Research on Evaluation of Intelligent Manufacturing Capability and Layout Superiority of Supply Chain by Big Data Analysis

2022 ◽  
Vol 30 (7) ◽  
pp. 0-0

With the rise of cloud computing, big data and Internet of Things technology, intelligent manufacturing is leading the transformation of manufacturing mode and industrial upgrading of manufacturing industry, becoming the commanding point of a new round of global manufacturing competition. Based on the literature review of intelligent manufacturing and intelligent supply chain, a total factor production cost model for intelligent manufacturing and its formal expression are proposed. Based on the analysis of the model, 12 first-level indicators and 29 second-level indicators of production line, workshop/factory, enterprise and enterprise collaboration are proposed to evaluate the intelligent manufacturing capability of supply chain. This article also further studies the layout superiority and spatial agglomeration characteristics of intelligent manufacturing supply chain, providing useful reference and support for enterprises and policy makers in the decision-making.

2014 ◽  
Vol 651-653 ◽  
pp. 1594-1598
Author(s):  
Yan Hong Ma ◽  
Yao Xie ◽  
Lian Gang Liu

China is a big manufacturing country, and the number of manufacturing enterprises with a certain scale is large. With the increasingly fierce market competition, manufacturing enterprises need to adopt new management methods, improve the market competitiveness of enterprises, and reduce the production cost. This requirement has large scale of market, and it has good economic and society profits. In this paper, the design for intelligent manufacturing information management system is proposed, and it can make the production process be digital and information. The process accuracy is improved, labor costs are reduced, and management efficiency is improved. It can promote the upgrade of production lines effectively, and it can reduce the production line improvement cost. This system not only helps to establish digital production line, but also makes the production manufacturing mode be reformed. It can promote management system, standard, method, and way of production manufacturing to be changed.


Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Haifei Yu ◽  
Songjian Han ◽  
Dongsheng Yang ◽  
Zhiyong Wang ◽  
Wei Feng

The concept of digital twinning has become a hot topic in the manufacturing industry in recent years. The emerging digital twin technology is an intelligent technology that makes full use of multimodels, big data, and interdisciplinary knowledge, which provides some new approaches for the field of the intelligent manufacturing industry. The job shop scheduling problem has been an important research field in the discrete manufacturing industry. Digital twin technology is adopted to solve the problem of job shop scheduling, which provides the possibility for the intelligent development of workshops. Based on digital twin technology and combined with the actual problem of production line scheduling, we propose a new intelligent scheduling platform to solve the shop scheduling problems above. Meanwhile, based on the prediction and diagnosis of multisource dynamic interference in the workshop production process by big data analysis technology, the corresponding interference strategy is formulated in advance by the scheduling cloud platform. The model simulation experiment of intelligent dispatching cloud platform was carried out, and some enterprises in intelligent manufacturing workshop were taken as examples to verify the superiority of the dispatching cloud platform. Finally, we look forward to the future research direction of intelligent manufacturing based on digital twin technology.


2018 ◽  
Vol 153 ◽  
pp. 08005
Author(s):  
Danlin Cai ◽  
Mingyu Chen ◽  
Daxin zhu ◽  
Junjie Liu

With the coming of the intelligent manufacturing, the technology and application of industrial big data will be popular in the future. The productivity, competitiveness and innovation of the manufacturing industries will be improved through the integrated innovation of big data technology and industries. Besides, products, production process, management, services, new form and new models will be more intellectualized. They will support the transformation and upgrading of manufacturing industry and the construction of an open, shared and collaborative ecological environment for intelligent manufacturing industry.


Logistics ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 22
Author(s):  
Hisham Alidrisi

This paper presents a strategic roadmap to handle the issue of resource allocation among the green supply chain management (GSCM) practices. This complex issue for supply chain stakeholders highlights the need for the application of supply chain finance (SCF). This paper proposes the five Vs of big data (value, volume, velocity, variety, and veracity) as a platform for determining the role of GSCM practices in improving SCF implementation. The fuzzy analytic network process (ANP) was employed to prioritize the five Vs by their roles in SCF. The fuzzy technique for order preference by similarity to ideal solution (TOPSIS) was then applied to evaluate GSCM practices on the basis of the five Vs. In addition, interpretive structural modeling (ISM) was used to visualize the optimum implementation of the GSCM practices. The outcome is a hybrid self-assessment model that measures the environmental maturity of SCF by the coherent application of three multicriteria decision-making techniques. The development of the Basic Readiness Index (BRI), Relative Readiness Index (RRI), and Strategic Matrix Tool (SMT) creates the potential for further improvements through the integration of the RRI scores and ISM results. This hybrid model presents a practical tool for decision-makers.


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