China's manufacturing locus in 2025: With a comparison of “Made-in-China 2025” and “Industry 4.0”

By Ling Xia Li. Technological forecasting & social change, 2017

927 citations recorded by the source corpus.

In this study, we have compared Germany's “Industry 4.0” and China's “Made-in-China 2025” and estimated China's locus in “Made-in-China 2025”. “Made-in-China 2025” has clear goals, measures and sector focus. Its guiding principles are to enhance industrial capability through innovation-driven manufacturing, optimize the structure of Chinese industry, emphasize quality over quantity, train and attract talent, and achieve green manufacturing and environment. Data show that currently China is no longer the lowest–cost labor market; it is being squeezed by newly emerging low-cost producers such as Vietnam, Cambodia, and Laos. Meanwhile, China is not the strongest player in the high-tech arena; well-established industrialized nations, the US, Germany, and Japan, have all effectively deployed digital technology to create new industrial environments, produce new products, and improve their well-established brands. Having analyzed the data from the World Bank and China's National Bureau of Statistics, we find an upward trajectory in China in manufacturing capability development, research and development commitment, and human capital investment. However, implementing an ambitious strategic plan such as “Made-in-China 2025” is coupled with challenges. This research helps us understand the relationship between technological entrepreneurship and socio-economic changes in emerging economies such as China. Furthermore, the experience accumulated in China can be referenced by both emerging economies and developed nations to advance their technological entrepreneurship.

Subfields: Economics and Econometrics, Industrial and Manufacturing Engineering, Management of Technology and Innovation

View Paper: https://www.semanticscholar.org/paper/a880b21c59deec88d1ead26f7f0571f7fac60c5d

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Citations in the corpus

Data-Driven Methods for Predictive Maintenance of Industrial Equipment: A Survey (2019, background): “Moreover, the government of China proposed the “Made in China 2025” strategy [4], which is particularly a significant”
Industry 4.0: How it is defined from a sociotechnical perspective and how much sustainability it includes – A literature review (2020, background): “Several publications consider CPS (Li, 2018; Wollschlaeger et al., 2017) and IoT (Li, 2018; Wollschlaeger et al., 2017; Xu et al., 2018) as the technological drivers of increased automation.”
Future of industry 5.0 in society: human-centric solutions, challenges and prospective research areas (2022, background): “Data accuracy is required for the business to validate the transactions, which are recorded [103–105].”
Industry 4.0 as an enabler of sustainability diffusion in supply chain: an analysis of influential strength of drivers in an emerging economy (2019, background): “0 has been materialised globally and popularly adopted by German firms such as Volkswagen, Daimler and BMW, etc. Recently, the Indian and Chinese Governments have also introduced the ‘Make in India’ and ‘Made in China 2025’ strategies (Li 2018) respectively.”
Decomposition analysis of sustainable green technology inventions in China (2019, background): “According to Li (2017), the Chinese government is working toward ‘Made-in-China 2025’ to promote industrial capability due to innovation-driven manufacturing, industrial optimization, quality improvements, and green development.”
Internet of Things (IoT) Cybersecurity Research: A Review of Current Research Topics (2019, background): “The IoT has dramatically changed the entire world and our daily lives [115].”
Barriers to the adoption of industry 4.0 technologies in the manufacturing sector: An inter-country comparative perspective (2020, background): “Subsequently, in 2013, the German government adopted it as a strategic initiative to revolutionize the manufacturing industry (Li, 2017; Xu et al., 2018).”
Reskilling and Upskilling the Future-ready Workforce for Industry 4.0 and Beyond (2022, neutral): “…the Internet of Things (IoT), and Industrial Internet of Things (IIoT), artificial intelligence, machine learning, hyper-con-verged infrastructure, deep learning, virtualization, and more to create an intelligent production system (Li, 2018, 2020; Xu et al., 2018; Li & Zhou, 2020; Xu et al., 2014).”