Machine Learning is the new Arms-Race for Supply Chain Planning
Supply Chain Planning involves identifying and resolving future demand and supply balancing problems equipped with large amount of historic and real-time data, of which only some is of a “decision grade” quality. The data is ever increasing in volume and complexity. There are key trends to identify. There are predictions to make. There are tasks to automate. There are outliers to examine. There are exceptions to initiate action. There is no end to the type of critical questions in supply chain planning. How much will I sell? How much effective capacity will I have? How long will it take to deliver? What will be the results of a new product launch? The importance of a timely and accurate response is critical to business continuity. This is where Machine Learning comes to play.
Application of Machine Learning in Supply Chain Planning
There are many natural applications for both supervised and unsupervised Machine Learning adoption within the supply chain planning spectrum.
- Cluster analysis: the application of unsupervised machine learning concepts to Product Clustering significantly improves the planning accuracy and simplicity across large product portfolios.
- New product launch: Machine Learning concepts can be used to predict the performance of a product launch. It identifies correlation and causation which subsequently predicts a sales volume and a sales profile that determines the demand for the initial pipe-fill and the honey-moon 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 look at trends (growth or decline, peaks and troughs) of individual components, it also considers time-phased inter-product relationships.
- And many more...
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