Kinaxis Advances Large-Scale Supply Chain Optimization with NVIDIA AI
Achieves up to 12X faster end-to-end planning performance in large-scale enterprise models
As supply chains grow in scale and complexity, planning models must reconcile tens of millions of variables across extended time horizons and multiple planning levels. As model size expands, the number of potential decisions can scale into billions, dramatically increasing computational needs. Organizations are no longer constrained by insight alone. They are constrained by how quickly they can iterate.
In testing on a large-scale semiconductor planning model with nearly 50 million decision variables,
This shift enables organizations to move from long-running batch processes to interactive scenario iteration, reinforcing Kinaxis’ concurrent supply chain orchestration approach and allowing planners to evaluate more alternatives within operational decision windows.
“This milestone demonstrates how accelerated computing can change the way large-scale planning problems are solved,” said
Accelerated optimization also supports Kinaxis’ broader agent-driven orchestration strategy. Agent-based workflows rely on rapid scenario iteration and trigger multiple optimization runs as they evaluate alternatives. By reducing solve times, GPU acceleration expands the number of scenarios agents can assess within practical decision windows, strengthening the foundation for more responsive and adaptive supply chain decisions.
Key advancements include:
- Up to 12X faster solve times for a semiconductor wafer planning use case
- GPU-accelerated optimization using NVIDIA cuOpt
- Execution on NVIDIA AI infrastructure to support large-scale industrial models
- Integrated within the Maestro platform, connecting data, people, and decisions across the global supply chain
“The increasing complexity of global supply chains demands a fundamental shift to accelerated decision-making,” said
About
Forward-Looking Statements
This news release contains forward-looking statements within the meaning of applicable securities laws. Forward-looking statements include, but are not limited to, statements regarding the anticipated benefits and performance improvements associated with GPU-accelerated optimization, the integration and expansion of NVIDIA cuOpt and NVIDIA GPUs within the Maestro platform, Kinaxis’ agent-driven orchestration strategy, and future innovation initiatives. Such statements are based on management’s current expectations and assumptions and are subject to risks and uncertainties that could cause actual results to differ materially from those expressed or implied.
Risks and uncertainties include, among others, factors related to technological development and integration, customer adoption, reliance on third-party technologies, competitive offerings, and other risks described in Kinaxis’ most recently filed Annual Information Form and Management’s Discussion and Analysis available on SEDAR+ at www.sedarplus.ca. Forward-looking statements are made as of the date of this release and
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