Articlediamond2024

LEVERAGING ARTIFICIAL INTELLIGENCE FOR ENHANCED SUPPLY CHAIN OPTIMIZATION: A COMPREHENSIVE REVIEW OF CURRENT PRACTICES AND FUTURE POTENTIALS

Olorunyomi Stephen Joel · Adedoyin Tolulope Oyewole · Olusegun Gbenga Odunaiya · Oluwatobi Timothy Soyombo

104 citations0/0 files storedInternational Journal of Management & Entrepreneurship Research

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Impact of AI and Big Data on Business and Society Supply Chain and Inventory Management Big Data and Business Intelligence

Abstract

The integration of artificial intelligence (AI) technologies into supply chain management has emerged as a crucial avenue for enhancing efficiency, agility, and responsiveness in modern business operations. This comprehensive review synthesizes current practices and future potentials of leveraging AI for supply chain optimization. Beginning with an overview of traditional supply chain management challenges, the review elucidates how AI solutions address these complexities by enabling predictive analytics, real-time visibility, and intelligent decision-making. The review delves into the diverse applications of AI across different stages of the supply chain, including demand forecasting, inventory management, logistics optimization, and supplier relationship management. Examples of AI-driven technologies such as machine learning, natural language processing, and robotic process automation are analyzed for their role in revolutionizing supply chain operations. Furthermore, the review highlights the transformative impact of AI on supply chain resilience, emphasizing its ability to mitigate disruptions, adapt to dynamic market conditions, and optimize resource allocation. The review also addresses critical considerations such as data privacy, ethical implications, and organizational readiness for AI adoption within supply chain contexts. Lastly, the review discusses future research directions and potential advancements in AI-enabled supply chain management, envisioning intelligent autonomous supply chains characterized by self-learning systems, collaborative ecosystems, and enhanced sustainability practices. In conclusion, this review underscores the pivotal role of AI in driving continuous innovation and competitive advantage within supply chain networks, while also emphasizing the importance of strategic planning and responsible implementation to harness its full potential. Keywords: AI, Supply Chain, Optimization, Practices, Review.

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References

2020A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0 2021Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: an empirical investigation 2023Artificial intelligence in logistics and supply chain management: A primer and roadmap for research 2022Artificial intelligence and big data analytics for supply chain resilience: a systematic literature review 2022Risks and Global Supply Chains: What We Know and What We Need to Know 2023Predictive Analytics and Machine Learning for Real-Time Supply Chain Risk Mitigation and Agility 2021Prerequisites for the adoption of AI technologies in manufacturing – Evidence from a worldwide sample of manufacturing companies 2020Machine learning demand forecasting and supply chain performance

Cited by

2025SustAI-SCM: Intelligent Supply Chain Process Automation with Agentic AI for Sustainability and Cost Efficiency 2024Artificial intelligence applications for information management in sustainable supply chain management: A systematic review and future research agenda 2024Leveraging AI for sustainable accounting: Developing models for environmental impact assessment and reporting

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