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

TL;DR

This paper presents a hybrid deep learning framework for real-time disruption detection in digital supply chain twins, aiming to improve resilience by identifying disruptions and predicting recovery times.

Contribution

It introduces a novel hybrid deep learning approach combining autoencoders, SVMs, and LSTM models for disruption detection and recovery prediction in supply chain twins.

Findings

Effective disruption detection with manageable false alarms

Trade-off identified between detection sensitivity and delay

First application of this hybrid approach in supply chain disruption detection

Abstract

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.