Agentic AI for retail supply chain
Adapting to Evolving Requirements: Agentic AI for Retail Supply Chain Operations
The framework outperforms direct LLM reformulation in supply chain operations, offering retailers a more reliable solution for adapting to evolving requirements.
Researchers developed a graph-constrained agentic framework for adapting retail supply chain operations. The framework was evaluated with 100 warehouse requirements using GPT, Qwen, and DeepSeek as base LLMs. It improved end-to-end success from 72-76% to 79-83% compared to direct LLM reformulation.
Adapting to Evolving Requirements: Agentic AI for Retail Supply Chain Operations
Retail supply chain operations rely on coupled decision modules that must adapt as requirements evolve. LLMs offer a natural-language interface for this task, but existing methods primarily focus on individual optimization models. Extending them to heterogeneous decision pipelines is challenging because a requirement may admit multiple intervention paths with different downstream effects. We formulate requirement-driven adaptation as the joint selection of an intervention route and an admissible module-level change, and propose a graph-constrained agentic framework in which domain agents expose admissible reformulation interfaces and a central processor searches over bounded intervention paths. Candidates are validated and compared using downstream KPIs. In collaboration with a large retail partner, we evaluate 100 warehouse requirements elicited from practitioner interviews, with GPT, Qwen, and DeepSeek as base LLMs. Relative to direct LLM reformulation, our framework improves correctness and end-to-end success across all three models, raising end-to-end success from 72--76% to 79--83%.
