Machine learning techniques applied to supply chain dynamics

Nowadays, supply chains are strongly affected by many sources of disruptions, which in turns may trigger a detrimental effect on social-economic aspects of countries. A leading contribution for improving the responsiveness of supply chains to disruptions might come from machine learning techniques. The objective of the thesis would consists in implementing artificial intelligence techniques to predict or to promptly react to sudden changes in the distribution network. As further source of uncertainty, changeovers in the production plant feeding the downstream supply chain will be also considered.