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Machine Learning for Supply Chain Planning

The adoption of machine learning in enterprise technology is becoming the next arms-race for many vendors. Supply Chain Planning technology is currently experiencing a great leap forward along these lines. The point of no return has been reached.




This is an exciting time with enormous benefits to be realized. Many companies sit on a gold mine of data, and they now have an opportunity to realize its value. The only bad machine learning plan is no plan at all. Machine learning is now a crucial part of a comprehensive digital transformation strategy.

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Next Generation Machine Learning for Supply Chain Planning

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There are many natural applications for both supervised and unsupervised machine learning adoption within the supply chain planning space.

  • Cluster analysis: The application of unsupervised machine learning concepts to product clustering significantly improves planning accuracy and efficiency across large product portfolios.
  • New product launch: Machine learning concepts can be used to predict the performance of a product launch. They identify correlation and causation for forecasting sales volumes ,and a sales profile to determine demand for the initial pipe-fill and the honeymoon period.
  • Make-to-order component planning: Machine learning not only predicts the future demand of the MTO/CTO item but it can predict the corresponding BOM. It not only looks at trends (growth or decline, peaks and troughs) for individual components, but it also considers time-phased inter-product relationships.

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The Advent of Machine Learning for Supply Chain Planning

Shaun Phillips is an expert in Machine Learning technologies and is the Global Product Manager at QAD DynaSys.

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Machine Learning White Paper

AI can be a super-cool topic when discussing friendly robots, drones, and driverless cars. However it’s applicability in enterprise technology, and especially in supply chain planning is less alluring, albeit equally as valuable.

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Machine Learning Resources

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