Full Text:   <3002>

Summary:  <218>

CLC number: TP391; F273

On-line Access: 2023-03-25

Received: 2022-03-04

Revision Accepted: 2022-09-26

Crosschecked: 2023-03-25

Cited: 0

Clicked: 1429

Citations:  Bibtex RefMan EndNote GB/T7714


Lujun ZHAO


Yiping FENG


-   Go to

Article info.
Open peer comments

Frontiers of Information Technology & Electronic Engineering  2023 Vol.24 No.3 P.417-432


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

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",
publisher="Zhejiang University Press & Springer",

%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

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

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.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1]Akdil KY, Ustundag A, Cevikcan E, 2018. Maturity and readiness model for Industry 4.0 strategy. In: Ustundag A, Cevikcan E (Eds.), Industry 4.0: Managing the Digital Transformation. Springer, Cham, p.61-94.

[2]Angreani LS, Vijaya A, Wicaksono H, 2020. Systematic literature review of Industry 4.0 maturity model for manufacturing and logistics sectors. Proc Manuf, 52:337-343. https://doi.‍org/10.1016/j.‍promfg.‍2020.11.056

[3]Basl J, Doucek P, 2019. A metamodel for evaluating enterprise readiness in the context of Industry 4.0. Information, 10(3):89.

[4]Bibby L, Dehe B, 2018. Defining and assessing Industry 4.0 maturity levels - case of the defence sector. Prod Plann Contr, 29(12):1030-1043.

[5]Blanchet M, Rinn T, Von Thanden G, et al., 2014. Roland Berger Industry 4.0 Readiness Index. Roland Berger Strategy Consultants.

[6]Botha AP, 2018. Rapidly arriving futures: future readiness for Industry 4.0. S Afr J Ind Eng, 29(3):‍148-160.

[7]Boyes H, Hallaq B, Cunningham J, et al., 2018. The Industrial Internet of Things (IIoT): an analysis framework. Comput Ind, 101:1-12.

[8]Chonsawat N, Sopadang A, 2021. Smart SMEs 4.0 maturity model to evaluate the readiness of SMEs implementing Industry 4.0. Chiang Mai Univ J Nat Sci, 20(2):e2021027.

[9]Demeter K, Losonci D, Szász L, et al., 2018. Assessing Industry 4.0 readiness: a multi-country industry level analysis. Proc 25th Annu EurOMA Conf.

[10]Dilberoglu UM, Gharehpapagh B, Yaman U, et al., 2017. The role of additive manufacturing in the era of Industry 4.0. Proc Manuf, 11:545-554.

[11]Erol S, Schumacher A, Sihn W, 2016. Strategic guidance towards Industry 4.0—a three-stage process model. Proc Int Conf on Competitive Manufacturing.

[12]Gökalp E, Şener U, Eren PE, 2017. Development of an assessment model for Industry 4.0: Industry 4.0-MM. Proc 17th Int Conf on Software Process Improvement and Capability Determination, p.128-142.

[13]Hamidi SR, Aziz AA, Shuhidan SM, et al., 2018. SMEs maturity model assessment of IR4.0 digital transformation. Proc 7th Int Conf on Kansei Engineering and Emotion Research, p.721-732.

[14]He B, Bai KJ, 2021. Digital twin-based sustainable intelligent manufacturing: a review. Adv Manuf, 9(1):1-21.

[15]Hizam-Hanafiah M, Soomro MA, Abdullah NL, 2020. Industry 4.0 readiness models: a systematic literature review of model dimensions. Information, 11(7):364.

[16]Holubek R, Kostal P, 2013. The intelligent manufacturing systems. Adv Sci Lett, 19(3):972-975.

[17]Horvat D, Stahlecker T, Zenker A, et al., 2018. A conceptual approach to analysing manufacturing companies' profiles concerning Industry 4.0 in emerging economies. Proc Manuf, 17:419-426.

[18]Kang HS, Lee JY, Choi S, et al., 2016. Smart manufacturing: past research, present findings, and future directions. Int J Prec Eng Manuf Green Technol, 3(1):111-128.

[19]Li BH, Hou BC, Yu WT, et al., 2017. Applications of artificial intelligence in intelligent manufacturing: a review. Front Inform Technol Electron Eng, 18(1):86-96.

[20]Lichtblau K, Stich V, Bertenrath R, et al., 2015. IMPULS-Industrie 4.0-Readiness. Impuls-Stiftung des VDMA, Aachen-Köln.

[21]Lin WD, Low MYH, Chong YT, et al., 2019. Application of SIRI for Industry 4.0 maturity assessment and analysis. Proc IEEE Int Conf on Industrial Engineering and Engineering Management, p.1450-1454.

[22]Methavitakul B, Santiteerakul S, 2018. Analysis of key dimension and sub-dimension for Supply Chian of Industry to fourth Industry Performance Measurement. Proc IEEE Int Conf on Service Operations and Logistics, and Informatics, p.191-195.

[23]Oztemel E, Gursev S, 2020. Literature review of Industry 4.0 and related technologies. J Intell Manuf, 31(1):127-182.

[24]Pacchini APT, Lucato WC, Facchini F, et al., 2019. The degree of readiness for the implementation of Industry 4.0. Comput Ind, 113:103125.

[25]Pereira AC, Romero F, 2017. A review of the meanings and the implications of the Industry 4.0 concept. Proc Manuf, 13:1206-1214.

[26]Schaupp E, Abele E, Metternich J, 2017. Potentials of digitalization in tool management. Proc CIRP, 63:144-149.

[27]Schuh G, Anderl R, Gausemeier J, et al., 2017. Industrie 4.0 Maturity Index. Managing the Digital Transformation of Companies (Acatech STUDY). Herbert Utz Verlag, Munich.

[28]Schumacher A, Erol S, Sihn W, 2016. A maturity model for assessing Industry 4.0 readiness and maturity of manufacturing enterprises. Proc CIRP, 52:161-166.

[29]Singapore Economic Development Board, 2017. The Singapore smart Industry Readiness Index: Catalysing the Transformation of Manufacturing. Singapore EDB.

[30]Stefan L, Thom W, Dominik L, et al., 2018. Concept for an evolutionary maturity based Industrie 4.0 migration model. Proc CIRP, 72:404-409.

[31]Wang BC, Tao F, Fang XD, et al., 2021. Smart manufacturing and intelligent manufacturing: a comparative review. Engineering, 7(6):738-757.

[32]Wang LH, 2019. From intelligence science to intelligent manufacturing. Engineering, 5(4):615-618.

[33]World Economic Forum, 2022. The Global Lighthouse Network Playbook for Responsible Industry Transformation. World Economic Forum.

[34]Wright PK, Bourne DA, 1988. Manufacturing Intelligence. Addison-Wesley, Reading, USA.

[35]Wu FJ, Kao YF, Tseng YC, 2011. From wireless sensor networks towards cyber physical systems. Perv Mob Comput, 7(4):397-413.

[36]Zhong RY, Xu X, Klotz E, et al., 2017. Intelligent manufacturing in the context of Industry 4.0: a review. Engineering, 3(5):616-630.

[37]Zuehlke D, 2010. SmartFactory—towards a factory-of-things. Ann Rev Contr, 34(1):129-138.

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE