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A reference system of smart manufacturing talent education (SMTE) in China

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Abstract

Taking smart manufacturing as a strategy for industrial development, China has put forward a people-oriented policy and launched a series of plans for smart manufacturing talent education (SMTE). The demand for smart manufacturing talents in ten priority areas and the industrial applications in China is very huge. Therefore, in this paper, a reference system of SMTE in China is presented, which includes discipline system, training system, practice system, and assessment system. In order to further refine the architecture of smart manufacturing system, a reference course system was proposed; the system contains seven layers, which are basic layer, technique layer, implementation layer, management layer, platform layer, application layer, and industrialization layer. Finally, nine stakeholders of the common operation body were investigated, and a reference implementation of SMTE in China was put forward. In this paper, the smart manufacturing talent education reference system, reference model, and related reference subsystems can be a very useful guideline for Chinese industry and education to design, set, and carry out the smart manufacturing talent education system. At the same time, the system has its reference value for the improvement of China’s smart manufacturing system architecture.

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Reference

  1. Cheng H, Li F, Mao Q (2015) The empirical analysis on the influence of CO2 emission regulation on the export transformation of Chinese manufacturing industries. J Coast Res 73:209–215 https://www.researchgate.net/publication/277579825

    Article  Google Scholar 

  2. Gentner S (2016) Industry 4.0: reality, future or just science fiction? How to convince today’s management to invest in tomorrow’s future! Successful strategies for industry 4.0 and manufacturing IT. Chimia 70(9):628–633 https://www.researchgate.net/publication/308272980

    Article  Google Scholar 

  3. Wang Q, Sun X, Cobb S, Lawson G, Sharples S (2018) 3D printing system: an innovation for small-scale manufacturing in home settings?—early adopters of 3D printing systems in China. Int J Prod Res 54(20):1–16 https://www.researchgate.net/publication/296620706

    Google Scholar 

  4. Ding Q, Cai W, Wang C, Sanwal M (2017) The relationships between household consumption activities and energy consumption in china—an input-output analysis from the lifestyle perspective. Appl Energy 207. https://www.researchgate.net/publication/317638833

  5. Stahmer AC, Suhrheinrich J, Schetter PL, Hassrick MG (2018) Exploring multi-level system factors facilitating educator training and implementation of evidence-based practices (EBP): a study protocol. Implement Sci 13(1):3. https://doi.org/10.1186/s13012-017-0698-1

  6. Li L (2017) China’s manufacturing locus in 2025: with a comparison of “Made-in-China 2025” and “Industry 4.0”. Technological Forecasting & Social Change. https://www.sciencedirect.com/science/article/pii/S0040162517307254

  7. Liu Z, School EL (2015) The choices of legal business development model on P2P network lending platform: talking from the “guiding opinions of promoting the healthy development of internet banking”. J Cent South Univ. http://www.cnki.com.cn/Article/CJFDTotal-ZLXS201506005.htm

  8. Guibao X (2016) Analysis of “three-year implementation plan of ‘Internet plus’ artificial intelligence”. China Internet. http://www.en.cnki.com.cn/Article_en/CJFDTotal-HLWT201612010.htm

  9. Li Q, Zhang W, Li H, He P (2017) CO 2 emission trends of China's primary aluminum industry: A scenario analysis using system dynamics model. Energy Policy 105:225–235 http://ir.ipe.ac.cn/handle/122111/22538

    Article  Google Scholar 

  10. Xu Y (2017) The Next Generation of Artificial Intelligence: The New Driving force Leading World Development. Front Inform Tech El. http://www.en.cnki.com.cn/Article_en/CJFDTOTAL-RMXS201720003.htm

  11. Chao LI, Jin-Fa LI (2016) Analysis of manufacturing talents’ development pattern in Industry 4.0. value engineering. http://en.cnki.com.cn/Article_en/CJFDTOTAL-JZGC201631027.htm

  12. Dong Z (2016) Discussion on the Construction of Highly-skilled Personnel in Petroleum Enterprises. Journal of the Party School of Shengli Oilfield. http://www.en.cnki.com.cn/Article_en/CJFDTOTAL-SLYT201601027.htm

  13. Guoqiang T, Economics So (2016) “Double First-class” Construction and China's Contribution to Economics Development. J Financ Econ. http://en.cnki.com.cn/Article_en/CJFDTOTAL-CJYJ201610003.htm

  14. Cao GH (2017) Exploration and Analysis on the Social Value of China's Higher Education under the Background of Creating Double First-class. Heilongjiang Researches on Higher Education. http://www.en.cnki.com.cn/Article_en/CJFDTotal-HLJG201705025.htm

  15. Jian-Xiong HU (2016) A Brief Study on the Contemporary Craftsman Spirit in China and Its Cultivation Paths. Journal of Liaoning Provincial College of Communications. http://qikan.cqvip.com/article/detail.aspx?id=668979539

  16. Yang B, Wang ZY, Center ET (2017) Intelligent Manufacturing Talents Cultivation Based on Flexible Manufacturing System's Engineering Training Teaching. Research & Exploration in Laboratory. http://www.cnki.com.cn/Article/CJFDTOTAL-SYSY201701050.htm

  17. Xu S, Chen S, Han X (2011) Notice of RetractionTaking students as essentials, quality as foundation in cultivating advanced practice-oriented talents in Yantai University. IEEE:1–5. https://www.researchgate.net/publication/252008641

  18. Mcgrail MR, Russell DJ, Campbell DG (2016) Vocational training of general practitioners in rural locations is critical for the Australian rural medical workforce. Med J Aust 205(5):216 https://www.researchgate.net/publication/307602041

    Article  Google Scholar 

  19. Bai J, Song Y, Liu D, Duan H (2016) Adapt to the need of the production line adaptive social building research and practice of applied talents training system. J Am Coll Cardiol 50(8):741–747 https://www.researchgate.net/publication/305633202

    Google Scholar 

  20. Otieno MA (2016) Assessment of teacher education in Kenya. Clin Neurophysiol 127(3):e9–e9 http://oasis.col.org/handle/11599/2648

    Article  Google Scholar 

  21. Fields A (2015) Partnerships and new roles in the 21st-century academic library: collaborating, embedding, and cross-training for the future. J Biol Chem 107(2):591–597 https://www.researchgate.net/publication/317117978

    Google Scholar 

  22. Acatech (2017) Cyber-physical systems. Computer 50(4):14–16 https://xrds.acm.org/article.cfm?aid=2590778

    Article  Google Scholar 

  23. Wasim A, Shehab E, Abdalla H, Al-Ashaab A, Sulowski R, Alam R (2013) An innovative cost modelling system to support lean product and process development. Int J Adv Manuf Technol 65(1–4):165–181. https://doi.org/10.1007/s00170-012-4158-4

    Article  Google Scholar 

  24. Li J, Tao F, Cheng Y, Zhao L (2015) Big data in product lifecycle management. Int J Adv Manuf Technol 81(1–4):667–684. https://doi.org/10.1007/s00170-015-7151-x

    Article  Google Scholar 

  25. Huang B, Li C, Yin C, Zhao X (2013) Cloud manufacturing service platform for small- and medium-sized enterprises. Int J Adv Manuf Technol 65(9–12):1261–1272. https://doi.org/10.1007/s00170-012-4255-4

    Article  Google Scholar 

  26. Zhuang YT, Wu F, Chen C, Pan YH (2017) Challenges and opportunities: from big data to knowledge in AI 2.0. Front Inform Tech El 18(1):3–14. https://doi.org/10.1631/FITEE.1601883

  27. Li W, Wu WJ, Wang HM, Cheng XQ, Chen HJ, Zhou ZH, Ding R (2017) Crowd intelligence in AI 2.0 era. Front Inform Tech El 18(1):15–43. https://doi.org/10.1631/FITEE.1601859

  28. Zheng NN, Liu ZY, Ren PJ, Ma YQ, Chen ST, Yu SY, Xue JR, Chen BD, Wang FY (2017) Hybrid-augmented intelligence: collaboration and cognition. Front Inform Tech El 18(2):153–179. https://doi.org/10.1631/FITEE.1700053

  29. Peng YX, Zhu WW, Zhao Y, Chang-Sheng XU, Huang QM, Han-Qing LU, Zheng QH, Huang TJ, Gao W (2017) Cross-media analysis and reasoning: advances and directions. Front Inform Tech El 18(1):44–57. https://doi.org/10.1631/FITEE.1601787

  30. Zhang T, Qing LI, Zhang CS, Liang HW, Ping LI, Wang TM, Shuo LI, Zhu YL, Cheng WU, Automation DO (2017) Current trends in the development of intelligent unmanned autonomous systems. Front Inform Tech El 18(1):68–85. https://doi.org/10.1631/FFITEE.1601650

  31. Endelt B (2017) Design strategy for optimal iterative learning control applied on a deep drawing process. Int J Adv Manuf Technol 88(1–4):3–18. https://doi.org/10.1007/s00170-016-8501-z

    Article  Google Scholar 

  32. Ostasevicius V, Jurenas V, Augutis V, Gaidys R, Cesnavicius R, Kizauskiene L, Dundulis R (2017) Monitoring the condition of the cutting tool using self-powering wireless sensor technologies. Int J Adv Manuf Technol 88(9–12):2803–2817. https://doi.org/10.1007/s00170-016-8939-z

    Article  Google Scholar 

  33. Liu Y, Wang X, Du F, Yao M, Gao Y, Wang F, Wang J (2017) Computer vision detection of mold breakout in slab continuous casting using an optimized neural network. Int J Adv Manuf Technol 88(1–4):557–564. https://doi.org/10.1007/s00170-016-8792-0

    Article  Google Scholar 

  34. Li BH, Hou BC, Yu WT, Lu XB, Yang CW (2017) Applications of artificial intelligence in intelligent manufacturing: a review. Front Inform Tech El 18(1):86–96. https://doi.org/10.1631/FFITEE.1601885

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Funding

This work is supported by Shanghai Key Laboratory of Advanced Manufacturing Environment, Shanghai Institute of Producer Service Development (SIPSD), and Shanghai Research Center for industrial Informatics (SRCI2). This work was also supported by the National Natural Science Foundation of China #1 under Grant number 71632008, Transformation and Upgrading of Industry in 2017 (China Manufacturing 2025) #2 under Grant number ZL35060009002, and Innovation and Development of Industrial Internet in Shanghai of China #3 under Grant number 2017-GYHLW-01009.

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Correspondence to Xinguo Ming.

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Zhang, X., Ming, X., Liu, Z. et al. A reference system of smart manufacturing talent education (SMTE) in China. Int J Adv Manuf Technol 100, 2701–2714 (2019). https://doi.org/10.1007/s00170-018-2856-2

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