Risk model of financial supply chain of Internet of Things enterprises: A research based on convolutional neural network

2021 ◽  
Author(s):  
Jingfu Lu ◽  
Xu Chen
Keyword(s):  
Neural Network ◽  
Supply Chain ◽  
Internet Of Things ◽  
Convolutional Neural Network ◽  
Risk Model

An Empirical Study of Supply Chain Risk Warning in China’s Automobile Manufacturing Based on BP Neural Network

2012 ◽  
Vol 630 ◽  
pp. 496-501 ◽  
Author(s):  
Ming Hong Zhang ◽  
Liang Lu ◽  
Yu Liu
Keyword(s):  
Neural Network ◽  
Supply Chain ◽  
Empirical Study ◽  
Risk Model ◽  
Warning System ◽  
Risk Indicator ◽  
Supply Chain Risk ◽  
Automobile Manufacturing ◽  
Academic Level ◽  
Risk Warning

From the perspective of risk indicator of supply chain, this paper makes an empirical study of risk warning system in Jinlong Automobile Group in Fujian province. It discusses several indicators that cause risks to supply chain in company and categorize them. Then risk model is tested with artificial neural network to testify its applicability and accuracy. It’s argued that this is a rewarding attempt to go from academic level towards practical use and explores ways of thinking for risk warning system designing.

scholarly journals An Integrative Framework for Online Prognostic and Health Management Using Internet of Things and Convolutional Neural Network

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2338 ◽  
Author(s):  
Yuanju Qu ◽  
Xinguo Ming ◽  
Siqi Qiu ◽  
Maokuan Zheng ◽  
Zengtao Hou
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Internet Of Things ◽  
Convolutional Neural Network ◽  
Health Management ◽  
Added Value ◽  
Time Data ◽  
Integrative Framework ◽  
Operational Level ◽  
Prognostic And Health Management

With the development of the internet of things (IoTs), big data, smart sensing technology, and cloud technology, the industry has entered a new stage of revolution. Traditional manufacturing enterprises are transforming into service-oriented manufacturing based on prognostic and health management (PHM). However, there is a lack of a systematic and comprehensive framework of PHM to create more added value. In this paper, the authors proposed an integrative framework to systematically solve the problem from three levels: Strategic level of PHM to create added value, tactical level of PHM to make the implementation route, and operational level of PHM in a detailed application. At the strategic level, the authors provided the innovative business model to create added value through the big data. Moreover, to monitor the equipment status, the health index (HI) based on a condition-based maintenance (CBM) method was proposed. At the tactical level, the authors provided the implementation route in application integration, analysis service, and visual management to satisfy the different stakeholders’ functional requirements through a convolutional neural network (CNN). At the operational level, the authors constructed a self-sensing network based on anti-inference and self-organizing Zigbee to capture the real-time data from the equipment group. Finally, the authors verified the feasibility of the framework in a real case from China.

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