Integration of Emerging Technologies AI and ML into Strategic Supply Chain Planning Processes to Enhance Decision-Making and Agility

Jayapal Reddy Vummadi () and Krishna Chaitanya Raja Hajarath ()

International Journal of Supply Chain Management, 2024, vol. 9, issue 2, 77 - 87

Abstract: Purpose: The aim of this research was to discuss the use of artificial intelligence (AI), machine learning (ML), and big data analytics as fundamental pillars of strategic supply chain management, for better decision-making, more precise forecasting, and higher supply chain agility. Methodology: The paper reviewed existing literature and industry reports to get an in-depth insight into the modern supply chain planning environment, the problems that it faces, and the efficiency of traditional techniques. It then analyzed the opportunities of utilization of AI, ML and big data analytics as well as the certain technologies or techniques that could be utilized, such as the predictive/prescriptive analytics, digital twins and blockchain. Findings: The study concluded that the traditional supply chain planning processes are becoming more and more out of style and inefficient, taking into account the business environment that are constantly changing, global supply chains, and technological advancements. It emphasized the risks to long-term performance associated to relying too much on the past practices and a call for action for progressive modernization of supply chain planning mechanisms. Unique Contribution to Theory, Practice and Policy: The report pointed to innovative ways such as AI, ML, and big data analytics for the integration into the supply chain operations for increasing the productivity, resilience and competitiveness. Moreover, it promoted the increase of budgeting on the talent side in order to obtain an appropriate use of technology and to explore new paths in the market.

Keywords: Supply Chain Planning; Artificial Intelligence; Machine Learning; Big Data Analytics; Decision-Making; Forecasting Accuracy; Supply Chain Agility; Emerging Technologies; Digital Transformation (search for similar items in EconPapers)
Date: 2024
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