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In the current logistics technology market, besides deploying logistics warehousing information system, the more intelligent way is the mechanical logistics automation represented by mechanization/robotics, and the lightweight logistics automation represented by the Internet of Things (IoT).

Automation of mechanical logistics

• Highly efficient
• Complete mechanical automation

• Large investments with slow returns，it takes an average of 4-10 years to see investment returns
• Difficult to move or modify， >6-18 months construction period
• Requiresstandalone space，single interval operationscan only be strictly human-operated or machine-operated
• There is an upper limit on operational capacity,resulting in poor operational flexibility
• Unable to handle business operations with large fluctuations

Lightweight Logistics Automation

• With traditional storage facilities,it is able to achieve almost fully-mechanized levels of efficiency
• Human-computer collaborative hybrid automation

• Small investment,quick returns on investment after a year
• Easy to disassemble and modify; quick to launch;1-2 months construction period
• 人Hybrid operations possible,the number of workers and machines can be increased or decreased according to operational needs
• Flexible, adaptable, able to cope with various business fluctuations

From the bottom up, lightweight logistics automation architecture comprises of “industrial IoT hardware - industrial IoT platform - operation system - optimization module”, to achieve a complete framework that encompasses software, hardware and system optimization.

• Analysis and optimization of logistics processes：logistics resource utilization analysis, logistics behavior data analysis, bottleneck analysis, etc.;
• Standard logistics business system：warehouse management including warehouse access, and shelf management; and logistics execution such as picking and distribution;
• IoT platform：provides IoT device management, configuration, predictive maintenance, and interface docking with standard business data in business systems;
• Intelligent IoT devices：enables wireless communication, sensor interaction, location sensing, automatic identification and other functions.

Using industrial-grade intelligent IoT equipment, traditional logistics facilities can be easily transformed into intelligent facilities and connected to information systems, allowing these logistics facilities to better "understand" business operations and interact automatically with other facilities.

• Operators:Operators are able to receive information exchanged by the intelligent IoT devices through wearable smart bracelets, for hands-free and efficient logistics operations;

• Picking wagon:The picking wagon is equipped with RFID sensors, guiding lights and communication modules to realize intelligent interaction with the system and picking shelves, thus guiding operators to complete the picking task more efficiently;

• Picking shelves:The picking shelves are equipped with RFID sensors, guiding lights, smart bracelet interaction and communication modules to realize intelligent interaction with the system and picking wagons, thus guiding operators to complete the picking task more efficiently.

No manpower required; big data technology automatically collects and analyzes all behavioral data in the logistics process, and reverse-optimizes the IoT module for each logistics scenario, in an on-site implementation and improvement loop.

• IoT smart devices empower storage facilities and people for greater efficiency in performing logistics tasks;
• Noah IoT platform automatically collects data on logistics behavior
• Big data technology analyzes the collected data on logistics behavior;
• AI technology reverse-optimizes the IoT module, thus achieving closed-loop optimization.

Closed-loop optimization is achieved through the use of AI technology that analyzes logistics behavior data, and assists in predictive decision-making and reverse-optimization based on different logistics scenarios.

• Job segment analysis：real-time analysis of operators’ picking frequency, timeliness, picking path and other job segment data;
• Resource status analysis：real-time analysis of the utilization rate of labor, wagon and logistics resources;
• Resource conflict analysis：real-time analysis of the compatibility between storage resources and business needs;
• Predictive assisted decision-making：to assist management decision-making, the system provides suggestions for optimization based on the various types of analysis above, combined with the needs of the business.