Ai-Driven Retail Digital Twins For Inventory And Supply-Chain Decision Optimization A Closed-Loop Framework For Simulation, Scenario Intelligence, And Governed Execution

Abstract

Retail supply chains operate under simultaneous pressure from volatile demand, fragmented inventory, omnichannel fulfillment, supplier uncertainty, transportation disruption, labor constraints, and rising customer expectations. Conventional dashboards and control towers improve visibility, but visibility alone does not answer the most important operational question: what should the enterprise do next, and what will happen if it does?

Digital twins provide a mechanism for answering that question by maintaining a synchronized virtual representation of the retail network and using it as a safe environment for scenario testing. In 2026, the convergence of real-time event streams, semantic data models, optimization, physical simulation, computer vision, agentic AI, and governed workflow orchestration is moving the digital-twin concept beyond visualization toward decision intelligence. Current examples include digital twins for warehouse and robotic-fleet optimization, continuously updated retail inventory models, and AI systems that connect supply-chain data to execution-ready actions [1–7].

This article proposes a Retail Supply-Chain Decision Twin Framework (RSDTF) that separates seven capabilities: signal capture, semantic network modeling, synchronized twin state, scenario simulation, multi-objective optimization, governed execution, and outcome learning. It also introduces a Twin Decision Readiness Score (TDRS) to determine whether a twin is sufficiently current, complete, calibrated, and governed to support a specific decision. A five-level maturity model distinguishes descriptive digital representations from adaptive agentic twins.

The central argument is that a retail digital twin should not be evaluated by visual fidelity or the volume of data connected to it. Its value depends on decision fidelity: whether the twin can reproduce the constraints that matter, compare feasible futures, select actions against explicit business objectives, execute them within policy boundaries, and learn from the real-world result.