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The primary goal of any industrial organization is to achieve profitability through meeting the needs of customers, ensuring high uptime for products, and minimizing stock costs. Since maintaining high availability is costly and cannot reach 100%, backorders may occur when items are out of stock. While some backorders are inevitable, it is important to anticipate them in order to take proactive measures, such as reducing lead times and costs. To predict backorders accurately, we suggest using a deep learning model in this study with the aim of maximizing profits from backorder decisions.

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A Predictive Approach Based on Artificial Neural Networks for Supply Chain Optimization

  • 会议论文

  • 发表日期：

    2026年

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  • Said Tkatek,
  • Hamza Ettakifi

摘要

The primary goal of any industrial organization is to achieve profitability through meeting the needs of customers, ensuring high uptime for products, and minimizing stock costs. Since maintaining high availability is costly and cannot reach 100%, backorders may occur when items are out of stock. While some backorders are inevitable, it is important to anticipate them in order to take proactive measures, such as reducing lead times and costs. To predict backorders accurately, we suggest using a deep learning model in this study with the aim of maximizing profits from backorder decisions.