Lead DIGIT Lab Academics: Professor Roger Maull and Maximilian Stout (PhD student)

Challenge and Methodology

This collaborative PhD project between the University of Exeter and Vendigital addresses the scarcity of case studies in AI-driven Predictive Analytics for supplier disruption. The focus is on creating Machine Learning models with client data to predict the risk of disruptions occurring.

Progress

Data analysis for the paper “Predicting Supply Chain Disruptions in the Aerospace Market” is ongoing. Key questions include:

• Can disruption be predicted based on Product Category?
• Does greater product complexity increase disruption risk?

The evolving model will include variables like Geographic Distance, Order Quantity, Price Fluctuations, Material Category, and Manufacturing Method.

Outcomes

The goal is to apply Machine Learning to forecast the likelihood of supplier disruption. By quantifying the impact of variables using Spend Analytics and Bill of Materials data, businesses can proactively protect their supply chains by identifying at-risk suppliers and implementing targeted resilience measures.