AI AND MACHINE LEARNING IN SUPPLY CHAIN OPTIMIZATION: TRENDS, CHALLENGES AND FUTURE DIRECTIONS

Paper ID: EIJTEM_2019_6_1_22-32

Author's Name: Naga Bharadwaj Bhavikatta

Volume: 6

Issue: 1

Year: 2019

Page No: 22-32

Abstract:

Traditional optimization paradigms have changed as a result of supply chain management's (SCM) use of artificial intelligence (AI) and machine learning (ML) technology, allowing for more intelligent, data-driven, and flexible decision-making. This study offers a thorough analysis of supply chain optimization applications of AI and ML, with an emphasis on advancements until the end of 2019. It examines the core technological advancements, key functional areas impacted, and sector-specific case studies to highlight emerging adoption patterns. The study also addresses the critical challenges associated with implementation, such as data quality, model interpretability, and organizational readiness. Furthermore, it identifies research gaps and outlines future directions to foster the next generation of smart, autonomous supply chains. By consolidating insights from academic literature and industry practice, this paper offers a foundational understanding of how AI and ML are reshaping supply chains and provides actionable guidance for stakeholders aiming to leverage these technologies strategically.

Keywords: AI, ML, Supply Chain Optimization, Smart Logistics, Digital Supply Chain, Supply Chain Risk Management, Predictive Analytics

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