A Deep Learning Approach for Supply Chain Risk Prediction

Document Type

Conference Proceeding

Source of Publication

Lecture Notes of the Institute for Computer Sciences Social Informatics and Telecommunications Engineering Lnicst

Publication Date

4-1-2026

Abstract

The increasing complexity of supply chains necessitates advanced risk prediction methods to mitigate disruptions and inefficiencies. Traditional risk assessment models often fail to capture sequential dependencies and evolving patterns in supply chain data. This study proposes a Bi-LSTM-based deep learning framework for supply chain risk prediction, leveraging bidirectional learning to enhance classification accuracy. The model is trained and evaluated on real-world supply chain transaction data, demonstrating superior performance over conventional machine learning and deep learning classifiers. Experimental results show that the proposed Bi-LSTM model achieves higher accuracy, recall, and F1-score, effectively identifying potential risks. By incorporating sequential dependencies, the model improves predictive reliability, addressing limitations of existing risk classification approaches. The findings highlight the potential of deep learning for robust supply chain risk management, paving the way for more adaptive and scalable predictive solutions.

ISBN

[9783032166340]

ISSN

1867-8211

Publisher

Springer Nature Switzerland

Volume

676 LNICST

First Page

115

Last Page

130

Disciplines

Computer Sciences

Keywords

Bi-LSTM, Deep Learning, Supply Chain Risk Prediction

Scopus ID

105038814234

Indexed in Scopus

yes

Open Access

no

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