AI-Augmented Decision Support System for Sustainable Transportation and Supply Chain Management: A Review

Abstract

This study is based on a critical review of the use of Artificial Intelligence (AI) in the context of Decision Support System (DSS) in enhancing sustainability in transportation and supply chain management. It sheds some light on the inefficiencies of the traditional DSS to process the demands of dynamic and data-intensive environments as well as the transformational capabilities of AI technologies such as machine learning, deep learning, natural language processing, and reinforcement learning to the need of decision-making processes. DSS that are AI-enhanced can make predictions and prescriptions, real-time flexibility, and enhanced performance, translating to efficient routing, enhanced demand modeling, fuel efficiency, and lower environmental harm. The paper examines the practical applications in the area of logistics and transportation giving examples of successful applications by major international organizations. It also mentions some of the most crucial issues, which are data privacy, transparency of algorithms, integration with legacy systems, and ethics. The study concludes that AI-enhanced DSS can contribute considerably to moving towards sustainable, adaptive, resilient transport and supply chains implementable in a favourable regulatory and organizational environment. The future involvement should be on how we can mitigate these hindrances and lay guidelines on how we can ethically and sustainably use AI.