Integrating AI into Logistics Audit

Abstract

The longitudinal analysis undertaken in this article aims to carefully scrutinize the deployment of full traceability within a specific supply chain. The main objective is to discern the determining elements that influenced this implementation and to detail the evolution of the traceability system over time within this particular supply chain. This analytical approach is intrinsically linked to the contemporary adoption of Artificial Intelligence (AI) as a central tool.

However, at the heart of this exploration lies a crucial question: how does the integration of AI within the framework of total traceability impact the performance and resilience of the supply chain? In other words, what are the practical implications of using AI on data management, anomaly detection, and decision-making within this specific supply chain?

The integration of AI in the context of full traceability offers substantial benefits for various aspects of the supply chain, from the initial data collection phase to real-time monitoring. The article will focus on an in-depth exploration of the specific contributions of AI in this context. This encompasses its remarkable ability to quickly and efficiently analyze large quantities of data, anticipate potential anomalies, and facilitate informed decision-making throughout the traceability process.

Keywords: Audit, supply chain, Artificial Intelligence

JEL Classification: M 42

Paper type: Theoretical Research