A Kind of Novel Social Manufacturing Supply Chain Model and its Configuration and Operation Decision Support Techniques

Abstract

In the digital-intelligent transformation of traditional manufacturing enterprises, supply chain modeling provides a systematic management approach.     However, existing methods primarily emphasize network topology, abstracting individual factories and enterprises as nodes while neglecting multi-dimensional representations of internal resources and roles, leading to insufficient characterization of node attributes. Consequently, real-time monitoring of production capacity, inventory, and logistics status at each node becomes challenging. To address the aforementioned issues, this paper proposes a new model for social manufacturing supply chains. In this model, nodes are represented by a Block node model, which is constructed by coupling resource nodes and role nodes, incorporating additional dimensions of node characteristics. This node model can provide standardized input-output data, knowledge, and information, after data mining and security control analysis. Furthermore, the model incorporates a digital twin framework, representing the digital form of the Block node model using tags, cards, and canvases. After upgrading the node model, network modeling and analysis are no longer limited to traditional complex network topology methods, but are capable of capturing the complex dynamic behaviors of nodes within the supply chain, and real-time monitoring of node states and interdependencies in actual operations. This enables collaborative operations in the supply chain network and optimization of resource allocation, offering multi-decision support services. Finally, based on a case study of a company that masters industrial-grade additive manufacturing technology, the graph attributed network embedding (GATNE) algorithm was used to implement quality traceability in the supply chain, validating the feasibility of the proposed method.

Keywords: Supply chain modeling, Operation decision support, Block node model, Digital twin, Social manufacturing