Department of Engineering / Research / Strategic Themes / Uncertainty Risk and Resilience / Projects / Predicting Supply Chain Failures with Big Data

Department of Engineering

Predicting Supply Chain Failures with Big Data

Predicting Supply Chain Failures with Big Data

Principal Investigator: Professor D McFarlane

Manufacturers face significant challenges with suppliers supporting manufacturing operations: if a supplier does not deliver an assembly or component on time and at the right quality, then the impact to production can be significant and costly.  Many manufacturers deal with hundreds of shortages per day for a variety of reasons such as being overloaded by orders from other customers, day-to-day delays during transportation of goods, and suppliers not being able to satisfy manufacturing demand.  The Virtual Intelligent Production, Procurement and Prediction (VIPr) project, working with the Boeing Company, aims to address these issues by developing a system utilising data analytics to make predictive interpretations about potential supplier operational disruptions.