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Frontiers of Information Technology & Electronic Engineering 

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A novel model for assessing the degree of intelligent manufacturing readiness in the process industry: Process-industry Intelligent Manufacturing Readiness Index (PIMRI)


Author(s):  Lujun ZHAO, Jiaming SHAO, Yuqi QI, Jian CHU, Yiping FENG

Affiliation(s):  State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, 310027, China; more

Corresponding email(s):  zhaolj@supcon.com, ypfeng@zju.edu.cn

Key Words:  Process-industry; Industry 4.0; Readiness model; Intelligent manufacturing; Readiness index


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Lujun ZHAO, Jiaming SHAO, Yuqi QI, Jian CHU, Yiping FENG. A novel model for assessing the degree of intelligent manufacturing readiness in the process industry: Process-industry Intelligent Manufacturing Readiness Index (PIMRI)[J]. Frontiers of Information Technology & Electronic Engineering , 1998, -1(5): .

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publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2200080"
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Abstract: 
Recently, the implementation of Industry 4.0 has become a new tendency, and it brings both opportunities and challenges to worldwide manufacturing companies. Thus, many manufacturing companies are attempting to find advanced technologies to launch intelligent manufacturing transformation. In this study, we propose a new model to measure the intelligent manufacturing readiness for the process industry, which aims to guide companies in recognizing their current stage and short slabs when carrying out intelligent manufacturing transformation. Although some models have already been reported to measure Industry 4.0 readiness and maturity, there are no models that are aimed at the process industry. This new proposed model has six levels to describe different development stages for intelligent manufacturing. In addition, the model consists of four races, nine species and twenty-five domains that are relevant to the essential businesses of company daily operation and capabilities requirements of intelligent manufacturing. Furthermore, twenty-five domains are divided into 249 characteristic items to evaluate the manufacturing readiness in detail. A questionnaire was also designed based on the proposed model to help process-industry companies easily carry out self-diagnosis. By using the new method, a case including 196 real-world process-industry companies was evaluated to introduce the method of how to use the proposed model. Overall, the proposed model provides a new way to assess the degree of intelligent manufacturing readiness for process-industry companies.

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