Project Background

The client is a manufacturing enterprise with annual revenue exceeding 5 billion RMB, operating a complete chain from raw material procurement to finished product delivery. As the business scaled, traditional manual operations became a bottleneck—order processing, production scheduling, quality inspection, and logistics heavily relied on human coordination, leading to low efficiency and frequent errors.

Core Pain Points

Slow Order Processing: From receiving a sales order to entering it into the ERP took an average of 4 hours, causing severe backlogs during peak periods.

Experience-Based Scheduling: Production scheduling depended on senior employees' experience, making it difficult for newcomers; suboptimal schedules led to wasted capacity.

Manual Quality Inspection: Quality checks relied on visual inspection by humans, with a missed defect rate of about 5% and frequent customer complaints.

Information Silos: Data across ERP, MES, and WMS systems did not flow automatically; cross-system data entry was done manually.

Solution

We designed and implemented an "AI-powered end-to-end automation" solution for the client:

1. Intelligent Order Processing

NLP models automatically parse order information from multiple channels: email, WeChat, EDI, etc.

AI auto-matches customers, products, and pricing, and creates sales orders in the ERP.

Exception orders (e.g., insufficient credit line) are automatically escalated for manual handling.

2. AI-Optimized Production Scheduling

Intelligent scheduling algorithm based on historical data and constraints (equipment, personnel, materials).

Real-time production progress monitoring, with automatic schedule adjustments for rush orders, equipment breakdowns, etc.

Scheduling results are automatically dispatched to the MES for execution.

3. AI-Powered Visual Quality Inspection

Deep learning-based visual inspection model covering 12 common defect types.

Inspection speed: 200 items/minute; missed defect rate reduced to 0.3%.

Inspection results are automatically recorded and generate quality analysis reports.

4. System Integration

Connected ERP, MES, and WMS via API middleware.

Real-time data synchronization eliminates manual data entry.

Unified dashboard enables management to view end-to-end operational data in real time.

Results Data

Metric	Before	After	Improvement
Order Processing Time	4 hrs/order	3 mins/order	98.7%
Scheduling Accuracy	78%	96%	23%
Missed Defect Rate	5%	0.3%	94%
Manual Operation Steps	12	2	83%
Order Delivery Cycle	15 days	7 days	53%

Technology Stack

AI/ML: Python, PyTorch, Transformers, OpenCV

Backend: Node.js, Python FastAPI, PostgreSQL

Frontend: React, Next.js

Integration: REST API, WebSocket, MQTT

Deployment: Docker, Kubernetes, Private Cloud

Yangtze River Delta Equipment Manufacturing Order-to-Delivery AI Automation Project

Project Background

Core Pain Points

Solution

1. Intelligent Order Processing

2. AI-Optimized Production Scheduling

3. AI-Powered Visual Quality Inspection

4. System Integration

Results Data

Technology Stack

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