Expertise behind intelligent manufacturing,
 Passion behind value creation.
Expertise behind intelligent manufacturing, 
 Passion behind value creation.
Expertise behind intelligent manufacturing,
Passion behind value creation.

    Vezu Intelligent Manufacturing Platform

    Vezu Industrial Intelligence Platform builds a full-chain architecture of "data connection - intelligent engine - scenario implementation". It aggregates full-volume industrial data including sensors, multimodal data, and MES/WMS data, and is driven by three core platforms: machine learning (production decision optimization), deep learning (multimodal perception), and AIGC (knowledge services). Covering over 100 industrial scenarios such as process parameter optimization, intelligent quality inspection, predictive equipment maintenance, and embodied intelligence, it achieves seamless connection from data insights to intelligent decision-making, helping enterprises break through efficiency bottlenecks, accelerate intelligent transformation, and reshape the industrial value creation model.
    特色介绍

    Vezu Intelligent Manufacturing Business

    Centered on Industry 4.0, Vezu leverages its core capabilities to provide key business services in smart manufacturing and supply chains, helping clients enhance efficiency, reduce costs, and strengthen competitiveness.
    特色介绍

    Smart Manufacturing Consulting and Implementation

    特色介绍

    Custom Industrial AI Solutions

    特色介绍

    Smart Manufacturing System Integration and Optimization

    特色介绍

    Supply Chain Agility Services

    Vezu Intelligent Manufacturing Insights

    Shift to Digital Manufacturing Thinking

    Transition from "experience-driven" to a systematic "data + model-driven" mindset, with the core being the transformation of business problems into quantifiable, computable, and iterative mathematical problems. This establishes a causal relationship between "production factors → value objectives" through formulas and algorithms.
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    Smart Manufacturing as Lean Manufacturing

    Eliminate non-value-added work: Remove activities in production processes that do not create value.
    Improve decision quality: Optimize decisions in production, operations, and other links based on accurate information and analysis to enhance efficiency and benefits.


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    Enhance the Agility of Supply Chain

    Supply chain agility refers to the ability to quickly respond to changes in internal and external environments such as market demand fluctuations and unforeseen supply-side disruptions. The core lies in rapid response and flexible handling of uncertainties, enabling enterprises to maintain competitiveness amid changes.

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    Vezu Intelligent Manufacturing Innovation

    特色介绍

    Technical Methodology

    Vezu's innovation in Industry 4.0 is reflected in its unique technical methodology. By organizing 5M1E process knowledge into AI-understandable graphs, we train models based on graph data to replace experience-based decision-making and achieve adaptive unmanned execution.
    特色介绍

    MVP Development Model

    A fast and efficient product iteration approach. Build a usable product prototype in the fastest and simplest way to test whether the product meets expectations. Define goals around the core value and establish AI models to ensure continuous optimization of the product during iteration.
    特色介绍

    Transformation Roadmap

    Vezu has a systematic transformation pathway. Starting with designing an MVP, piloting in a small scope, and gradually promoting after verifying feasibility, we carry out targeted expansion to achieve full coverage of innovation in the entire production system and drive the overall digital transformation of enterprises.
    特色介绍

    Value Tracking Measurement

    Vezu attaches great importance to tracking and measuring innovation outcomes. Through adoption rate and value tracking mechanisms, we quantify the benefits of innovation. For example, in terms of cost savings, specific data on weight savings, labor savings, and energy consumption savings clearly demonstrate the actual value brought to clients by innovation.

    Vezu Intelligent Manufacturing Industry

    Challenges: Complex food safety traceability, large seasonal demand fluctuations, insufficient process stability.
    Solutions: AI quality control systems and dynamic scheduling optimization. Ensure compliance through real-time IoT monitoring and blockchain traceability technologies, and optimize key process parameters through digital twins to achieve dual improvements in inventory turnover and production efficiency.

    Food Industry

    Challenges: Long new drug R&D cycles, strict GMP compliance requirements, and process reliance on experience.
    Solutions: R&D-production integrated technology. Ensure compliance through AI automated review and electronic batch record systems, and shorten R&D cycles and improve product quality stability through Bayesian network real-time optimization of process parameters.

    Pharmaceutical Industry

    Challenges: Disconnection between formula R&D and market demand, low multi-SKU changeover efficiency, and pressure for sustainable transformation.
    Solutions: Consumer insight-driven new product development via AI, intelligent packaging line changeover using visual recognition systems, and material usage optimization through life cycle analysis to support green production.

    Daily Chemicals Industry

    Challenges: Complex multi-vehicle mixed production scheduling, high supply chain disruption risks, and carbon management pressures.
    Solutions: Flexible manufacturing systems and supply chain digital twin simulation. Reduce changeover time through AI dynamic scheduling, optimize global supply chain resilience, and reduce production carbon emissions using carbon efficiency models.

    Automotive Industry

    Challenges: Yield fluctuations, high equipment maintenance costs, and process tuning reliance on experts.
    Solutions: AI defect prediction and root cause analysis models, predictive maintenance to reduce unplanned downtime, and knowledge graph-based autonomous optimization of deposition, lithography, and other process parameters to improve yield and overall equipment efficiency.

    Electronics and Semiconductor Industry

    Challenges: Process safety risks, raw material price fluctuations, and environmental compliance requirements.
    Solutions: Real-time equipment anomaly early warning through multi-sensor fusion models, dynamic procurement strategy optimization integrating futures data, and intelligent control of waste gas treatment systems using mechanism models to ensure production safety and emission compliance.

    Chemical Industry

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