre-process, cluster, classify, etc. the unstructured data to structured data using modern micro-services architecture.” He added, “The toolkit and accelerator use AI technologies such as Computer Vision, Natural Language Processing (NLP), Natural Language Understanding (NLU), with fully consumable REST API to enable multiple use cases, scenarios and solutions.”
