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Achieve Operational Excellence Using Machine Learning and AI

AI Apps for Manufacturing

Gain Actionable Insights into Operational Inefficiencies

Analyze key patterns and correlations that are related to manufacturing operations.

Perform Rapid Root-Cause Analysis

Trace manpower, machine, material, method, and management-related information.

Proactively Address Potential Risks

Predict the probability of critical events and take proactive measures to address them.

Assess and Mitigate Business Impact

Identify impacted products, processes, suppliers, and customers, and take actions to mitigate risks.

Explore AI Apps for Supply Chain & Manufacturing

Product Features

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Data Acquisition and Storage

    Data from Machines and Equipment

  • Leverage automated and manual upload capabilities to ingest data from sensor-enabled equipment, machines, and facilities on the shop floor.
  • Data from Enterprise Applications

  • Ingest data from transactional applications such as MES, Quality Management, LIMS, ERP, SCM, HCM, and CRM.
  • Embedded Data Management Platform

  • Utilize embedded Oracle PaaS technologies across database and big data stacks running on Oracle Cloud Infrastructure that support a manufacturing-aware data lake, storing structured, semistructured, and unstructured data from a variety of sources.

Data Contextualization and Preparation

    Operational Technology (OT) and IT Data Contextualization

  • Use inbuilt capabilities to contextualize data coming from sensor-enabled machines and equipment (OT data) with transactional data (IT data) coming from applications such as MES, Quality Management, LIMS, ERP, SCM, HCM, and CRM. Get a comprehensive snapshot of the manufacturing state at any given point in time.
  • Sensor-Time-Series Data

  • Convert continuous streams of sensor-time-series data from machines and equipment into time-window aggregates using Symbolic Aggregate approXimation (SAX) to facilitate machine-learning analysis.
  • 5M Data Preparation

  • Organize the massive data present in the data lake into 5M categories (manpower, machine, method, material, and management) with a preseeded library of attributes from Oracle applications (as well as custom attributes) to facilitate comprehensive analysis of the entire manufacturing process.

Model Lifecycle Management

    Model Creation

  • Leverage simple and intuitive user interfaces to allow data scientists to create an unlimited number of descriptive and predictive models for analyzing key performance indicators (KPIs) such as yield, quality, cycle time, scrap, rework, and cost.
  • Model Training and Deployment

  • Continuously train models with historical training data sets to attain the required accuracy levels and scores. One-touch deployment allows selected models to be immediately deployed for monitoring ongoing manufacturing processes.
  • Model Performance Evaluation

  • Evaluate accuracy of predictive models using a confusion matrix by comparing predicted values with actuals. Continue to refine the models for improved accuracy.

Patterns and Correlations Analysis

    5M Input Factors

  • Analyze 5M-related information from manufacturing operations to understand the impact on key business outcomes.
  • Top Influencing Factors

  • Identify the factors and variables in the manufacturing environment that have the highest influence on key performance metrics.
  • Patterns and Correlations from Historical Data

  • Identify the relationship between a multitude of influencing factors and variables from the manufacturing process that affect KPIs such as yield, quality, cycle time, scrap, rework, and costs.

Predictive Analysis

    Critical Outcomes During Manufacturing

  • Compare current manufacturing conditions against suspect patterns from historical data analysis to predict potential yield loss and product defects.
  • Prediction Alert Rules

  • Configure the application to receive alerts for predictions that match specific conditions such as probability and product context.
  • Downstream Orchestration

  • Subscribe to published REST services for predictive alerts (for example, put the job on hold or create quality nonconformance) to create transactions in other applications.

Genealogy and Traceability Analysis

    Self-Guided Navigation for Traceability

  • Using an intuitive, graph-based navigation, traverse back the entire manufacturing process to identify 5M-related information.
  • Time-Window Traceability

  • For any window of time, view all relevant manufacturing events such as machine sensor reading anomalies, alarms/alerts, quality test results, and work order start/stop, as well as status changes such as released and on hold.
  • Impacted Products and Customers

  • Trace forward from any combination of manufacturing factors to identify products made under those conditions and the impacted customers.
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