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AI-Augmented Manufacturing and Logistics Analytics Review
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence
Abstract
Artificial intelligence (AI) is transforming the way that manufacturing and logistics functions operate, helping companies to gain a competitive advantage from an ever-increasing amount of complex data that is now available in real-time. Even though much research and investment has been made, there is little consistency within and across both of these interrelated areas in synthesizing the empirical evidence regarding AI augmentation. It is a paper that gives an overview of the literature in the form of a narrative empirical research analysis and explores the role of AI in improving the analytics-driven decision making in manufacturing operations and logistics systems. The review is based on peer-reviewed studies published from 2015 to 2024 from the Scopus, Web of Science and Google Scholar databases which were identified as five thematic areas namely, applications of Artificial Intelligence in manufacturing, applications of Artificial Intelligence in logistics and supply chain, analysis frameworks for supporting the augmentation with Artificial Intelligence, human-AI collaboration, and barriers to the implementation of Artificial Intelligence. The results indicate that AI augmentation is consistently found to have a positive effect on the operational performance in both areas, with predictive maintenance, demand forecasting, and warehouse automation being the areas with the most empirical support. The human-AI collaboration is a complex but fruitful model, especially in decisions support applications, whereas limitations in data quality and infrastructure and costs of implementation remain an obstacle to adoption, particularly in small and medium sized enterprises (SMEs) and developing economy contexts. The review helps fill a gap in the literature by summarising the existing evidence on these topics in an integrated thematic synthesis, by highlighting the gaps in the research and by proposing a research pathway focused on novel contexts and methodological approaches. The implications for manufacturing companies, logistics companies, and technology policy makers looking to responsibly and effectively use AI as an augmentation tool are significant.
Keywords
artificial intelligence (ai); manufacturing operations; logistics and supply chain management; ai augmentation; human–ai collaboration; predictive analytics; operational performance.
How to cite this paper
@article{1722500,
author = {Mercy Chinenye Chukwu},
title = {AI-Augmented Manufacturing and Logistics Analytics Review},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {2},
pages = {2791-2797},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1722500.pdf},
abstract = {Artificial intelligence (AI) is transforming the way that manufacturing and logistics functions operate, helping companies to gain a competitive advantage from an ever-increasing amount of complex data that is now available in real-time. Even though much research and investment has been made, there is little consistency within and across both of these interrelated areas in synthesizing the empirical evidence regarding AI augmentation. It is a paper that gives an overview of the literature in the form of a narrative empirical research analysis and explores the role of AI in improving the analytics-driven decision making in manufacturing operations and logistics systems. The review is based on peer-reviewed studies published from 2015 to 2024 from the Scopus, Web of Science and Google Scholar databases which were identified as five thematic areas namely, applications of Artificial Intelligence in manufacturing, applications of Artificial Intelligence in logistics and supply chain, analysis frameworks for supporting the augmentation with Artificial Intelligence, human-AI collaboration, and barriers to the implementation of Artificial Intelligence. The results indicate that AI augmentation is consistently found to have a positive effect on the operational performance in both areas, with predictive maintenance, demand forecasting, and warehouse automation being the areas with the most empirical support. The human-AI collaboration is a complex but fruitful model, especially in decisions support applications, whereas limitations in data quality and infrastructure and costs of implementation remain an obstacle to adoption, particularly in small and medium sized enterprises (SMEs) and developing economy contexts. The review helps fill a gap in the literature by summarising the existing evidence on these topics in an integrated thematic synthesis, by highlighting the gaps in the research and by proposing a research pathway focused on novel contexts and methodological approaches. The implications for manufacturing companies, logistics companies, and technology policy makers looking to responsibly and effectively use AI as an augmentation tool are significant.},
keywords = {artificial intelligence (ai); manufacturing operations; logistics and supply chain management; ai augmentation; human–ai collaboration; predictive analytics; operational performance.},
month = {August},
}