Precision Manufacturing Digital Transformation
A world-leading precision fastener and hardware manufacturer. Under the wave of global supply chain restructuring, ordering trends from European and American clients have shifted toward "small-batch, multi-delivery, rush orders." Facing millions of specification combinations and extreme delivery pressure, the traditional "experience-driven manual" operating model has reached its limits.
How to thoroughly transform from "reactive response" to "AI predictive decision-making" in an ever-changing international market is the core challenge for this company to consolidate its global leadership.
Four Key Technical Highlights
Highlight 1 — Omnichannel Opportunity Consolidation: AI Voice Assistant Precisely Extracts Customer Intelligence
Integrating LINE, WhatsApp, and other multi-channel communications. Sales staff simply speak their input; AI automatically performs speech-to-text and precisely extracts key opportunity indicators from conversations — "requirement specifications, customer budget, rush-order attributes" — auto-tagging and syncing to CRM to prevent missed opportunities.
Highlight 2 — AI Visual Parsing & RAG Quotation Engine: Unlocking 8.4 Million Specification Blind Spots
Facing 8.4 million specification combinations and outsourcing variables, the system uses AI vision to automatically scan and parse customer engineering drawings (shape, dimensions, etc.), combined with RAG (Retrieval-Augmented Generation) to instantly retrieve similar historical transaction records and supplier costs, auto-generating quotation recommendations with optimal margin ranges.
Highlight 3 — Dynamic Order Intake Control: "Risk Traffic Light" for Capacity and Delivery
Real-time integration with ERP inventory and MES production line load. When sales prepares to accept an order, AI automatically manages: green light (capacity sufficient, accept immediately), yellow light (bottleneck exists, suggest staggered delivery), red light (capacity at maximum, suggest declining or renegotiating), ensuring every order is delivered on time while maintaining profitability.
Highlight 4 — Real-Time Overview: The Ultimate Copilot for Cross-Department Collaboration
Built on a Copilot philosophy, providing executives with complete real-time situational awareness. Quotation approvals, scheduling planning, and customer complaint handling are "cardified"; AI automatically aggregates departmental progress and proactively pushes anomaly alerts, establishing a data-driven conversational decision-making mechanism.
Twelve Core Smart Features
From order intake to delivery — the AI-powered precision manufacturing digital command center
Screw Visual Recognition
Upload screw drawing or photo; AI auto-identifies item and specifications
Automatic Specification Parsing
Part number, material, thread, tolerance, certification — structured in one step
RAG Smart Quotation
Compare historical transactions; calculate optimal quote in seconds
Gross Margin Optimization
AI recommends unit price and margin, balancing competitiveness and profitability
Explainable Quotation Logic
Which factors affect cost and by how much — fully transparent and traceable
Dynamic Order Traffic Light
Real-time capacity and delivery risk control with green/yellow/red light alerts
ERP/MES Real-Time Integration
Material inventory and production line load dynamically linked to order decisions
Delivery Risk Early Warning
Detects bottlenecks; auto-suggests staggered delivery or renegotiation
Omnichannel Opportunity Capture
AI voice assistant precisely extracts customer intelligence — no opportunity missed
Specification Blind Spot Breaker
Navigate 8.4 million specification combinations to find the right product
Continuous ML Model Training
Learns from historical transactions; quotation logic iterates and improves over time
Cross-Department Real-Time Copilot
Quotation/scheduling/complaints cardified; proactively pushes anomaly alerts
Screw Visual AI Recognition → Smart Quotation
Upload a screw engineering drawing; AI auto-parses specifications, compares historical transactions, and generates an optimal margin quotation recommendation
① AI Specification Analysis
② RAG Match — Historical Transactions (Similarity)
Based on analysis of 3 aerospace-grade titanium alloy historical quotes, accounting for Ti-6Al-4V raw material costs, AS9100/NADCAP certified process premium, and volume discount, AI recommends a quote of €1.34/pc — balancing aerospace client competitiveness with a reasonable margin.
RAG Smart Quotation · Real-Time Cost Factor Breakdown
Adjust the specifications and instantly see how each factor affects unit cost; RAG compares historical transactions to generate a quotation
AI Quotation Model · Full Training Pipeline
The model learns from historical transactions to identify which factors affect unit cost and by how much — making quotation logic traceable, explainable, and iteratively improvable
- Data Generation
- Feature Engineering
- Model Training
- Prediction Validation
| Part No. | Material | Qty(K) | Customer | Unit€ | Margin |
|---|
M6 × 1.0 · AlMg3
Order Qty 2M pcs
Customer BMW AG
Frequently Asked Questions
Screw/fastener quotation takes a long time — how fast can AI do it?
With 8.4 million specification combinations, how do you find the right product?
How do you set quotation margins? Will accepting orders result in losses?
Will accepting orders exceed production capacity or delivery timelines?
Will the quotation model become more accurate over time?
Contact US
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Email | [email protected]
- Phone | 02-55687660