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CLC number: TP391; F273
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2023-03-25
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Citations: Bibtex RefMan EndNote GB/T7714
A novel model for assessing the degree of intelligent manufacturing readiness in the process industry: process-industry intelligent manufacturing readiness index (PIMRI)
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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, 2023, 24(3): 417-432.
@article{title="A novel model for assessing the degree of intelligent manufacturing readiness in the process industry: process-industry intelligent manufacturing readiness index (PIMRI)",
author="Lujun ZHAO, Jiaming SHAO, Yuqi QI, Jian CHU, Yiping FENG",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="24",
number="3",
pages="417-432",
year="2023",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2200080"
}
%0 Journal Article
%T A novel model for assessing the degree of intelligent manufacturing readiness in the process industry: process-industry intelligent manufacturing readiness index (PIMRI)
%A Lujun ZHAO
%A Jiaming SHAO
%A Yuqi QI
%A Jian CHU
%A Yiping FENG
%J Frontiers of Information Technology & Electronic Engineering
%V 24
%N 3
%P 417-432
%@ 2095-9184
%D 2023
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2200080
TY - JOUR
T1 - A novel model for assessing the degree of intelligent manufacturing readiness in the process industry: process-industry intelligent manufacturing readiness index (PIMRI)
A1 - Lujun ZHAO
A1 - Jiaming SHAO
A1 - Yuqi QI
A1 - Jian CHU
A1 - Yiping FENG
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 24
IS - 3
SP - 417
EP - 432
%@ 2095-9184
Y1 - 2023
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2200080
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 newly proposed model has six levels to describe different development stages for intelligent manufacturing. In addition, the model consists of four races, nine species, and 25 domains that are relevant to the essential businesses of companies’ daily operation and capability requirements of intelligent manufacturing. Furthermore, these 25 domains are divided into 249 characteristic items to evaluate the manufacturing readiness in detail. A questionnaire is also designed based on the proposed model to help process-industry companies easily carry out self-diagnosis. Using the new method, a case including 196 real-world process-industry companies is 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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