BOM Smart Comparison Agent
Overview
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Product introduction Manufacturing enterprises often use multiple business systems such as PDM, SAP, and ERP in design, process, and production, which causes version fragmentation and manual entry deviations in different systems for data such as material codes, specifications, quantities, and suppliers. Once the information flows inconsistently into the production process, it is easy to cause serious problems such as wrong materials, rework, and delivery delays. Material comparison agents are specially designed to solve such cross-system data consistency pain points. The product uses a three-layer AI architecture of “perception-cognition-decision”, automatically analyzes drawings and BOM documents through high-precision OCR, combines AI comparison engines with production orders such as SAP to perform multi-dimensional field level matching, and has built-in closed loops of manual confirmation feedback and self-learning. The system supports lightweight docking of standard APIs, and can automatically verify material consistency across the entire link from drawing storage to production and delivery without modifying the existing IT architecture.
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Resolve core business pain points
- Material data inconsistency across systems: AI multi-field comparison engine automatically aligns PDM/SAP/ERP and other system data and outputs a list of differences in real time
- Drawing analysis relies on manual entry: self-developed OCR+ image document analysis model, automatically extracts material information, field extraction accuracy exceeds 95%
- Difference review takes time and is easy to miss: visual difference reminders + closed loop of user confirmation feedback, and the misjudgment rate continues to decline with use
- High system integration cost and long cycle time: Provides standardized API interfaces to support rapid docking of PDM, SAP, TC and other systems without underlying restructuring
- Why choose this Agent
- Cost reduction and efficiency: drawing processing and verification costs are expected to be reduced by 30% +, and manual comparison time is reduced by 50% +
- Business empowerment: break through design-process-production data breakpoints, establish a closed loop of consistent BOM management, and support digital process transformation
- Zero AI threshold: Business personnel can complete comparison and confirmation through a visual interface, no algorithm or development background required
- Continuous evolution: built-in human-in-the-loop mechanism, and comparison accuracy steadily improves with actual business use
- Applicable customers and typical scenarios
- Process/technical departments of manufacturing enterprises: they need to process a large number of drawings and BOM maintenance on a daily basis. We hope that automation will replace manual verification and improve data accuracy.
- Enterprise informatization and integration teams: Focus on system scalability, connection costs and data security, and need standardized interfaces to quickly integrate into existing IT architectures.
- Digital factory/intelligent manufacturing project team: To promote design and manufacturing collaboration, material standardization and process digitalization, and AI capabilities are needed to improve the level of process automation.
- Typical application links: material verification of engineering changes, BOM comparison of new product imports, verification of supplier incoming materials, and synchronization of material master data across plants.
Highlights
- High-precision structured analysis of drawings: Self-developed OCR+ image document recognition model supports automated analysis of PDF scans. The field extraction accuracy is over 95%, greatly reducing manual entry workload.
- Multi-dimensional intelligent material comparison: The AI comparison engine supports multi-field matching such as name, specification, quantity, manufacturer, etc., visualizes differences, and automatically identifies material inconsistencies between design and production.
- Self-learning misjudgment optimization mechanism: A closed loop of user confirmation feedback is introduced to record manual correction results and fine-tune the model, and the misjudgment rate continues to decline with use.
Details
Pricing
BOM Smart Comparison Agent
Usage costs (3)
Dimension | Cost/hour |
|---|---|
c5.large Recommended | CN¥2.50 |
c5.2xlarge | CN¥8.11 |
c5.xlarge | CN¥4.51 |
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Usage information
Delivery details
64-bit (x86) Amazon Machine Image (AMI)
Amazon Machine Image (AMI)
An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.
Version release notes
Function 1: Structured analysis of drawings Based on the self-developed OCR+ image document analysis model, the drawing content in the PDF scan is automatically recognized, key fields such as material name, specification, quantity, and manufacturer are extracted, and structured storage is completed. The field extraction accuracy rate is over 95%, and there is no need for manual entry item by item. Highlights: Supports user confirmation feedback and model fine-tuning mechanisms. The system continuously records correction results, and the analysis accuracy continues to improve with use.
Function 2: Multi-dimensional material information comparison The AI comparison engine matches structured drawing data with material information in SAP production orders in multiple fields, covering dimensions such as name, specification, quantity, manufacturer, etc., automatically identifies differences and highlights reminders in a visual manner to help users quickly locate inconsistencies.
Highlights: Field-level difference identification is supported. The difference results are traceable and exportable, which facilitates audit and problem location, and reduces production errors caused by mismatch of information. Function 3: Self-learning and continuous optimization A closed-loop mechanism for user confirmation and feedback is introduced to record manual correction results and trigger model fine-tuning, forming a self-learning link of “use, feedback, and optimization”. The misjudgment rate continues to decrease with the usage cycle, reducing the frequency of repeated manual intervention. Highlights: There is no need for manual retraining, the system automatically accumulates and optimizes in daily business use, and long-term stability continues to improve. Function 4: Enterprise system integration It provides a standard API interface and supports quick connection with enterprise systems such as PDM, SAP, and TC without large-scale transformation of existing architectures. Open up data links between archives, design systems and production systems, and establish a closed loop of design and production consistency. Highlights: Lightweight deployment, low integration costs, business personnel can complete operations through the interface, and no AI technical background is required.
Additional details
Usage instructions
After starting the instance:
- Configure an elastic public IP and open port 80 of the security group
- Visit http://公网IP/materialRecognition/
- Click “Model Configuration” in the upper right corner, select the model type (OpenAI or Amazon Web Services Bedrock), fill in the corresponding parameters, and save.
- Upload two BOM images and click “Extract Data” to start the comparison.
Support
Vendor support
Amazon Web Services infrastructure support
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