PhD Studentship in Supply Chain Analytics at the University of Strathclyde
Big data exist for supply chains within, for example, enterprise resource planning systems. The data sets are large and complex and include variables linked to, for example, supplier delivery and quality performance, for which observations are generated dynamically through time. Such data can be used to estimate and predict events, such as late delivery of orders or nonconforming parts. Understanding and anticipating the risk of supply chain events allows managers to identify mitigation or control actions to minimise operational disruption.
Computation of key supply risk performance measures can be complicated because we need to appropriately measure the exposure of a supplier to risk. Ongoing work by the supervisor team has developed theoretical methods for estimating, ranking and predicting operational supply risk events. However these methods have been developed under a particular set of stationary assumptions about the probability model which generates the observed data and so are relevant for mature suppliers. There is a need to examine models for new and innovative suppliers. Further, there has been little development of algorithmic rules for optimal data preparation to, for example, determine relevant exposure to risk and this is required because it allows the inference methods to be integrated with the supply chain data management process. This PhD project aims to address these challenges which are important if we are to provide a robust suite of methods that can form a useful decision support tool for operational supply risk management.
For details please visit the following link:
www.strath.ac.uk/studywithus/scholarships/strathclydebusinessschoolscholarships/managementsciencescholarships/reascholarshipsupplychainriskanalysis/
or contact either of:
Prof. John Quigley ([log in to unmask]) or Prof Lesley Walls ([log in to unmask])
Department of Management Science
University of Strathclyde
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