AI-Powered Supply Chain Business Intelligence for Procurement, Operations Teams

The new Business Intelligence capability provides procurement and operations teams, from senior management to frontline workers, with data-driven insights in the form of actionable and interactive reports and dashboards.

Zinetro N Adobe Stock 328958904
ZinetroN AdobeStock_328958904

Verusen launched an artificial intelligence (AI)-powered supply chain business intelligence capability to its Trusted Material application for procurement and operations teams.

“In 2023, supply chains continue their transition past the pandemic world, embracing advanced technologies,” says Paul Noble, CEO and founder of Verusen. “However, data is still siloed, and collaboration between operations and procurement teams is challenged by limited visibility, data staleness, and manual efforts to collect and share data. Working in disparate silos, procurement and operations teams are frequently in conflict, with traditionally misaligned goals limiting execution at scale. We’re thrilled to be able to empower these groups with visual and actionable reporting and analytics to drive alignment and add a new level of collaboration and strategic decision-making. Having these kinds of insights is critical to advancing business. Companies can now collaborate seamlessly, efficiently, and effectively across their enterprise supply network.”

 

From GlobeNewswire:

 

  • The new Business Intelligence capability provides procurement and operations teams, from senior management to frontline workers, with data-driven insights in the form of actionable and interactive reports and dashboards.
  • Users gain increased clarity and materials visibility to view, filter, export and share reports.
  • Manufacturers can leverage Verusen’s platform to implement better decision-making for streamlined and strategic material management. They can interact, filter and customize material reports to drive improved business outcomes, including inventory and risk optimization and tail spend analysis, without the need for data cleanses. 
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