Optimizing Talent Allocation to Empower the Digital Transformation of Manufacturing Industry in Guangdong Province
DOI:
https://doi.org/10.53469/wjimt.2025.08(11).01Keywords:
Manufacturing industry, Digital Transformation, Talent allocationAbstract
Digital transformation of the manufacturing industry has entered a new stage of systematic development. Optimal talent allocation provides necessary skills support and innovative impetus. This paper selects the manufacturing industry in Guangdong Province as research object, analyzing the practical pathways of optimizing talent allocation to empower the digital transformation of the manufacturing industry. Results find that driven by market, technological, and management transformation, optimized talent allocation effectively empowers the digital transformation process of the manufacturing industry. Furthermore, focusing on the strategic system, development environment, and cultivation model of digital transformation talent, this paper presents practical strategies for effectively optimizing talent allocation, to accelerating the digital transformation and upgrading of the manufacturing industry in Guangdong province.
References
Yu Minggui, He Mengmeng, Zhang Mengmeng. Talent Introduction Policy, Optimal Allocation of Labor Force and Intelligent Manufacturing [J]. China Industrial Economics, 2024, (05): 116-134.
Liang Lin, Cao Wenrui, Liu Bing. Simulation and Optimization Path Study on Talent Resource Allocation Policy in Beijing-Tianjin-Hebei Region [J]. China Human Resources Development, 2019, 36(03):91-100.
Wang Weizhe, Zhao Zhong, Hu Kai. Making The Best Use of Talent: Institutional Attraction, Talent Allocation and High-Quality Development [J]. China Economic Quarterly, 2024, 24(02):605-623.
Lan Anqi, Jia Haonan. Research on Industrial Structure Upgrading and Talent Allocation Mechanism Based on System Dynamics [J]. Journal of Entrepreneurship in Science & Technology, 2025, 38(08):106-114.
Shi Xiaofei, Cai Yuqing, Liu Zhichao. Mixed Ownership Reform of State-Owned Enterprises and Allocation of Innovative Talents [J]. Finance and Accounting Monthly, 2025,46(20):116-123.
Southern Finance| Guangdong’s total skilled workforce has reached 22.01 million; subsidized training will be conducted focusing on advanced manufacturing - Guangdong Provincial People’s Congress Website [EB/OL] https://www.gdpc.gov.cn/gdrdw/rdzt/xlzthy/gszrcyy/sydt/content/post_238530.html.
Zhang Guiping, Chen Xinxin, Zhang Xuan. What a Digital Transformation in Manufacturing Affects Enterprise Human Capital Structure: A Heterogeneous Explanation Based on Dual Transformation Paths [J]. Human Resources Development of China, 2025, 42(06):6-24.
Jiang Weimin, Zheng Qiongjie, Cao Jingsong. Construction and Application of Evaluation System for Digital Transformation of Regional Manufacturing[J]. Journal of Nanjing University (Philosophy, Humanities and Social Sciences), 2023, 60(06):127-137.
Tan Zhixiong, Mu Siying, Zheng Huarong. Digital Transformation of Manufacturing Empowers the Emergence of New Quality Productivity: A Case Study of “Lighthouse Factories” [J]. China Soft Science, 2025, (06): 28-40.
Zhang Yan, Huang Junjie, Chen Zhen. Digital Economy Empowers the Transformation and Upgrading of Manufacturing Industry: Logic, Cases and Paths - A Perspective Based on the Evolution of the “Technology-Economy Paradigm” [J]. Journal of Technology Economics, 2024, 43(11):49-59.