Improving Supply Chain Visibility with Machine Learning

In this case, practitioners would have data with known inputs and known outputs and use a supervised learning algorithm to determine the relationship between them and extract it to apply toward future planning. This is used for data that is unlabeled, meaning there is some uncertainty surrounding what the data represents. Supply chain practitioners would use this algorithm to find hidden relationships in the data to highlight new patterns. This allows practitioners to take in as much data as possible and have the unsupervised learning algorithm organize it in a more meaningful way. A good example would be attempting to solve a 1,000-piece puzzle where every puzzle piece was colored black.