Predicting Supplier Delivery Quality with Machine Learning

Supplier disruptions are often visible in operational data before they become obvious to planners, but turning those signals into a useful decision tool requires more than training a model.

For my master’s thesis, I translated stakeholder needs from cross-functional workshops into a supplier-risk use case, analyzed data from an industrial data lake and developed an end-to-end Python and machine-learning solution. I also built a web dashboard to make the results accessible for management and supplier-management workflows.

The solution identified approximately 25% of subsequent supplier risks up to five days in advance, giving supplier managers an earlier window to act before a disruption reached the shop floor.