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<p>This research proposes a machine learning-based approach to enhance demand forecasting in inventory systems, taking into account crucial factors like inflation, preservation technology, and carbon emissions. By leveraging machine learning algorithms, the model improves forecasting accuracy, minimizes total inventory costs, and supports eco-friendly practices by reducing carbon footprints. Numerical illustrations and comparative analyses demonstrate the superiority of machine learning models over traditional statistical methods in handling uncertain and fluctuating demand patterns. Ultimately, this approach contributes to the development of sustainable inventory systems that align with environmental regulations and green supply chain strategies. Sensitivity analysis is performed for the behavior of different parameters on the optimal results.</p>

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Machine Learning Approach for Demand Forecasting in an Inventory Model Under Carbon Emissions and Preservation Technology

  • 期刊论文

  • 发表日期：

    2025年10月31日

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  • Jyoti Chaudhary,
  • S. R. Singh,
  • Dipti Singh

摘要

This research proposes a machine learning-based approach to enhance demand forecasting in inventory systems, taking into account crucial factors like inflation, preservation technology, and carbon emissions. By leveraging machine learning algorithms, the model improves forecasting accuracy, minimizes total inventory costs, and supports eco-friendly practices by reducing carbon footprints. Numerical illustrations and comparative analyses demonstrate the superiority of machine learning models over traditional statistical methods in handling uncertain and fluctuating demand patterns. Ultimately, this approach contributes to the development of sustainable inventory systems that align with environmental regulations and green supply chain strategies. Sensitivity analysis is performed for the behavior of different parameters on the optimal results.