Digital Twin (DT) control in Smart Warehouse Environments requires balancing representation accuracy against computational latency. Static DT approaches either violate timing constraints with high fidelity or compromise decision quality with low fidelity. We introduce an Adaptive Fidelity Digital Twin (AF-DT) framework leveraging Rate-Distortion Theory (RDT), which relates information rate (R, reflecting resource cost) to distortion (D, the inverse of fidelity). Our AF-DT dynamically adjusts its operating point (R, D) and integrates RDT with system characterization to predict and manage operational conflicts where task-demanded fidelity implies an estimated latency \(\left( {L_{est} } \right)\) exceeding maximum tolerable latency \(\left( {L_{{max,T_{j} }} } \right)\) for task \(T_{j}\) . The methodology includes an RDT-informed adaptation algorithm with explicit conflict detection and fallback mechanisms. Using warehouse simulation data, we demonstrate the framework’s ability to identify latency conflicts and maintain stable operation, enhancing DT robustness for reliable control in demanding environments.