Supply chain management relies on accurate backorder prediction for
optimizing inventory control, reducing costs, and enhancing customer
satisfaction. However, traditional machine-learning models struggle with
large-scale datasets and complex relationships, hindering real-world data
collection. This research introduces a novel methodological framework for
supply chain backorder prediction, addressing the challenge of handling large
datasets. Our proposed model, QAmplifyNet, employs quantum-inspired techniques
within a quantum-classical neural network to predict backorders effectively on
short and imbalanced datasets. Experimental evaluations on a benchmark dataset
demonstrate QAmplifyNet's superiority over classical models, quantum ensembles,
quantum neural networks, and deep reinforcement learning. Its proficiency in
handling short, imbalanced datasets makes it an ideal solution for supply chain
management. To enhance model interpretability, we use Explainable Artificial
Intelligence techniques. Practical implications include improved inventory
control, reduced backorders, and enhanced operational efficiency. QAmplifyNet
seamlessly integrates into real-world supply chain management systems, enabling
proactive decision-making and efficient resource allocation. Future work
involves exploring additional quantum-inspired techniques, expanding the
dataset, and investigating other supply chain applications. This research
unlocks the potential of quantum computing in supply chain optimization and
paves the way for further exploration of quantum-inspired machine learning
models in supply chain management. Our framework and QAmplifyNet model offer a
breakthrough approach to supply chain backorder prediction, providing superior
performance and opening new avenues for leveraging quantum-inspired techniques
in supply chain management.

供应链管理中，准确的缺货预测对于优化库存控制、降低成本和提高客户满意度至关重要。本研究提出了一种新的方法论框架，通过使用量子启发技术在量子 - 经典神经网络中对缺货进行有效预测，有效应对大规模数据集处理的挑战。实验评估表明，我们的模型 QAmplifyNet 在处理短且不平衡的数据集方面表现优越，使其成为供应链管理的理想解决方案。此外，为了提高模型的可解释性，我们还运用了可解释的人工智能技术。未来的工作将包括探索更多的量子启发技术、扩展数据集，以及研究其他供应链应用。本研究揭示了量子计算在供应链优化中的潜力，并为在供应链管理中进一步探索量子启发机器学习模型铺平了道路。我们的方法论框架和 QAmplifyNet 模型提供了一种突破性的供应链缺货预测方法，提供卓越性能，并为利用量子启发技术在供应链管理中开辟了新的途径。