Journal Home Online First Current Issue Archive For Authors Journal Information 中文版

Strategic Study of CAE >> 2022, Volume 24, Issue 2 doi: 10.15302/J-SSCAE-2022.02.026

Research Status and Future Prospects of Intelligent Manufacturing Evaluation Theory

1. School of Economics and Management, University of Science and Technology, Beijing 100083, China;

2. Department of Informatization and Industry Development, State Information Center, Beijing 100045, China

Funding project:National Natural Science Foundation of China “Research on Feature Representation and Clustering of Highdimensional Mixed Data Fusion Based on Deep Learning”(71971025) Received: 2021-08-26 Revised: 2021-12-03 Available online: 2022-02-17

Next Previous

Abstract

Intelligent manufacturing is crucial for constructing a powerful manufacturing country. As China’s intelligent manufacturing enters a comprehensive promotion stage, the scientific evaluation of intelligent manufacturing becomes a practical demand. This paper provides a systematic survey on the intelligent manufacturing evaluation theories in recent years. The evaluation index systems of intelligent manufacturing are classified and summarized from three perspectives, that is, key technology, overall system, and specific sector. Furthermore, the methods commonly used in intelligent manufacturing evaluation are compared and analyzed. This paper also investigates the major problems regarding intelligent manufacturing evaluation and discusses the future research directions of the field. Currently, there are deficiencies in the standards, processes, index system, and application of intelligent manufacturing evaluation. It is necessary to improve the evaluation paradigm, evaluation system, and new technology integration, so as to promote the research of intelligent manufacturing evaluation theories and guide the development of intelligent manufacturing. Specifically, China should improve the standards design to establish an intelligent manufacturing evaluation paradigm, optimize the index system to enrich the key evaluation content, strengthen the integration of new technologies, and promote the synergy of theory and practice.

Figures

Fig. 1

References

[ 1 ] Zhou J. Intelligent manufacturing: Main direction of “Made in China 2025” [J]. China Mechanical Engineering, 2015, 26(17): 2273–2284. Chinese. link1

[ 2 ] Zhong Z H, Zang J Y, Yan J L, et al. Intelligent manufacturing promotes the comprehensive upgrading and innovative growth of China’s manufacturing industry [J]. Strategic Study of CAE, 2020, 22(6): 136–142. Chinese. link1

[ 3 ] Rauch E, Dallasega P, Unterhofer M. Requirements and barriers for introducing smart manufacturing in small and medium-sized enterprises [J]. IEEE Engineering Management Review, 2019, 47(3): 87–94. link1

[ 4 ] Liu Q. Study on architecture of intelligent manufacturing theory [J]. China Mechanical Engineering, 2020, 31(1): 24–36. Chinese. link1

[ 5 ] Wang B C, Tao F, Fang X D, et al. Smart manufacturing and intelligent manufacturing: A comparative review [J]. Engineering, 2021, 7(6): 738–757. link1

[ 6 ] Zheng T, Ardolino M, Bacchetti A, et al. The applications of Industry 4.0 technologies in manufacturing context: A systematic literature review [J]. International Journal of Production Research, 2021, 59(6): 1922–1954. link1

[ 7 ] Alcácer V, Cruz-Machadoab V. Scanning the Industry 4.0: A literature review on technologies for manufacturing systems [J]. Engineering Science and Technology, an International Journal, 2019, 22(3): 899–919. link1

[ 8 ] Zhou J, Li P G, Zhou Y H, et al. Toward new-generation intelligent manufacturing [J]. Engineering, 2018, 4(1): 11–20. link1

[ 9 ] Chen D F, Heyer S, Ibbotson S, et al. Direct digital manufacturing: definition, evolution, and sustainability implications [J]. Journal of Cleaner Production, 2015, 107: 615–625. link1

[10] Gokalp E, Martinez V. Digital transformation capability maturity model enabling the assessment of industrial manufacturers [J]. Computers in Industry, 2021, 132: 1–12. link1

[11] Li J, Qiu J J, Zhou Y, et al. Study on the reference architecture and assessment framework of industrial Internet platform [J]. IEEE Access, 2020, 8: 164950–164971. link1

[12] Li B H, Zhang L, Wang S L, et al. Cloud manufacturing: A new service-oriented networked manufacturing model [J]. Computer Integrated Manufacturing Systems, 2010, 16(1): 1–7. Chinese. link1

[13] He K T, Zhu D Y. Quality evaluation of cloud manufacturing service [J]. Computer Integrated Manufacturing Systems, 2018, 24(1): 53–62. Chinese. link1

[14] Yang X X, Wang S L, Yang B, et al. A service satisfaction-based trust evaluation model for cloud manufacturing [J]. International Journal of Computer Integrated Manufacturing, 2019, 32(6): 533–545. link1

[15] Hu Y J, Wu L Z, Pan X Q, et al. Comprehensive evaluation of cloud manufacturing service based on fuzzy theory [J]. International Journal of Fuzzy Systems, 2021, 23: 1755–1764. link1

[16] Li B H, Hou B C, Wen T Y, et al. Applications of artificial intelligence in intelligent manufacturing: a review [J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(1): 86–96. link1

[17] Castelo-Branco I, Cruz-Jesus F, Oliveira T. Assessing Industry 4.0 readiness in manufacturing: Evidence for the European Union [J]. Computers in Industry, 2019, 107: 22–32. link1

[18] Han Y T, Wu J Q, Ma J L, et al. Evaluation on intelligent manufacturing capability based on interval number Promethee method [J]. Modern Manufacturing Engineering, 2021 (3): 1–9. Chinese. link1

[19] Li Q, Tang Q L, Chen Y T, et al. Smart manufacturing standardization: Reference model and standards framework [J]. Computer Integrated Manufacturing Systems, 2018, 24(3): 539–549. Chinese. link1

[20] Lee J, Jun S, Chang T W, et al. A smartness assessment framework for smart factories using analytic network process [J]. Sustainability, 2017, 9(5): 1–15. link1

[21] Yang H, Song H M, Yu A P. Theoretical analysis and empirical study on competitive advantages of service-oriented manufacturing: Based on the data of 200 manufacturers in Jiangsu Province [J]. Management Review, 2014, 26(3): 89–99. Chinese. link1

[22] Li L, Mao C, Sun H, et al. Digital twin driven green performance evaluation methodology of intelligent manufacturing: Hybrid model based on fuzzy rough-sets AHP, multistage weight synthesis, and Promethee II [J]. Complexity, 2020, (6): 1–24. link1

[23] Li L H, Qu T, Liu Y, et al. Sustainability assessment of intelligent manufacturing supported by digital twin [J]. IEEE Access, 2020, 8: 174988–175008. link1

[24] Yuan Q T, Yin R Y, Cao X H, et al. Strategic research on the goals, characteristics, and paths of intelligentization of process manufacturing industry for 2035 [J]. Strategic Study of CAE, 2020, 22(03): 148–156. Chinese. link1

[25] Schumacher A, Erol S, Sihn W. A maturity model for assessing Industry 4.0 readiness and maturity of manufacturing enterprises [J]. Procedia CIRP, 2016, 52: 161–166. link1

[26] Zhang J L, Wu S, Gong Y M. Research on evaluation and development of intelligent machinery manufacturing in China [J]. China Mechanical Engineering, 2020, 31(4): 451–458. Chinese. link1

[27] Qian F, Zhong W M, Du W L. Fundamental theories and key technologies for smart and optimal manufacturing in the process industry [J]. Engineering, 2017, 3(2): 154-160. link1

[28] Guo Z L. Research on intelligent manufacturing capability analysis and evaluation of process enterprises [D]. Beijing: Beijing University of Posts and Telecommunications(Master’s thesis), 2020. Chinese. link1

[29] Yin S, Liu L, Hou J. A multivariate statistical combination forecasting method for product quality evaluation [J]. Information Sciences, 2016, 355–356: 229–236. link1

[30] Zhu L, Johnsson C , Varisco M, et al. Key performance indicators for manufacturing operations management–gap analysis between process industrial needs and ISO 22400 standard [J]. Procedia Manufacturing, 2018, 25: 82–88. link1

[31] Shan Z G. Research on intelligent strategy of national manufacturing industry [R]. Beijing: State Information Center, 2018. Chinese.

[32] Leng J W, Zhang H, Yan D X, et al. Digital twin-driven manufacturing cyber-physical system for parallel controlling of smart workshop [J]. Journal of Ambient Intelligence and Humanized Computing, 2019, 10: 1155–1166. link1

Related Research