Influencing Factors and Evaluation Model of Quality Risks in Intelligent Manufacturing Mobile Supply Chain

Abstract

The intelligent manufacturing (IM) supply chain (SC) involves multiple distributed agents. The mobile supply chain (MSC) technology supports the real-time management of key information resources in the supply chain of IM products. This paper explores the influencing factors and evaluation model of quality risks in IM MSC, trying to make realistic evaluation of the actual quality risks of the enterprise. Firstly, the authors constructed a quality risk identification framework for IM MSC, and a hierarchical evaluation index system (EIS) based on the factors affecting quality risks. Besides, the features and attributes of four dimensions of quality risks were specified, and the corresponding intuitive triangular fuzzy numbers were given. Next, an evaluation model was established for the quality risks of IM MSC based on backpropagation neural network (BPNN). After the evaluation of quality risks, a contract model was designed for the quality risk control in IM MSC. The proposed EIS and models were proved effective through experiments.

Amos-Based Risk Forecast of Manufacturing Supply Chain

“…In practice, the product quality is highly uncertain, owing to the immaturity of multiple links (e.g., design, research and development, production, and process flow), and the imperfect management of such links as procurement, production, storage, and transportation. The product quality risk that ensues could bring huge economic losses to enterprises [22].…”

Digital Production Control of Manufacturing Workshop Based on Internet of Things

“…But there is still a large gap from the advanced level of manufacturing in developed countries [1][2][3][4][5][6][7]. Since the proposal of the China Manufacturing 2025 strategy, the penetration of Internet of Things (IoT) in the production control of modern manufacturing workshop has prompted advanced intelligent and information-based manufacturing techniques, such as manufacturing logistics [8][9][10][11]. Combined with wireless sensor network (WSN) and swarm intelligence autonomous perception, these emerging techniques support the whole-process perception of intelligent manufacturing, and provide data support to the rational allocation of production resources and anomaly detection [12][13][14].…”