Backorder Prediction

Backorder prediction focuses on accurately forecasting instances where customer orders cannot be immediately fulfilled due to inventory shortages. Current research emphasizes improving prediction accuracy using diverse machine learning models, including deep learning architectures (like sequence-to-sequence models and variational autoencoders), ensemble methods, and even emerging quantum-inspired approaches. These advancements aim to address challenges like imbalanced datasets and the need for model interpretability, ultimately leading to better inventory management, reduced costs, and improved supply chain resilience. The resulting insights offer significant practical value for businesses and contribute to the broader field of predictive analytics.

Papers

July 23, 2024

Forecasting Automotive Supply Chain Shortfalls with Heterogeneous Time Series
Bach Viet Do, Xingyu Li, Chaoye Pan, Oleg Gusikhin
Time Series Disruptive Event Supply Chain Risk Supply Chain Disruption Science Factory Backorder Prediction

September 25, 2023

Backorder Prediction in Inventory Management: Classification Techniques and Cost Considerations
Sarit Maitra, Sukanya Kundu
Hidden CoST Classification Algorithm Inventory Management Ensemble Bagging Backorder Prediction

July 24, 2023

QAmplifyNet: Pushing the Boundaries of Supply Chain Backorder Prediction Using Interpretable Hybrid Quantum-Classical Neural Network
Md Abrar Jahin, Md Sakib Hossain Shovon, Md. Saiful Islam, Jungpil Shin, M. F. Mridha, Yuichi Okuyama
Document Boundary Quantum Classical Neural Network Quantum Inspired Machine Learning Supply Chain Optimization Interpretable Hybrid Backorder Prediction

Backorder Prediction

Papers

Forecasting Automotive Supply Chain Shortfalls with Heterogeneous Time Series

Backorder Prediction in Inventory Management: Classification Techniques and Cost Considerations

QAmplifyNet: Pushing the Boundaries of Supply Chain Backorder Prediction Using Interpretable Hybrid Quantum-Classical Neural Network