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Smart Manufacturing

SIE MI
Early Warning Center Decision-Making Center Command Center Indicator System Overall product system Early Warning Center (Anticipating Issues) Command Center (Execution Closed-Loop) Decision-Making Center (Analysis & Decision-Making) Customer case Data Governance
Early Warning Center

Management Philosophy: Observe subtle changes, anticipate trends through data, and eliminate risks invisibly.

Scenario Value: Sense and predict abnormal fluctuations in production, supply, and sales within seconds, nipping operational and management risks in the bud.
Functional Features: Multiple types of warning objects, flexible rule configuration, hierarchical push of risk messages, AI-driven root cause tracing, and cross-domain collaboration.
Data Characteristics: Real-time aggregation of global data, millisecond-level stream processing, and multi-source heterogeneity.
Technical Features: Rule engine + deep learning, visual tracking, and personalized cockpits tailored to individual users.


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Early Warning Center Early Warning Center
Decision-Making Center

Management Philosophy: Establish a five-tier system covering "group, company, value stream owner, business domain supervisor, and frontline executor."

Scenario Value: Enable linkage among multi-level decision-making organizations, build consensus, and align goals across all levels.

Functional Features: 100% consistency of indicator data, 3-level drill-down to details, and intelligent auxiliary analysis.

Data Characteristics: Fusion of multi-source heterogeneous data, interweaving of historical and real-time data, and digital twin simulation.

Technical Features: AI-assisted attribution, integration of decision-making and task execution.


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Decision-Making Center Decision-Making Center
Command Center

Management Philosophy: The only manifestation of data value lies in improving task execution and verifying results.

Scenario Value: Ensure rigid closed-loop execution, intelligently summarize experience, and eliminate vague accountability.

Functional Features: Intelligent task recommendations, pre-configured lean templates, and full-cycle traceability of tasks.

Data Characteristics: Real-time penetration of command streams and full-link traceability.

Technical Features: Automatic integration of improvement experience into knowledge graphs to support future optimizations.


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Command Center Command Center
Indicator System

Management Philosophy: Establish standards, integrate data and logic into governance, and turn baselines into benchmarks.

Scenario Value: Connect strategic decoding to execution endpoints, anchor business directions with quantitative metrics, and eliminate vague goals.

Functional Features: AI-driven multi-dimensional penetration diagnosis, supporting closed-loop integration of goals, actions, and verification.

Data Characteristics: Fusion of global heterogeneous data, interweaving of historical, real-time, and predictive data, and traceability of indicator lineage.

Technical Features: Knowledge graphs to build indicator networks, and intelligent engines to recommend optimal baseline ranges.


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Indicator System Indicator System
Overall product system
As the 4th-generation intelligent decision-making platform, SMI signifies the ultimate transformation from data display to an intelligent decision-making brain. It breaks through the static presentation of traditional BI, with "indicator-knowledge hybrid analysis" at its core, constructing a complete decision chain covering "what the figures are → why they are so → what to do about it".


By integrating the four-tier data governance foundation (standards/modeling/indicators/services) and the dual-dimensional depth of knowledge governance (information + theory), it forms an event-logic graph that can precipitate business wisdom. Leveraging AI-driven visualization frameworks, Doris real-time computing, and AI semantic engines, it achieves an intelligent leap from data atoms to decision actions, ultimately delivering directly executable optimization solutions (such as dynamic response strategies for supply chain disruptions). This enables enterprise decision-making to be as precise and traceable as "using a turtle shell oracle to determine the universe's order".


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Overall product system Overall product system
Early Warning Center (Anticipating Issues)

Timely capture key indicator information across all business domains. Based on preset management rules (Know-How) and AI capabilities, it provides real-time warnings of operational risks, automatically attributes causes, and prompts relevant responsible persons to take improvement actions.

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Early Warning Center (Anticipating Issues) Early Warning Center (Anticipating Issues)
Command Center (Execution Closed-Loop)

Assign tasks with supporting templates integrated with concepts like 8D and lean improvement. It manages task progress, tracks execution results, and upon task completion, verifies the closed-loop effectiveness to ensure precise alignment with goals.

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Command Center (Execution Closed-Loop) Command Center (Execution Closed-Loop)
Decision-Making Center (Analysis & Decision-Making)

The management cockpit dynamically simulates target baselines, anchoring budgets and assessments during operational meetings. It decomposes benefit drivers through special reports, quantifies performance management, and outputs decision schemes. It links with the Early Warning Center to calibrate risk thresholds and issues executive guidelines to the Command Center.

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Decision-Making Center (Analysis & Decision-Making) Decision-Making Center (Analysis & Decision-Making)
Customer case
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Customer case Customer case
Data Governance

Management Philosophy: Establish standards with data and shape order through governance.

Scenario Value: Break down data silos, build a trusted data foundation, and support the three centers.

Functional Features: Metadata lineage penetration, quality rule engines, and hierarchical authorized governance.

Data Characteristics: Global heterogeneous integration (OT/IT/ETL), indicator-based conversion, and interweaving of three data states.

Technical Features: AI-driven intelligent cleaning and completion, with dynamic policy engines supporting compliant evolution.


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Data Governance Data Governance
SIE MOM

Comprehensively control the personnel, machinery, materials, methods, and environment involved in the production execution process

By implementing dual state management of manufacturing elements (dynamic and static), flexible production scheduling, optimizing business processes, improving operational efficiency, strengthening data analysis, and saving operating costs can be achieved, laying a solid data foundation for enterprises to achieve intelligent production


SIE iMOM
Advanced Planning and Scheduling

By using APS as the core to horizontally connect business chains and vertically break down information barriers, a dynamic balance system of sales, supply, production, and inventory is constructed, forming a closed-loop collaborative operation mechanism of demand side production end supply chain. A lean driven planning system is established to connect end-to-end value streams and achieve real-time linkage and closed-loop control of the entire value chain.

In plant logistics and driving

Based on supply chain collaboration and lean management, we aim to build a smart logistics platform that integrates supply logistics, production logistics, and sales and service logistics. Comprehensively enhance the ability of enterprise groups to manage multiple factories and warehouses in different locations, and to manage global inventory in a unified and precise manner throughout the supply chain. Realize end-to-end visual and intelligent scheduling of the entire business process from planning, procurement, delivery, production, distribution, fulfillment, and delivery, reduce the overall inventory and logistics costs of the supply chain, and improve supply chain efficiency.

Manufacturing Execution Control

Building an integrated intelligent manufacturing center with "planning, execution, monitoring, and optimization" centered on the control of the entire production process, comprehensively enhancing the production efficiency and quality control capabilities of enterprises. To achieve full process coverage from demand management, production scheduling, production execution, abnormal response, and data analysis, providing enterprises with a closed-loop production management system from work order issuance to product delivery, helping enterprises achieve transparent production, agile response, and continuous improvement.

Quality and Process Control

Building an integrated quality management platform of "prevention, control, governance, and retention" with PDCA closed-loop management as the core, comprehensively enhancing the enterprise's product quality assurance capabilities. Focus on five core competencies: quality prevention, data integration, management visualization, real-time anomaly warning, and improved closed-loop tracking. By covering the entire scenario of "process management, risk prevention, anomaly control, and knowledge retention", we provide enterprises with a closed-loop quality governance system from risk warning to continuous improvement.

Overall product system

Create a scenario based toolchain system using the "platform+industry application" model to achieve data-driven and information collaboration. Based on model driven component-based design, support flexible app selection, and adopt the Center Site architecture to help enterprises achieve "group consistent control, cost reduction and efficiency improvement" in intelligent manufacturing transformation and upgrading


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1+N "full manufacturing chain

Targeting the three major industries of electronics, auto parts, and equipment, covering business such as machining, sheet metal, packaging and testing, assembly, whole machine, and complex assembly

Full business coverage

Business scope: master data, process, planning, logistics, manufacturing, quality, equipment, data acquisition, operation and maintenance, environmental safety, manufacturing operations

Design and Manufacturing Integration

Integrating process planning and design, process simulation, and utilizing public manufacturing engineering management to facilitate rapid information flow and change response between design and manufacturing horizontally

Integration of IT and OT

Manage engineering data collection, recipe management, interface mapping with external IOT or SCADA, form status monitoring, message warning and other businesses

Group KPI system

Group based unified management indicator system, collecting data from various bases to form a data lake, predicting development trends, root cause analysis, and constructing digital twins

Meta model driven

Targeting business objects, modeling first and then instance, forming a comprehensive data value network throughout the entire lifecycle, providing data from the same source, and achieving a seamless process

Center Site architecture

Unified data model, unified application platform, support for COE central governance, on-demand cloud edge elastic deployment, support for disaster recovery, archiving, high availability and other solutions

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SIE INTERNATIONAL TECHNOLOGY CO., LIMITED
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Company Name：SIE INTERNATIONAL TECHNOLOGY CO., LIMITED
Global Headquarters：45th Floor, Guangsheng International Building, 17 Zhujiang West Road, Tianhe District, Guangzhou
International Headquarters：Unit 605, Building 12W, Hong Kong Science Park, Shatin, New Territories, Hong Kong
Service Hotline：400 968 5633
Headquarters Front Desk：020-38878880
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