Kinaxis®, a global leader in end-to-end supply chain orchestration, has announced a strategic partnership with Databricks, a leading data and AI company, to transform supply chain decision-making. By integrating Kinaxis’s AI-native platform, Maestro™, with Databricks’ Data Intelligence Platform, the collaboration aims to unify data, accelerate AI adoption, and enable organizations to respond to disruptions with greater agility.
“Global supply chain organizations are dealing with more data than ever, but it’s often fragmented across systems, making it difficult to act quickly or consistently,” said Andrew Bell, Chief Product Officer at Kinaxis. “This partnership strengthens the data fabric that powers Maestro™, accelerating our AI innovation agenda and enabling faster, smarter decisions at scale. Together, we’re giving businesses the ability to orchestrate their supply chains with the intelligence and speed today’s disruptions demand.”
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Maestro™ is designed to optimize supply chain orchestration, embedding domain-specific intelligence across planning, execution, and decision-making processes. Integrating Databricks’ Data Intelligence Platform enhances Maestro’s data infrastructure, allowing for seamless, secure, cross-platform data sharing through Delta Sharing. This advancement enables companies to break down data silos, eliminate duplication, and deploy predictive, generative, and autonomous AI at scale.
“Supply chain data is among the most complex and operationally critical in the enterprise, but it is critical to help organizations build data intelligence,” said Shiv Trisal, Global Manufacturing, Transportation & Energy GTM Leader at Databricks. “By partnering with Kinaxis, we’re helping companies centralize data and shift from fragmented insights to real-time, intelligent actions that power their enterprise AI strategy.”
This collaboration reinforces both companies’ commitment to driving supply chain innovation, equipping businesses with the necessary tools to navigate today’s rapidly evolving market conditions.