Applications Of Artificial Intelligence In Supply Chain Management In The Era Of Industry 4.0

Abstract

Nowadays, an emerging trend in Supply Chain Management (SCM) is a focus shift from classical Supply Chain (SC) to digital SC. However, decisions in the digital SC context require new tools and methodologies that consider the digitalization environment. Artificial Intelligence (AI) methodologies can provide learning, predictive, and automated decision-making capabilities in the digital environment. Among a wide range of problems in the SCM field, risk management, logistics, and transportation have received less attention from an AI perspective. The work presented in this dissertation proposes three AI-based approaches to help SCs manage their operations more effectively using creative risk monitoring and logistics/transportation solutions in the era of Industry 4.0. In the first study, a Digital Twin (DT) framework for analyzing and predicting the impact of COVID-19 disruptions on the manufacturing SC is developed to support the decision-making process in disrupted SC. The proposed Digital SC Twin (DSCT) model is aimed to work as an online controlling tower to monitor the behavior of physical SC in the digital environment and guide SCM managers to make the necessary adjustments to minimize risks and maintain SC stability during disruptions. In the second study, a contactless truck-drone delivery model for last-mile problems in the SC is introduced to support logistics and transportation operations during pandemics. A hybrid AI approach is developed to provide quality real-time solutions for the introduced truck-drone delivery system. In the third study, a collaborative Multi-Agent Deep Reinforcement Learning (MADRL) approach for vehicle routing in the SCM is designed to facilitate collaboration and communication among multiple vehicles in the SC distribution networks. Overall, the methods and models presented in this dissertation can enable SCs to transform their traditional practices, provide cost savings, support real-time decision-making, and enable self-optimization and self-healing capabilities in the age of Industry 4.0