# Disruption Detection for a Cognitive Digital Supply Chain Twin Using Hybrid Deep Learning

arXiv: https://arxiv.org/abs/2309.14557 · HTML: https://mlsanity.com/paper/2309.14557/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 43 references that are in the map.
- First posted on arXiv 25 Sep 2023.

## Builds on (most cited first, 2 of 2)

- [LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection](https://mlsanity.com/paper/1607.00148/) (2016)
- [Cognitive Twins for Supporting Decision-Makings of Internet of Things Systems](https://mlsanity.com/paper/1912.08547/) (2019)

## Built on by (most cited first, 3 of 3)

- [A Theoretical Framework for Graph-based Digital Twins for Supply Chain Management and Optimization](https://mlsanity.com/paper/2504.03692/) (2025)
- [Uncertainty in Supply Chain Digital Twins: A Quantum-Classical Hybrid Approach](https://mlsanity.com/paper/2411.10254/) (2024)
- [Towards explainable decision support using hybrid neural models for logistic terminal automation](https://mlsanity.com/paper/2509.07577/) (2025)

## Abstract (arXiv)

Purpose: Recent disruptive events, such as COVID-19 and Russia-Ukraine conflict, had a significant impact of global supply chains. Digital supply chain twins have been proposed in order to provide decision makers with an effective and efficient tool to mitigate disruption impact. Methods: This paper introduces a hybrid deep learning approach for disruption detection within a cognitive digital supply chain twin framework to enhance supply chain resilience. The proposed disruption detection module utilises a deep autoencoder neural network combined with a one-class support vector machine algorithm. In addition, long-short term memory neural network models are developed to identify the disrupted echelon and predict time-to-recovery from the disruption effect. Results: The obtained information from the proposed approach will help decision-makers and supply chain practitioners make appropriate decisions aiming at minimizing negative impact of disruptive events based on real-time disruption detection data. The results demonstrate the trade-off between disruption detection model sensitivity, encountered delay in disruption detection, and false alarms. This approach has seldom been used in recent literature addressing this issue.