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 (OCI) that support a manufacturing-aware data lake that stores structured, semi-structured, and unstructured data coming from a variety of sources.

Data Contextualization and Preparation

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 MES, Quality Management, LIMS, ERP, SCM, HCM, and CRM, to 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 SAX (Symbolic Aggregate approXimation) 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 pre-seeded 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 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, and 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 key performance indicators 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 job on hold or create quality non-conformance) 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 period, 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 impacted customers.
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