6G-enabled digital twin-based smart warehouse for supply chain of things

Abstract Traditional warehouses face several challenges, including limited technological integration, inefficient storage management, unproductive operations, and the lack of centralized monitoring due to manual processes. The Supply Chain of Things (SCoT) integrates the Internet of Things (IoT) with smart warehouse operations to support efficient decision-making. In such environments, digital twins enable data-driven warehouse management, while 6G provides the high data rates, ultra-low latency, and reliability required for real-time coordination. In this context, this paper addresses the Storage Location Assignment Problem (SLAP), a key challenge in smart warehouse management. First, we identify the main research questions, technological challenges, and representative use cases arising in digital twin–enabled smart warehouse environments. Based on these observations, we propose a 6G-enabled digital twin-based smart warehouse architecture that enables continuous interaction between physical warehouse assets and their digital counterparts. Within this architecture, SLAP is formulated as an optimization problem and solved using a Genetic Algorithm (GA) to support efficient, real-time, and energy-aware storage decisions. We also analyze warehouse investment aspects by considering energy consumption, equipment operating time, and warehouse cost per square foot. Evaluation results show that the proposed digital twin-driven approach improves warehouse efficiency by reducing energy consumption, operational time, and battery recharging requirements, while achieving approximately 2.5× lower warehouse storage investment cost through improved space utilization. Sources available at https://github.com/yusufozcevik/DT-based-Smart-Warehouse-for-SLAP .