Resilinc Awarded Patent for Supply-Chain Risk Analytics and Vulnerability Maps
Patent Protects Company’s “Publish Once, Share Often” Data Methodology
The United States Patent and Trademark Office has granted a patent to Resilinc Corporation for its methodology for analyzing supply chain vulnerability metrics. Those metrics include risk score and impact and presenting them via interactive maps and dashboards, and using supplier intelligence collected via a networked solution and platform.
The patent and trademark office recognized Resilinc’s “publish once, share often” data collection methodology where suppliers share part level mapping once and approved customers see the parts they buy without requiring the suppliers to replicate the effort. The patent also covers interactive charts and graphs and vulnerability maps where risk exposure is analyzed and shown and can be further explored and drilled down.
The inventors listed on the patent are Resilinc’s two founders, Bindiya Vakil and Sumit Vakil.
“Resilinc has been innovating in the field of supply chain visibility and risk management since 2010,” said CEO Bindiya Vakil. “We took the approach that the supply chain risk problem is inherently a data problem, and, by making the supplier data collection process efficient, we powered up the world’s largest and most extensive multi-tier mapping and risk analytics platform. We’re excited to have this key patent protecting our unique and highly differentiated methodology.”
Resilinc provides a digital, automated and networked platform to allow customers to collaborate with their direct and sub-tier suppliers. Resilinc’s approval-based mapping solution enables customers and suppliers to exchange supply chain intelligence with each other securely, and accelerates supply chain visibility. Resilinc EventWatch 24×7 solution monitors 30 different risk events in 44 languages and collects impact confirmations in real time from tens of thousands of users during an event, allowing customers to engage and resolve issues efficiently and with precision during disruptions.
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