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1. Receiving 2. Shelving 3. Picking 4. Storage 5. Sorting

Intelligent Warehouse Process Solutions

1

Receiving

Intelligent Receiving Process

Competitive Advantages

  • Cost Optimization: Labor reduced up to 70%; ROI within 12 months.
  • Ideal Use Cases: Full-pallet/full-carton unloading, carton-level palletizing.

Industry Overview

Inbound receiving involves manual quality checks, unloading, palletizing, barcode binding, and system data entry. Workers manually verify, palletize, bind barcodes, and check-in goods upon arrival.

Key Challenges

  • Efficiency Bottlenecks: Time-consuming, manual processes causing congestion, especially at peak times.
  • Data Silos: Manual data entry causes delays, errors, compromising accuracy.
  • Cost Pressure: Labor constitutes up to 60% of costs; difficulty retaining qualified staff.

Our Solution: Intelligent Receiving System

AI-Based Visual Recognition

  • 99.9% accurate real-time product/damage identification

Automated Unloading

  • Robotic arms/conveyors autonomously unload cartons directly to conveyors

Automated Palletizing

  • Robotic arms & AMRs fully automate unloading, scanning, palletizing
2

Shelving

Intelligent Shelving Process

Competitive Advantages

Our intelligent shelving solution optimizes product placement with dynamic slotting algorithms that reduce travel time and increase storage density, while continuously adapting to changing inventory patterns.

Industry Overview

Traditional shelving operations rely on forklifts, manual labor, and semi-automated WMS recommendations. Inefficient storage allocation leads to poor picking efficiency and slow fulfillment.

Key Challenges

  • Labor Dependence: High worker training time and turnover affect stability.
  • Low Efficiency: Excessive walking significantly reduces throughput.
  • Chaotic Storage: Mixed SKUs reduce picking efficiency by 20–40%.

Our Solution: Intelligent Shelving

Heatmap-Based Slotting Optimization

  • Dynamic heatmaps prioritize frequent SKUs, reducing picking times

3D Volume Optimization

  • Advanced modeling optimizes storage, increasing utilization by up to 50%

AMR-Enabled Goods-to-Person Shelving

  • Autonomous robots deliver goods directly to workers, reducing labor fatigue
3

Picking

Intelligent Picking Process

Competitive Advantages

  • +200–300% picking efficiency
  • <0.05% error rate
  • Elastic robot scaling; ideal for complex SKU e-commerce/retail

Industry Overview

Picking relies heavily on manual paper orders/terminals, causing low efficiency, errors, and fatigue. Traditional systems (shuttle, rotating racks) have poor scalability and ROI.

Key Challenges

  • Low Efficiency: 3–5 min/order; 2–3% error rate.
  • High Labor Intensity: Workers walk over 15km/day; up to 30% turnover.
  • Fragmented Systems: Difficulty dynamically allocating resources during order peaks/valleys.

Our Solution: AI Dynamic Picking System

Multi-Robot Coordination

  • AMRs/picking robots dynamically collaborate, maximizing speed/accuracy

Real-Time Path Optimization

  • Algorithms optimize picking paths under 1 sec response
4

Storage

Intelligent Storage Process

Competitive Advantages

  • +30–200% storage density
  • Flexible robot deployment scales with demand
  • Optimal for deep inventory/high outbound/moderate SKU variety

Industry Overview

Traditional storage (fixed racking/ASRS) leads to only 60–70% space utilization, with high initial investment and maintenance complexity.

Key Challenges

  • Space Waste: Slow-moving goods occupy prime locations, reducing efficiency.
  • Lack of Flexibility: Racks poorly adapt to changing SKU dimensions; costly reconfiguration.
  • High Energy Consumption: AS/RS consume continuous power; up to 25% operational costs.

Our Solution: AI Intelligent Storage System

Robotic Fleet + Real-Time Optimization

  • AMRs & real-time 3D heatmaps optimize SKU placement, improving throughput
5

Sorting

Intelligent Sorting Process

Competitive Advantages

  • Modular, easily reconfigurable
  • Scalable with demand
  • Ideal for sorting, receiving, and secondary distribution

Industry Overview

Sorting typically relies on manual experience or semi-automatic sorters, leading to limited parcel compatibility, frequent breakdowns, and suboptimal ROI.

Key Challenges

  • Lack of Accuracy: Irregular parcels lead to >5% sorting errors; manual rework required.
  • Limited Throughput Flexibility: Fixed sorter capacity doesn't meet demand spikes; unused off-peak capacity.
  • Complex Maintenance: Mechanical issues frequent; high downtime and costs.

Our Solution: AI Smart Sorting System

Vision-Powered + Dynamic Routing

  • AI identifies parcel details, routes AMRs for efficient point-to-point delivery, reducing travel and improving coordination