In the context of globalization and digital integration, enterprise supply chains are facing increasing risks of interruption, and traditional management focuses on efficiency while neglecting resilience. The existing research lacks empirical exploration of the specific mechanism of the role of AI driven big data analysis capabilities in supply chain risk management. This study integrates information processing theory and dynamic capability theory to construct a theoretical model of "AI driven big data analysis capability expected capability/improvisation capability supply chain elasticity". Social capital is introduced as a moderating variable, and the hypothesis is verified through hierarchical regression and Bootstrap method. Research has found that the big data analysis capabilities driven by artificial intelligence significantly enhance supply chain resilience, and generate partial mediating effects by enhancing expected capabilities (environmental trend prediction and strategy formulation) and improvisation capabilities (real-time decision-making and resource restructuring); Social capital plays a positive moderating role in the relationship between expected ability, improvisation ability, and supply chain elasticity. A high level of social capital can enhance the promoting effect of ability on elasticity and regulate the strength of mediating effects. Research provides a data completion path for optimizing the full lifecycle cost of the supply chain, emphasizing the synergistic effect of artificial intelligence technology and cross organizational social capital. Future expansion into multiple industries and exploration of vertical data deepening mechanisms.

Yanchun Wang. Application of data completion and full lifecycle cost optimization integrating artificial intelligence in supply chain. Socio-Economic Statistics Research (2025), Vol. 6, Issue 2: 127-135. https://doi.org/10.38007/SESR.2025.060213.

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