Intelligent Risk Prediction System in IoT-Based Supply Chain Management in Logistics Sector

Abstract

The Internet of Things (IoT) has resulted in substantial advances in the logistics sector, particularly in logistics storage management, communication systems, service quality, and supply chain management. The goal of this study is to create an intelligent supply chain (SC) management system that provides decision support to SC managers in order to achieve effective Internet of Things (IOT)-based logistics. Current research on predicting risks in shipping operations in the logistics sector during natural disasters has produced a variety of unexpected findings utilizing machine learning (ML) algorithms and traditional feature-encoding approaches. This has prompted a variety of concerns regarding the research’s validity. These previous attempts, like many others before them, used deep neural models to gain features without requiring the user to maintain track of all of the sequence information. This paper offers a hybrid deep learning (DL) approach, convolutional neural network (CNN) + bidirectional gating recurrent unit (BiGRU), to lessen the impact of natural disasters on shipping operations by addressing the question, “Can goods be shipped from a source location to a destination?”. The suggested DL methodology is divided into four stages: data collection, de-noising or pre-processing, feature extraction, and prediction. When compared to the baseline work, the proposed CNN + BiGRU achieved an accuracy of up to 94%.