'While we began as a humble AI-driven bidding platform, customers are now using our PPO platform to influence purchasing, contracting, tail spend, renewals and even p-cards. 'Our customers told us we should consider changing the company's name to reflect our expanded set of capabilities,' said Edmund Zagorin, Founder & CEO of Arkestro. Case studies and speakers at Optimal '22 point to benefits in Indirect Services, CapEx, Direct Materials, Logistics, Packaging and Materials and Repair and Operations (MRO) spend categories.

The Arkestro platform is ideal for any enterprise that experiences data quality issues in procurement operations, especially related to fast-moving, complex or constrained buying channels. Perhaps most significantly, non-procurement users and suppliers never need to login or navigate an 'app.' Arkestro is also announcing a set of out-of-the-box integrations with Coupa, SAP Ariba, Zycus and Jaggaer as well as SAP S4 HANA, Oracle Cloud, Infor and Microsoft Dynamics. Using a proprietary combination of behavioral science, game theory and predictive machine learning, procurement teams use Arkestro to deliver a 2-5x lift on cost savings from everyday procurement and sourcing cycles.Īrkestro runs on autopilot in the background in customers' existing systems. The Arkestro name change comes with an expansion of the company's value proposition, moving beyond predictive sourcing, contracting and bidding to influence spend within everyday purchasing activities, including tail spend. The new name highlights Arkestro's capability to embed preferred outcomes and instant feedback in any process across procurement, sourcing and supplier management. NEW YORK, Ma/PRNewswire/ - Today at its Optimal '22 conference, Bid Ops, the leading Predictive Procurement Orchestration platform, announced that it is changing its name to Arkestro.

Name Change Emphasizes Capabilities to 'Orchestrate' and Embed Preferred Outcomes and Instant Feedback in Any Process, Helping Customers Leverage Behavioral Science, Game Theory and Predictive Machine Learning in Procurement Operations

Jaggaer vs ariba