Products

aimFactory

GreenES

Intelligent factory operation evaluation system that analyzes and extracts all data based on big data so that plant resources can be operated more efficiently

GreenES Line-up

  • GreenSPC
    GreenSPC is an on-line, off-line SPC (Statistical Process Control) system, It collects production & instrumentation data in real time and performs the function of early detection and warning of abnormal points using statistical data and analysis techniques .
    • Key Features

      • Provide basic Western Electric Rules
        • Zone rules for symmetric data (4 types)
        • R-chart rules for asymmetric data (7 types)
        • Trend rules (2 types)
      • Provide Nelson Rules
        • 8 types of symmetric & asymmetric rules
      • Supports Custom Rule definitions
        • Limit out type
        • Bias type
        • Trend type
        • Oscillation type
        • User defines custom rules and applies in run-time
      • Rule group 정의 및 적용
        • 복수의 rule 을 하나의 rule group 으로 정의하여 적용
        • 규칙 검사를 위해 전체 규칙 그룹 적용
      • Provides 7 types of industry-common Control charts that can be used according to data types and sampling characteristics
        Category Attribute Defect type Chart type Sample Size
        Variable
        (Quality)
        Measurable
        (thickness, temp., pressure,..)
        X, mR Chart
        (Individuals and moving range)
        Sample count per sub group = 1
        Xbar, R Chart
        (Average and range)
        Sample count per sub group 2~10
        Xbar, S Chart
        (Average and STdev)
        Sample count per sub group > 10
        Attribute
        (Defects)
        Countable
        (defect, defective product, ..)
        Defective Unit np Chart
        (number defective)
        sample size constants (Defective GLS per 100 CST)
        p Chart
        (proportion defective)
        sample size varies (Defective GLS per CST)
        Defects c Chart
        (defects per subgroup)
        sample size constants (Defect count per 100 sample)
        u Chart
        (defects per unit)
        sample size varies (Defect counts per specific period)
  • GreenRMS
    A system that prevents process accidents due to wrong recipes by centrally managing the process information of the process equipment recipe (all pre-defined work instructions for processing) and controlling the recipes in real time.
    • Key Features

      • Efficient recipe integration management through remote recipe integration management
      • Provides recipe and key parameter verification function to prevent recipe accidents
      • Provides comparison management function before / after change based on recipe change point
      • Provides approval process for recipe change and creation (approval and notification of change / error)
      • Automatic modeling through recipe upload function
      • Provide Flexible Recipe Editor supporting various recipe structures
    • Architecture

  • GreenRPT
    Stores all data generated in the factory operating system through DW (Data Warehouse) configuration, integrates and collects a lot of data accumulated in the factory by subject from the perspective of users, engineers, and managers, and makes decisions using some analysis and inquiry techniques Systems that support
    • Key Features

      • Provides ETL Tool based on Workflow for data collection and processing (visualized modeling, powerful scheduling function)
      • Provides separate DB schema for data analysis
      • Inquiry and analysis of manufacturing data scattered in various systems through a single system
      • With data mart configuration, users can respond quickly to various queries
      • Provides various reports (performance analysis, quality index, production status, equipment efficiency, etc.)
      • Provides OLAP tools (various types of reports and charts) for unstructured reports
      • Provides real-time monitoring function for ETL job
    • Architecture

  • GreenYMS
    A system that enables tracking of processes and facilities that cause quality defects using various statistical techniques and intelligent algorithms, through DW (Data Warehouse), the data accumulated in the factory and the data of production facilities, measurement and inspection facilities are all integrated
    • Key Features

      • Provides ETL function and data standard system for DW (Data Warehouse) construction
      • Large data storage and high performance in connection with various Big Data platforms
      • Enhancement of analysis function through multi-dimensional analysis based on transmission data and securing quality traceability
      • Prevention of quality accidents through the monitoring system of minute changes in equipment processing conditions
      • Provides statistical-based analysis model and various charts considering process changes (PM, Recipe, etc.)
      • Early detection of abnormal signs of quality through verification of product and equipment identity (dispersion, change in central value, etc.)
      • Tracking of suspected equipment and causal factors using advanced statistics techniques
    • Architecture

  • GreenFMB
    A system that provides a variety of screens so that data such as status monitoring, key performance indicators (KPI), and performance against targets can be aggregated and monitored based on factories, process lines, and major facilities.
    • Key Features

      • Providing components reflecting manufacturing plant characteristics for easy and quick layout modeling
      • Component customization is possible in connection with commercial design tools
      • Integrated factory monitoring based on visualization with high intuitiveness based on web and mobile
      • Real-time display of various information (equipment status, WIP status, alarm, etc.)
      • Provides various types of alarms by quickly grasping the real-time status of factories and major equipment
      • Integrating with various systems, real-time information is visually transmitted to realize improved production operation management
    • Architecture

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