Artificial Intelligence in Predicting Automotive Supply Chain Disruptions: A Literature Review

EasyChair Preprint 15988

Abstract

The automotive industry plays a crucial role as an indicator of the economic development of the world economy, and now it is experiencing ever greater difficulties in supply chain disruptions caused by geopolitical factors, various kinds of catastrophes, fluctuations, and uncertainties. Supply chain-based forecasting using artificial intelligence (AI) holds the promise to overcome these issues. This paper presents a systematic literature review of the most advanced AI approaches used in predicting supply chain disruption specific to the automotive industry. This paper discusses the fields of machine learning, deep learning, and a combination of both models used for predictions and predict and mitigate supply chain disruptions. Based on this, key implementation challenges, ethical factors, and the trends for further research have been identified using case studies and the latest developments to support improving the resilience and adaptability of automotive supply chains. This research proposal should fill the gaps between academicians’ work and practitioners’ real-world implementation initiatives in one of the most competitive and unpredictable sectors with solid theoretical knowledge and best practices.

Keyphrases: Artificial Intelligence, Disruption Management, Predictive Analytics, Supply Chain Forecasting, automotive industry

@booklet{EasyChair:15988,
  author    = {Ikhlef Jebbor and Hanaa Hachmi and Zoubida Benmamoun},
  title     = {Artificial Intelligence in Predicting Automotive Supply Chain Disruptions: A Literature Review},
  howpublished = {EasyChair Preprint 15988},
  year      = {EasyChair, 2025}}