Tencent Cloud Manufacturing China Translated from Chinese

Ruitai Magang (瑞泰马钢)

Real-time Analytics

Ruitai Magang needed to connect disconnected factory systems, equipment, and decision data so it could manage refractory-material production with finer control and support lean manufacturing instead of relying on siloed information.

Inventory turnover improved 30%.
On-time delivery reached 100%.
Process cost fell 15%-20%.
Product qualification rate improved 3%-5%.

Reported impact

Business outcome

Ruitai Magang gained tighter process control, better visibility across production and operations, and measurable improvements in cost, yield, inventory, and delivery performance.

  • Cost Reduction
  • Productivity Improvement
  • Scalability

Solution

Ruitai Magang built a transparent-factory platform with Tencent Cloud to connect refractory-material production, operations, and management on top of an industrial internet foundation. The implementation uses a smart-data middle platform to collect lifecycle production data, then combines digital twins, dynamic 3D visualization, AI analysis, IoT integration, and BI to monitor process state across space, time, and equipment status. Tencent's Smart Energy Platform serves as the core coordination layer, while the IoT platform integrates intelligent equipment, multi-level business systems, and the finance ERP system into one data environment. The resulting data flows support intelligent warehousing, automated batching, precision pressing, product lifecycle control, and transparent tracking of logistics, information, and value flows. Instead of leaving plants and systems disconnected, the platform provides a unified view for lean manufacturing and more precise decision-making.

Data flow

Shop-floor, equipment, ERP, and operational data are captured through the IoT and data-middle-platform layers, visualized through digital-twin and BI services, and then used to coordinate warehousing, batching, pressing, lifecycle control, and enterprise operations.

Solution architecture

6 components · 4 layers
  1. Ingest
    • Tencent IoT Platform Connects intelligent equipment and operational systems into the shared data environment.
  2. Storage
    • Smart Data Middle Platform Collects and organizes lifecycle production data across the manufacturing process.
  3. Serving
    • Transparent Factory Platform Provides the user-facing operating model for unified manufacturing visibility and control.
    • Digital Twin Visualizes the factory and process state in a dynamic 3D environment.
    • Business Intelligence Analyzes logistics, information, and value flows for lean operations decisions.
  4. Orchestration
    • Smart Energy Platform Acts as the core coordination layer for industrial operations and connected systems.

Architecture clues

  • The design combines industrial internet methods with digital twins, dynamic 3D visualization, AI analysis, and BI.
  • The factory integrates equipment systems, business systems, and ERP into one IoT-connected data environment.
  • The solution synchronizes spatial, temporal, and status data across key process steps.

Evidence from the source

Before the project, data was trapped across different systems, vendors, and devices, which limited fine-grained management and decision support. (original: 数据烟窗式,存在不同系统、不同厂家、不同设备之间的数据的互联互通,数字化对经营决策助力有限,无法做到精细化经营管理。)
Ruitai Magang combines industrial internet technology with high-temperature refractory production and uses a smart data middle platform, digital twins, dynamic 3D visualization, and AI analysis to monitor the full process lifecycle. (original: 瑞泰马钢...将工业互联网应用技术与高温耐火材料生产工艺流程相结合,依托智能数据中台...利用数字孪生、动态3D大数据可视化、AI 数据应用分析等技术。)
The project delivered measurable gains in process cost, yield, inventory turnover, and on-time delivery. (original: 工序成本下降15%~20% 产品合格率提升3~5% 存货周转率提升30% 交付及时性达到100%。)
The transparent factory uses Tencent Cloud's Smart Energy Platform as its core and integrates intelligent equipment systems, information systems, and the finance ERP system into the Tencent IoT platform. (original: 瑞泰马钢透明工厂以工业互联网为基础、以腾讯云智慧能源平台为核心,将智能装备系统、各层级信息系统、财务 ERP 系统集成接入腾讯物联网平台。)
Transparency Evidence & methodology 100/100 reference value · 79% confidence · 1 source

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

01

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 confidence79%
  • 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 transparency8/10
    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.
02

Traceability

Sources & provenance

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

  1. Tencent Cloud

    Ruitai Magang (瑞泰马钢) customer story

    First-party
    https://cloud.tencent.com/customer/rtmg
    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
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.