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Accurate demand forecasting plays a pivotal role in minimizing stockouts, reducing inventory costs, and improving overall supply chain performance. The main purpose of this study is to compare three forecasting techniques and provide accurate operational forecasts of the monthly supply chain demand. The forecasting method consists of understanding and predicting customer demand in the objective of making smart decisions about supply chain operations and future sales. The forecast errors are compared using the root mean squared error and mean absolute percent error. The obtained results of the study indicate that LSTM provides better forecasting demand than other techniques.

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Forecasting in Supply Chain Using Machine Learning Techniques

  • 会议论文

  • 发表日期：

    2025年

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  • Abdellah Bais,
  • Khalid Amechnoue

摘要

Accurate demand forecasting plays a pivotal role in minimizing stockouts, reducing inventory costs, and improving overall supply chain performance. The main purpose of this study is to compare three forecasting techniques and provide accurate operational forecasts of the monthly supply chain demand. The forecasting method consists of understanding and predicting customer demand in the objective of making smart decisions about supply chain operations and future sales. The forecast errors are compared using the root mean squared error and mean absolute percent error. The obtained results of the study indicate that LSTM provides better forecasting demand than other techniques.