  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.
