Huawei Cloud Manufacturing Nantong, China Translated from Chinese

Nantong High-Tech Holding Group

Supply Chain Optimization

Nantong High-Tech Holding Group needed a trusted way to circulate design, image, and production data across the home-textile ecosystem so companies could reduce duplicated material spend, protect IP, and attach AI services to shared industry data.

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annual revenue about 2 billion yuan

assets under management about 50 billion yuan

cost reduced by more than 85%

defect detection rate increased by 80%

Reported impact

Business outcome

The trusted data space activated data sharing across the local home-textile cluster and produced measurable gains in design speed, cost control, and defect detection for participating enterprises.

Solution

Nantong High-Tech Holding Group worked with Huawei Cloud Stack to build a trusted data space for the home-textile industry and pair it with AI innovation scenarios. The architecture follows a '1+6+N' design. The shared data-circulation infrastructure combines MRS, DWS, and DataArts Studio for data processing and product development, then adds EDS data-use control, TICS privacy computing, and BCS blockchain evidence and audit for trusted circulation. A six-party trust framework coordinates platform operators, data providers, demanders, service providers, and supervisory roles. On top of that governed base, the group landed services such as a high-quality image library, IP protection, industry benchmarks, smart quality inspection, and a smart logistics network. The solution turns dispersed design and production data into controllable, auditable data products and feeds AI-enabled applications that help manufacturers reduce design waste, inspect defects, and coordinate the textile chain.

Data flow

Design assets, images, and other home-textile data are ingested into the trusted data-space infrastructure, processed through MRS, DWS, and DataArts Studio, and then circulated under EDS, TICS, and BCS controls. Those governed datasets are used by the home-textile large model and downstream services such as image libraries, quality inspection, and logistics applications.

Solution architecture

7 components · 5 layers

Compute
- Huawei Cloud Stack Provides the trusted data-space infrastructure for the home-textile industry.
- home-textile large model Adds AI capability to industry scenarios such as image libraries, quality inspection, and logistics coordination.
Storage
- MRS fused data lake Supports the processing and development of heterogeneous home-textile data products.
- DWS Stores and structures industry data for shared analytics and product development.
Serving
- smart quality inspection Uses AI on trusted home-textile data to identify defects and improve manufacturing quality.
Orchestration
- DataArts Studio Governs data development and lifecycle management across the textile data space.
Governance
- EDS, TICS, and BCS trusted-circulation services Control data use, privacy computation, and blockchain-based evidence and auditing for cross-party circulation.

Architecture clues

Data circulation combines processing, governance, privacy computing, and blockchain evidence.
The architecture is explicitly described as a 1+6+N trusted data-space system.
The solution is positioned as a national-scale data-market carrier for home textiles.
The trust model spans six participant roles across the ecosystem.

Evidence from the source

"Data space plus AI reduced design cycles by 90%, cut cost by more than 85%, and increased defect-detection rate by 80%. (original: 通过数据空间与AI双轮驱动... 企业实现设计周期缩短90%、成本降低超85%、瑕疵检出率提高80%。)"

"Nantong High-Tech Holding Group released the country's first home-textile large model and is building a China Data Home Textile City. (original: 南通高新控股集团是一家国有独资企业... 发展数字经济，发布国内首个家纺大模型；... 立足南通高新区，打造“中国数据家纺城”。)"

"The group and Huawei built a 1+6+N home-textile trusted data space. Data processing uses MRS, DWS, and DataArts Studio, while circulation relies on EDS, TICS, and BCS. (original: 南通高新控股集团联合华为云Stack，打造“1+6+N”体系的家纺可信数据空间... 加工能力基于融合数据湖MRS、数据仓库DWS、数据治理中心DataArts Studio建设... 流通能力依托EDS数据使用控制及连接器管理、TICS隐私计算、BCS区块链存证与审计等服务。)"

"The trusted data space has already landed scenarios such as a high-quality image library, integrated IP protection, industry benchmarks, smart quality inspection, and a smart logistics network. (original: 围绕家纺产业链挑战，落地高质量图库中心、一体化知识产权保护、家纺行业标尺、智能质检、智能物流网络等创新场景。)"

Transparency Evidence & methodology 100/100 reference value · 81% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value100/100

Problem clarity20/20
Measures whether the customer's operational problem is specific enough to compare.
Implementation depth25/25
Rewards named components, data flow, integrations, and architecture detail.
Outcome specificity20/20
Measures whether the reported benefit is concrete rather than a generic claim.
Quantified impact25/25
4 metrics describe measurable impact.
Deployment scale10/10
Rewards evidence of a live or scaled deployment and meaningful integration scope.

Evidence confidence81%

Source authority20/25
The main evidence is an official vendor-published customer story.
Claim traceability21/25
10 of 12 key fields link to a named source.
Direct evidence20/20
4 direct source quotes retained.
Date provenance0/10
The publication date has no documented provenance.
Source transparency10/10
1 source reference document publisher, URL, authority, retrieval, and supported fields.
Implementation specificity10/10
Rewards source-backed components, data flow, quantified impact, and a specific outcome.

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

Huawei Cloud
华为云Stack-南通家纺-华为云

First-party
https://www.huaweicloud.com/cases/ntgx.html

Source type
Vendor-published customer story
Supports
- Customer
- Business problem
- Solution
- Products
- Solution components
- Data flow
- Integrations
- technical area
- use case category
- Business outcome
- outcome category
- Metrics
- Architecture
- evidence quotes

Date evidence

First archived Apr 13, 2026 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.
Open date source

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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