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Optimizing Supply Chain Management Through BO-CNN-LSTM for Demand Forecasting and Inventory Management

Optimizing Supply Chain Management Through BO-CNN-LSTM for Demand Forecasting and Inventory Management

Rong Liu, Vinay Vakharia
Copyright: © 2024 |Volume: 36 |Issue: 1 |Pages: 25
ISSN: 1546-2234|EISSN: 1546-5012|EISBN13: 9798369324530|DOI: 10.4018/JOEUC.335591
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MLA

Liu, Rong, and Vinay Vakharia. "Optimizing Supply Chain Management Through BO-CNN-LSTM for Demand Forecasting and Inventory Management." JOEUC vol.36, no.1 2024: pp.1-25. http://doi.org/10.4018/JOEUC.335591

APA

Liu, R. & Vakharia, V. (2024). Optimizing Supply Chain Management Through BO-CNN-LSTM for Demand Forecasting and Inventory Management. Journal of Organizational and End User Computing (JOEUC), 36(1), 1-25. http://doi.org/10.4018/JOEUC.335591

Chicago

Liu, Rong, and Vinay Vakharia. "Optimizing Supply Chain Management Through BO-CNN-LSTM for Demand Forecasting and Inventory Management," Journal of Organizational and End User Computing (JOEUC) 36, no.1: 1-25. http://doi.org/10.4018/JOEUC.335591

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Abstract

This project addresses demand forecasting and inventory optimization in supply chain management. Traditional methods have limitations with complex demand patterns and large-scale data. Deep learning techniques are employed to enhance accuracy and efficiency. The project utilizes BO-CNN-LSTM, leveraging Bayesian optimization for hyperparameter tuning, Convolutional Neural Networks (CNNs) for spatiotemporal feature extraction, and Long Short-Term Memory Networks (LSTMs) for modeling sequential data. Experimental results validate the effectiveness of the approach, outperforming traditional methods. Practical implementation in supply chain management improves operational efficiency and cost control.