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.

Next Generation Machine Learning for Digital Supply Chain Planning





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.

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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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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