Rethinking Supply Chain Planning: A Generative Paradigm
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arXiv
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| Format: | Preprint |
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2025
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| author | Yin, Jiaheng Qi, Yongzhi Zhang, Jianshen Geng, Dongyang Chen, Zhengyu Hu, Hao Qi, Wei Shen, Zuo-Jun Max |
| author_facet | Yin, Jiaheng Qi, Yongzhi Zhang, Jianshen Geng, Dongyang Chen, Zhengyu Hu, Hao Qi, Wei Shen, Zuo-Jun Max |
| contents | Supply chain planning is the critical process of anticipating future demand and coordinating operational activities across the logistics network. However, within the context of contemporary e-commerce, traditional planning paradigms, typically characterized by fragmented processes and static optimization, prove inadequate in addressing dynamic demand, organizational silos, and the complexity of multi-stage coordination. To address these challenges, this study proposes a fundamental rethinking of supply chain planning, redefining it not merely as a computational task, but as an interactive, integrated, and automated cognitive process. This new paradigm emphasizes the organic unification of human strategic intent with adaptive execution, shifting the focus from rigid control to continuous, intelligent orchestration. To operationalize this conceptual shift, we introduce a Generative AI-powered agentic framework. Functioning as an intelligent cognitive interface, this framework bridges the gap between unstructured business contexts and structured analytical workflows, enabling the system to comprehend complex semantics and coordinate decisions across organizational boundaries. We demonstrate the empirical validity of this approach within JD.com's large-scale operations. The deployment confirms the efficacy of this cognitive paradigm, yielding an approximate 22% improvement in planning accuracy and a 2% increase in in-stock rates, thereby validating the transformation of planning into an adaptive, knowledge-driven capability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_03811 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Rethinking Supply Chain Planning: A Generative Paradigm Yin, Jiaheng Qi, Yongzhi Zhang, Jianshen Geng, Dongyang Chen, Zhengyu Hu, Hao Qi, Wei Shen, Zuo-Jun Max Artificial Intelligence Supply chain planning is the critical process of anticipating future demand and coordinating operational activities across the logistics network. However, within the context of contemporary e-commerce, traditional planning paradigms, typically characterized by fragmented processes and static optimization, prove inadequate in addressing dynamic demand, organizational silos, and the complexity of multi-stage coordination. To address these challenges, this study proposes a fundamental rethinking of supply chain planning, redefining it not merely as a computational task, but as an interactive, integrated, and automated cognitive process. This new paradigm emphasizes the organic unification of human strategic intent with adaptive execution, shifting the focus from rigid control to continuous, intelligent orchestration. To operationalize this conceptual shift, we introduce a Generative AI-powered agentic framework. Functioning as an intelligent cognitive interface, this framework bridges the gap between unstructured business contexts and structured analytical workflows, enabling the system to comprehend complex semantics and coordinate decisions across organizational boundaries. We demonstrate the empirical validity of this approach within JD.com's large-scale operations. The deployment confirms the efficacy of this cognitive paradigm, yielding an approximate 22% improvement in planning accuracy and a 2% increase in in-stock rates, thereby validating the transformation of planning into an adaptive, knowledge-driven capability. |
| title | Rethinking Supply Chain Planning: A Generative Paradigm |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2509.03811 |