Sustainable Supply Chain Resilience Through Predictive AI-Based Logistics Planning

Abstract

The concept of supply chain resilience has become a burning subject of planning in contemporary logistics, particularly when it comes to environmental factors like natural disasters, pandemics, and geopolitical factors. The conventional approaches to logistics planning do not take into consideration dynamic shifts in the supply chain environment. This paper will discuss the potential of Predictive AI in improving supply chain resilience through the use of smarter and data-driven logistics decisions. We recommend a structure on how AI models can be used to forecast the changes in the demand, detect risks, and optimize the route planning. Use of machine learning algorithms, including regression model, decision trees and reinforcement learning are reviewed. Findings indicate that predictive AI models can contribute greatly to quality decision-making, minimize the operational risks, and improve resilience by offering real-time information about possible disruptions. The paper ends with a conclusion and recommendations to enable a more sustainable and resilient supply chain, which involves the inclusion of AI-powered predictive models with the current logistics management systems.