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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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DynaSys - Machine Learning - Main Content
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.
DynaSys - Machine Learning - White Paper
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.
DynaSys - Machine Learning - Resources
Machine Learning Resources
A Blog article by Florian Loinard.
A Blog article by Brent Dawkins.
A Blog article by Diadie Sow.
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