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Effective management of pharmaceutical stocks is significant for guaranteeing the accessibility of basic drugs while minimizing wastage and costs. This paper points to improving pharmaceutical stock estimating by leveraging deep learning-based request expectation models. Traditional stock management procedures frequently fail to capture the complexities and varieties in pharmaceutical requests, driving stock outs or overloading. Progresses in machine learning, especially deep learning, give unused openings to make strides in determining exactness. Precise request forecasts are crucial for optimizing stock levels, reducing costs, and ensuring opportune accessibility of medicines. This extension addresses the gaps in current strategies by utilizing profound learning methods. The primary targets are to create a deep learning model for anticipating medical requests and give noteworthy stock management experiences.

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Medicine Stock Forecasting Using Deep Learning-Based Demand Prediction Model

  • 会议论文

  • 发表日期：

    2025年

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  • Tisha Murarka,
  • Sanvi Bhelkar,
  • Anurag Sinha,
  • Mayur Soni,
  • Sanika Bhave,
  • Bhagyashree Hambarde

摘要

Effective management of pharmaceutical stocks is significant for guaranteeing the accessibility of basic drugs while minimizing wastage and costs. This paper points to improving pharmaceutical stock estimating by leveraging deep learning-based request expectation models. Traditional stock management procedures frequently fail to capture the complexities and varieties in pharmaceutical requests, driving stock outs or overloading. Progresses in machine learning, especially deep learning, give unused openings to make strides in determining exactness. Precise request forecasts are crucial for optimizing stock levels, reducing costs, and ensuring opportune accessibility of medicines. This extension addresses the gaps in current strategies by utilizing profound learning methods. The primary targets are to create a deep learning model for anticipating medical requests and give noteworthy stock management experiences.