A certain snack food brand company integrates research and development, packaging, and retail, with its business covering both offline stores and online e-commerce. With a rapid increase in orders after the pandemic, the traditional 'people-to-goods' model could no longer meet the efficiency requirements. The company introduced 54 stealth AGVs and 4 forklift AGVs, achieving fully automated handling throughout the entire process from warehousing and picking to outbound delivery, significantly enhancing the level of intelligent logistics.

Low order processing efficiency: The traditional manual picking model cannot keep up with the growing demand for orders, resulting in low operational efficiency and production delays.
High labor intensity: Employees need to frequently go back and forth to the temporary storage area to find goods, which is labor-intensive and time-consuming.
Disorganized warehouse management: Goods are placed haphazardly, making picking difficult and increasing management complexity, especially during peak business periods.

A large pharmaceutical logistics company primarily engages in drug distribution. Due to the wide variety of drugs and strict regional management, the traditional manual picking model is inefficient and prone to errors. As the demand for order splitting continues to grow, the company has introduced 40 AGV transport robots, combined with automated equipment such as automated warehouses, conveyor lines, and robotic arms, to build an efficient and accurate intelligent logistics center, achieving a comprehensive upgrade of storage and distribution processes.

Low picking efficiency and high error rates: Traditional 'man-to-goods' picking methods rely heavily on walking and human judgment, making operations difficult and prone to errors.
Difficulty ensuring timeliness and accuracy: Pharmaceuticals have strict requirements for storage conditions and the timeliness of inbound and outbound operations, and traditional logistics models cannot consistently meet the demand for fast and accurate delivery.
Operational pressure from business growth: The demand for breaking bulk continues to rise, and the existing logistics system lacks sufficient capacity to handle it, becoming a bottleneck for business development.

Low production efficiency: The workshop logistics are complex, with frequent friction between people and materials, and between materials themselves, which limits production efficiency. High labor intensity: The traditional two-shift model involves high labor intensity and repetitive, monotonous work, making recruitment difficult and costly. High transformation risks: During the implementation of intelligent solutions, production stoppage risks must be avoided, and in the early stages there may be challenges such as mixed traffic of people and vehicles and coordination issues among multiple vehicles.

Low production efficiency: The logistics situation in the workshop is complex, with frequent friction between people and materials, as well as between materials themselves, leading to limited production efficiency.
High labor intensity: The traditional two-shift model involves high labor intensity and repetitive, monotonous work, making it difficult and costly to recruit workers.
High transformation risk: During the implementation of intelligent solutions, production stoppages must be avoided, and initially there may be challenges such as mixed operations of people and vehicles and coordination issues among multiple vehicles.

A chain supermarket company in Hubei, operating dozens of stores, faces the challenge of an increasing variety of products, especially fresh produce that demands high timeliness and freshness. The company introduced 50 stealth AGV handling robots and an intelligent management system, achieving an automated upgrade of warehouse logistics and effectively coping with high order pressure and timely requirements.

Difficult to ensure timeliness: Fresh products have high freshness requirements, and traditional manual operation models cannot consistently meet timeliness demands.
Challenges in handling order fluctuations: During peak periods such as holidays, manual handling intensity is high, making errors and delays more likely.
Low warehouse management efficiency: Goods are placed chaotically, searching is time-consuming, and manual errors are common, affecting overall operational efficiency.

In early January 2025, Yuanzi Intelligent urgently deployed an AGV intelligent handling system for a technology company in Harbin. Despite the harsh winter conditions in northeastern China, the project team completed the design, equipment production, and on-site implementation within two months, successfully meeting the client's peak production demands and demonstrating efficient execution and technical service capabilities.

Tight delivery schedule: The complete AGV system needs to be designed, produced, and deployed within two months, creating significant time pressure.
Harsh environmental conditions: The severe cold in Northeast China poses considerable challenges for equipment installation, debugging, and personnel operations.
Approaching production peak: The customer urgently needs the system to go online to cope with the upcoming production pressure, as traditional handling methods cannot meet the demand.