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The utilization of big data analytics, also known as BDA, is progressively emerging as a pivotal component within the domain of supply chain management (SCM), which is gaining prominence as a significant discipline. The aforementioned remark is based on the notion that BDA is sufficiently adaptable to be employed in a wide array of supply chain management (SCM) procedures. This area encompasses a diverse range of work opportunities, including but not limited to conducting trend analysis, predicting market demand, and assessing consumer behavior. Through the utilization of this literature study, our objectives encompass the classification of predictive big data analytics (BDA) applications within the realm of supply chain demand forecasting. Additionally, we aim to discern any existing research deficiencies and provide potential avenues for future exploration. In the challenging economic situation of today, corporations have adopted a wide array of targeted marketing methods in order to retain or extend their profit margin while simultaneously being one step ahead of their competitors. The utilization of forecasting models constitutes a substantial element within the realm of precision marketing, with the objective of enhancing comprehension and fulfillment of consumer needs. The assessment of consumer purchase habits and preferences, utilizing data obtained from consumers and transaction records, is gaining heightened attention and emphasis due to this prevailing tendency. The purpose of this practice is to ensure optimal management of product supply chains (SC).

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Identifying the Primary Factors of Big Data Analytics in Improving the Supply Chain Management Process for Sustainable Development by Utilizing a Machine Learning Methodology

  • 会议论文

  • 发表日期：

    2025年

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  • K. Srujan Raju,
  • A. Mahendar,
  • Borra Sivaiah,
  • D. S. Sanjeev,
  • R. Reeja Igneshia Malar,
  • Sumera Jabeen

摘要

The utilization of big data analytics, also known as BDA, is progressively emerging as a pivotal component within the domain of supply chain management (SCM), which is gaining prominence as a significant discipline. The aforementioned remark is based on the notion that BDA is sufficiently adaptable to be employed in a wide array of supply chain management (SCM) procedures. This area encompasses a diverse range of work opportunities, including but not limited to conducting trend analysis, predicting market demand, and assessing consumer behavior. Through the utilization of this literature study, our objectives encompass the classification of predictive big data analytics (BDA) applications within the realm of supply chain demand forecasting. Additionally, we aim to discern any existing research deficiencies and provide potential avenues for future exploration. In the challenging economic situation of today, corporations have adopted a wide array of targeted marketing methods in order to retain or extend their profit margin while simultaneously being one step ahead of their competitors. The utilization of forecasting models constitutes a substantial element within the realm of precision marketing, with the objective of enhancing comprehension and fulfillment of consumer needs. The assessment of consumer purchase habits and preferences, utilizing data obtained from consumers and transaction records, is gaining heightened attention and emphasis due to this prevailing tendency. The purpose of this practice is to ensure optimal management of product supply chains (SC).