Blockchained supply chain management based on IoT tracking and machine learning

Abstract

When it comes to running and managing modern supply chains, 6G Internet of things (IoT) is of utmost importance. To provide IoT with security and automation, blockchain and machine learning are two upper-layer technology that can help. First, we propose to utilize blockchain in modern supply chains to ensure efficient collaboration between all parties. Second, we adopt multi-head attention (MHA)-based gated recurrent unit (GRU) to do inbound logistics task prediction. Finally, numerical results justify that multi-head attention-based GRU model has better fitting efficiency and prediction accuracy than its counterparts.

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

“…According to Table 12, the performance of the suggested structure surpasses that of comparable methods developed for predicting SC risk (see Table 12). Additionally, we compared the proposed system with three more studies, i.e., Hu et al [1], Dong et al [5], and Liu et al [10]. The results presented in Table 12 show that our proposed technique surpasses the mentioned works.…”

“…Feature Map: Once the bias and activation functions are added to the convolution map, feature maps are computed for the input data of interest, as described in Equation (5).…”

“…The faster decision-makers can recognize or predict a potential threat to the SC, the more effectively they will be able to limit the risk's negative effects by implementing an adequate prevention strategy [4]. At the beginning of the risk detection and evaluation plan, it is critical to identify both external risks (such as the risk of demand versus supply, environmental risks, and corporate risks) and internal hazards (such as the risk of production, the risk of organizing and monitoring, and the risk of prevention and backup [5].…”

“…DL combines a complete collection of feature-embedding techniques to proactively train from data and reliably anticipate future events [6,7]. It has been used in a variety of applications over the years, including stock market predictions [6], evaluating academic accomplishments [7], developing predictive models [3], and classifying literature [8], among others [5]. As a result, data scientists work hard to develop real-world solutions that can assist network administrators in more accurately forecasting SC hazards in the logistics sector [3].…”

Supply Chain Risk Diffusion in Partially Mapping Double-Layer Hypernetworks

“…Research on supply chain risk is crucial to financial risk management [3,4]. There is a need to study the performance indicators and characteristics of the supply chain and develop a conceptual model for identifying supply chain risks [5], design an intuitionistic fuzzy two-stage supply chain network considering multi-mode demand and multi-mode transportation [6], and utilize machine learning to optimize supply chain management and maximize benefits on block-chained big data [7]. In addition to the main supply chain research mentioned above, the study of supply chain risk diffusion behavior referencing epidemic models is also a hot topic.…”