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September 12, 20250 citations

Optimizing Supply Chain Performance Using AI and Machine Learning: A Predictive Analytics Approach

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GBGaurav BatraABArun Batra

Key Points

  • AI and ML significantly enhance supply chain management, improving demand forecasts and operational efficiency.
  • Data quality and system compatibility pose challenges in implementing AI-driven solutions in supply chains.
  • Predictive analytics can reduce logistics costs, offering a competitive advantage in various industries.
  • Small and large businesses alike can benefit from AI and ML, emphasizing the importance of strategic planning in implementation.

Abstract

The paper presents the manner in which AI and ML have reshaped supply chain management (SCM) by making demand predictions, controlling inventory, reducing logistics costs, and controlling risks. It points out the opportunities of predictive analytics in enhancing the performance of supply chains in different industries. The paper analyses cases to demonstrate the efficiency benefits that AI/ML can bring as well as discuss some of the challenges, like those of quality and scalability of data and scalability and compatibility of systems. Although the process of implementing AI/ML can make the operations more efficient, it is expensive and necessitates clear planning and technological and people resources. The paper established that AI and ML have significant potential to provide businesses with a competitive advantage in the new global economy.

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Cite This Study

Batra et al. (2025) studied this question.

synapsesocial.com/papers/68d44a4731b076d99fa53e7fhttps://doi.org/10.71143/n5d6fr98
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