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Kinaxis|30 May 2019

Machine Learning

When planning assumptions don’t reflect the reality of what’s actually happening in your supply chain, metrics like revenue-at-risk, customer satisfaction and inventory costs suffer.

When planning assumptions don’t reflect the reality of what’s actually happening in your supply chain, metrics like revenue-at-risk, customer satisfaction and inventory costs suffer.

Kinaxis offers purpose-built machine learning capabilities to automatically close the gap between the results you expect and the results you realise.

Supply chains are complex and full of inter-dependencies. A problem in one area can wreak widespread havoc on others. The Self-Healing Supply Chain™ changes all of that.

Built using advanced machine learning algorithms, the Self-Healing Supply Chain delivers value in a practical way by bridging the gap between supply chain planning and execution, automatically closing the gap between design and actual results.

The Self-Healing Supply Chain detects high-impact exceptions between designed and actual performance, giving you the visibility you need to spot potential issues and take corrective action before they impact planning performance.

By measuring the impact of incorrect design assumptions on critical business metrics and automatically correcting inconsistencies based on customizable tolerances, the Self-Healing Supply Chain continuously monitors and adjusts design inputs over time for on-going value.

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