Contact Shehu Sani
Background
Shehu Sani obtained his MSc in Mechanical Engineering with a major in Energy studies from the University of Agriculture Makurdi. Before joining Cranfield University he completed his thesis on modelling and simulation of thermo-syphon solar water eater using transient system simulation. He then moved to University of Liverpool as a PhD research student, where he undertook his research within the Systems Realisation Laboratory. He has now moved to Cranfield University to continue his research at the School of Aerospace, Transport and Manufacturing. His research focuses on developing strategies foe achieving pre-emptive resilience in military supply chains.
Research opportunities
My research interest lies in enhancing the resilience of supply chains through the application of data-driven approaches within the dynamic framework of Industry 4.0. In an era characterized by rapid technological advancements, the integration of cutting-edge technologies such as IoT, artificial intelligence, and big data analytics offers unprecedented opportunities to fortify supply chain networks. My focus revolves around developing innovative methodologies that leverage real-time data to proactively identify vulnerabilities, predict disruptions, and implement adaptive strategies to mitigate risks. By harnessing the power of Industry 4.0 technologies, I aim to contribute to the creation of agile and robust supply chain systems capable of navigating the complexities of the modern business landscape.
Current activities
Shehu is carrying out his PhD research within Cranfield University, within School of Aerospace, Transport and Manufacturing. His PhD research focuses on how to develop strategies s for achieving pre-emptive resilience in military supply chains.
Publications
Articles In Journals
- Sani Mohammed S, Schaefer D & Milisavljevic-Syed J. (2023). Towards pre-emptive resilience in military supply chains: A compromise decision support model-based approach. Production & Manufacturing Research, 11(1)
- Sani S, Xia H, Milisavljevic-Syed J & Salonitis K. (2023). Supply Chain 4.0: A Machine Learning-Based Bayesian-Optimized LightGBM Model for Predicting Supply Chain Risk. Machines, 11(9)