ReeWell

WellFMS enables unified management and control of all horizontal transport equipment across diverse logistics scenarios. Through centralized dispatch and monitoring, it improves fleet utilization, reduces downtime, and ensures efficient coordination across operations.

Built on dual-engine AI, WellScheduler handles complex scheduling for vehicles, cranes, and assets, supporting automated, semi-automated, and manual modes. It integrates with TOS for seamless task dispatch, adjusts to customer needs, and recalculates resources in real-time during disruptions, minimizing energy use and restoring flow quickly.

WellSimtec provides high-precision digital twins and full-link AI simulation verification for logistics and ports, enabling the replication of layouts, equipment dynamics, cargo flows, and disturbance factors. It also interfaces with TOS/FMS/ERP for global joint debugging to identify optimization points and predict key KPIs. The platform supports "simulation validation + ultra-fast simulation," enabling autonomous testing and validation of functional details from product design to pre-launch. It outputs the best/worst-case scenarios and playbacks, guiding pre-shift decision-making and improving throughput and collaboration efficiency.

WellRCMS is a task allocation and dispatch system for non-vehicle operational equipment, such as quay cranes and yard cranes. It unifies equipment status, operation plans, and energy data to cover task orchestration, execution monitoring, and collaborative dispatching. Combining AI-driven, full-scenario task allocation, the system dynamically balances equipment loads to prevent excessive energy consumption from prolonged high-intensity operation of any single device, while reducing idle time and waiting to boost overall efficiency and resource utilization.

Driven by "orders + data + AI," WellYMS utilizes AI-driven strategies and predictive models to enable real-time collaboration and inventory visibility across the entire inbound/outbound process. It centrally dispatches automated equipment, such as autonomous vehicles, AS/RS, and Wellbots, to maximize throughput and space utilization. The system seamlessly connects upstream and downstream order and inventory data. Its modular microservices architecture ensures easy adaptation and rapid deployment, helping warehouses reduce costs and increase efficiency.

AI digital modeling reconstructs the energy management of traditional social units. Driven by "retail-oriented product core + digital management," it integrates electricity, oil, gas, and water/steam into one system, seamlessly integrating metering, data collection, closed-loop data, predictive analysis, and optimal scheduling for refined control by process, zone, or equipment. It also aggregates distributed assets (EVs, heat pumps, storage batteries, large cranes) to participate in demand response and grid ancillary services, supporting the full value chain from energy retail to customer service.