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%.

Feature extraction for artificial intelligence enabled food supply chain failure mode prediction

“…Work to predict possible risks in the distribution of vaccines used machine learning on only two management system cases: vaccine demand forecasting and vaccine review sentiment analysis [64]. A hybrid deep learning approach has been proposed to lessen the impact of natural disasters on shipping operations which relies on a convolutional neural network composed of a convolutional layer and a pooling layer to recognize the most significant features, however, it is noted that the logistics industry would benefit from a more nuanced approach to supply chain risks rather than that presented which relies exclusively on binary predictions [65]. The exploration of additional approaches and combinations of deep learning techniques is recommended for more reliable outcomes as well as for increasing the quantity and quality of the database, including the use of real-world case studies and external datasets.…”

“…A multi-agent system relying on artificial intelligence and edge computing has been designed as an alternative consumer-based price-making mechanism to mitigate food speculation whilst improving the sustainability of agricultural production [66]. However, there is a scarcity of case studies into the application of deep learning techniques in supply chain management [65]. Consequently, this research aims to address the gap by considering perishable food supply chains by incorporating both qualitative and quantitative (heterogeneous) sources of data to identify multi-dimensional failure modes via two case studies in a manner that is efficient and effective for large real-world datasets.…”