Flowr -- Scaling Up Retail Supply Chain Operations Through Agentic AI in Large Scale Supermarket Chains

2026-04-07

AI Summary

This study addresses the challenges of fragmented coordination and delayed responsiveness in retail supermarket supply chains, which stem from reliance on manual decision-making. To overcome these limitations, the authors propose Flowr, a novel framework that integrates a multi-agent architecture with a human-in-the-loop mechanism, enabling end-to-end automated decision-making. Flowr employs a central reasoning large language model (LLM) to orchestrate multiple domain-specific fine-tuned LLMs, while introducing a supervisable collaboration interface based on the Model Context Protocol (MCP) to ensure scalability and accountability. Empirical validation on a real-world large-scale supermarket chain demonstrates that Flowr significantly reduces manual coordination overhead, improves demand–supply matching accuracy, and supports proactive anomaly resolution. The framework exhibits strong potential for generalization across industries.

Abstract

Retail supply chain operations in supermarket chains involve continuous, high-volume manual workflows spanning demand forecasting, procurement, supplier coordination, and inventory replenishment, processes that are repetitive, decision-intensive, and difficult to scale without significant human effort. Despite growing investment in data analytics, the decision-making and coordination layers of these workflows remain predominantly manual, reactive, and fragmented across outlets, distribution centers, and supplier networks. This paper introduces Flowr, a novel agentic AI framework for automating end-to-end retail supply chain workflows in large-scale supermarket operations. Flowr systematically decomposes manual supply chain operations into specialized AI agents, each responsible for a clearly defined cognitive role, enabling automation of processes previously dependent on continuous human coordination. To ensure task accuracy and adherence to responsible AI principles, the framework employs a consortium of fine-tuned, domain-specialized large language models coordinated by a central reasoning LLM. Central to the framework is a human-in-the-loop orchestration model in which supply chain managers supervise and intervene across workflow stages via a Model Context Protocol (MCP)-enabled interface, preserving accountability and organizational control. Evaluation demonstrates that Flowr significantly reduces manual coordination overhead, improves demand-supply alignment, and enables proactive exception handling at a scale unachievable through manual processes. The framework was validated in collaboration with a large-scale supermarket chain and is domain-independent, offering a generalizable blueprint for agentic AI-driven supply chain automation across large-scale enterprise settings.

Problem

• retail supply chain
• manual coordination
• scalability
• decision-intensive workflows
• supply chain automation

Innovation

• Agentic AI
• Supply Chain Automation
• Human-in-the-Loop
• Large Language Models
• Retail Operations