New quality productivity enables the development of artificial intelligence enterprises ‐‐ taking Alibaba as an example

Liu Shuke1, Cai Zhizhao2, Song Xinying3 

(1: Zhongnan University of Economics and Law, Hubei, China 2: Hua Qiao University, Fujian, China 3: Fujian Medical University)

ABSTRACT 

New quality productivity is a kind of innovative and advanced productivity covering new fields and new technologies. A manufacturing enterprise hopes to improve the market competitiveness by introducing new quality productivity technology. Through the construction of mathematical model and the analysis of market economy, this paper provides the basis of technology selection decision and production plan formulation, and carries out risk assessment and risk management for enterprises, so as to help enterprises achieve long‐term and high‐quality development. In order to comprehensively evaluate the impact of new technologies on enterprise production, we selected four indicators of production efficiency, product quality and cost saving as measures. Given that the selection and evaluation of new technologies is a multi‐scheme and multi‐criterion decision problem, and it is difficult to directly quantify and compare each evaluation index, hierarchical analysis is used to design the evaluation scheme. By establishing the hierarchical structure model, constructing the judgment matrix and testing the consistency of the matrix, using the MATLAB calculation, the total weight of each new quality productivity technology is obtained. Among them, the total weight of artificial intelligence technology is 0.2662, ranking the first place, providing a strong basis for enterprises to make decisions. Considering that the application of new technology should bring better production benefits, benefits and technological innovation to enterprises, the rate of scientific and technological progress, capital output, labor output and other indicators are introduced to judge the expected effect of the application of new technology. Combined with the results of market research, this paper analyzes the challenges that enterprises may face, such as the sharp rise in cost and the difficulties in market investment. For the specific application of artificial intelligence technology, considering the combination of science and technology with labor and capital, this paper selects the Cobb‐Douglas production function to determine the contribution of scientific and technological progress, capital growth and labor growth to the output growth. In terms of risk assessment, this paper focuses on the risks of the introduction of artificial intelligence technology, such as increased R & D investment and management expenses. Due to the great uncertainty in the risk assessment, and the artificial intelligence is in the initial stage, the available sample data is limited, so the grey association analysis method is used to build the risk assessment model, determine the analysis sequence, and calculate the correlation degree of each risk factor. The results showed that the correlation degree of R & D investment risk, administrative expense risk, fixed asset risk and market risk were 0.7717,0.5084,0.715 and 0.5411, respectively. This provides a basis for enterprises to develop targeted risk management strategies. 29 Make a long‐term technology update and business development plan for the rapid development of new quality productivity technology and the change of industry competitive environment. Considering the limited operating cost of an enterprise, it is necessary to maximize the return on investment under the limited cost, the multi‐objective optimization model is adopted to provide decision support for enterprise planning business development. By setting goals for the allocation optimization of technology iteration, market expansion and product innovation investment, the objective function and multi‐objective optimization model are established, and then the target approximation method is used to solve them. The reform scheme helps enterprises to adjust the cost allocation scheme in real time according to the dynamic changes of the market and technology, so as to achieve higher investment benefits. 

Keywords: hierarchical analysis, Cobb‐Douglas production function, gray correlation analysis, multi‐objective optimization model.

REFERENCE 

[1] Liang Shengrong, & Luo Liangwen. (2024). Focus, difficulties and key paths to accelerat e the formation of new quality productive forces in the new era. Contemporary econom ic management: 1‐10. 

[2] Tan Zhixiong, Mu Siying, & Han Jingwei and so on. (2024). New quality productivity drives the global value chain upward: theoretical logic and realistic path. Journal of Ch ongqing University (Social Science Edition):1‐12. 

[3] Zhang Zhixin, Zheng Xiaoming, & Qian Chen. (2024).  ʺFour‐chainʺ  integration empower s new quality productivity: internal logic and practical path. Journal of Shandong Unive rsity (Philosophy and Social Sciences Edition):1‐12. 

[4] Hu Hongbin.(2023).Theoretical Logic and Practical Approach of General Secretary Xi Jin pingʹs Important Discourse on New Productive Forces. Economist,16‐25.   

[5] Jiang, Y., & Qiao, Z. (2024). New Productivity: Logic, Connotation, and Path. Social Scie nce Research, (1). 

[6] Xu Zheng, Zheng Linhao, & Cheng Mengyao. (2023). The Inherent Logic and Practical I deas of Empowering High‐Quality Development with New Productivity. Contemporary Economic Research, 339(11), 51‐58. 

[7] Zan Qiong. (2024).Fiscal and tax risks and coping strategies for R & D activities of tech nology and medium‐sized smes. Caixun, 1‐4. 

[8] Li Jie. (2023). Research on fixed assets Performance evaluation of public hospitals  ‐‐  bas ed on dual perspectives of financial supervision and asset management. Fiscal Science,95 ‐101. 

[9] ZHAO Ruonan, Song Xiangrong, & Chen Hailong. (2024).New quality productivity, Ne w Industrialization and high quality development. Finance and Economics ,1‐15. 

[10] Xu Zheng, & Zhang Jiaoyu. (2024).New quality productivity Promoting the transformati on and upgrading of manufacturing industry: Value orientation, logical mechanism and important measures. Journal of Social Sciences, Hunan Normal University,53(02),95‐101

Liu Shuke, Cai Zhizhao, Song Xinying.pdf