Data-Driven Segmentation and Targeting in China’s Mineral Resource Supply Chain: A Quantitative Study of Industry Buyers and Traders

Hongyu Zhou

doi:10.54097/4717nw17

Authors

Hongyu Zhou

DOI:

https://doi.org/10.54097/4717nw17

Keywords:

Mineral Resource Supply Chain, Data-Driven Segmentation, Big Data Analytics, Machine Learning, Supply Chain Risk Management, China, Artificial Intelligence, Blockchain, Geopolitical Risks

Abstract

China's mineral resource supply chain is crucial in global industrial production and economic stability. That is why political instability, environmental issues, and unstable materials prices make it compulsory for firms to employ data-driven segmentation and targeting techniques. This paper uses a quantitative approach to analyze big data, machine learning and advanced analytics for effective supply chain management in mineral resources. The study reveals that the industry buyers and traders can be classified depending on their purchasing activeness and willingness to take risks to design strategies that increase efficiency among the suppliers. It also looks into the roles of advanced frontiers like artificial intelligence and blockchain in enhancing transparency, demand forecasting and risk management. Also, international relations such as trade wars and interrupted supply chains impact China’s strategic management of mineral resources. The study provides policy prescriptions for increasing domestic production, engaging in international cooperation, and developing technology to reduce supply chain risks. Ultimately, data-driven segmentation and targeting offer a robust framework for strengthening China's position in the global mineral market while ensuring sustainability and security.

Downloads

Download data is not yet available.

References

[1] Wang et al. "Guidance and review: Advancing mining technology for enhanced production and supply of strategic minerals in China." Green and Smart Mining Engineering (2024).

[2] Bi et al. "A new reform of mining production and management modes under industry 4.0: cloud mining mode." Applied Sciences 12, no. 6 (2022): 2781.

[3] Li et al. "Overview on the development of intelligent methods for mineral resource prediction under the background of geological big data." Minerals 12, no. 5 (2022): 616.

[4] Buchholz et al. "Big data analyses for real-time tracking of risks in the mineral raw material markets: implications for improved supply chain risk management." Mineral Economics 35, no. 3 (2022): 701-744.

[5] Zhou et al. "Demystifying China's Critical Minerals Strategies: Rethinking ‘De-risking’ Supply Chains." World Trade Review (2023): 1-25.

[6] Cho, Sungjoon. "Gazing into World Society: A System-Based Approach to Global Governance." Available at SSRN 4611935 (2023).

[7] Ghoshray, Atanu, and Madhavi Pundit. "Economic growth in China and its impact on international commodity prices." International Journal of Finance & Economics 26, no. 2 (2021): 2776-2789.

[8] Greitemeier et al. "China's hold on the lithium-ion battery supply chain: Prospects for competitive growth and sovereign control." Journal of Power Sources Advances 32 (2025): 100173.

[9] Gulley et al. "China, the United States, and competition for resources that enable emerging technologies." Proceedings of the National Academy of Sciences 115, no. 16 (2018): 4111-4115.

[10] Jiang et al. "Supply chain resilience of mineral resources industry in China." Discrete Dynamics in Nature and Society 2023, no. 1 (2023): 1338223.

[11] Li et al. "Overview on the development of intelligent methods for mineral resource prediction under the background of geological big data." Minerals 12, no. 5 (2022): 616.

[12] Lithium: Critical for Net Zero, Yet Facing Challenges.

[13] Rolf et al. "A review on reinforcement learning algorithms and applications in supply chain management." International Journal of Production Research 61, no. 20 (2023): 7151-7179.

[14] Shiquan, Dou, and Xu Deyi. "The security of critical mineral supply chains." Mineral Economics 36, no. 3 (2023): 401-412.