Overview of Oracle MOC

Overview of Oracle Manufacturing Operations Center 

Oracle Manufacturing Operations Center (MOC) is a prebuilt, flexible, and extensible solution that improves your plant performance by collecting data from disparate plant floor systems, contextualizing them with business context, and provides real-time intelligence on plant floor operations. Oracle Manufacturing Operations Center is the foundation for continuous improvement. Key components of this application include:

• A data model based on ISA-95 industry standard and extensible attribute framework.
• Pre-built extensible adapters for Oracle EBS.
• A robust contextualization rules engine to convert raw shop floor data into meaningful business information.
• Pre-built key performance indicators and analytics, delivered in Oracle's industry leading, easy to configure and extend Business Intelligence (BI) technology.
• Ability to monitor production performance in real time.

Oracle Manufacturing Operations Center provides new capabilities to drive sustainability initiatives. Sustainability Aspect is a generic name used for continuous inputs consumed by companies to be able to operate and create goods or services. Common examples of sustainability aspects are electricity, gas, oil, water, helium etc. The Sustainability Sensor Data Management system of Oracle MOC helps you meet the legislative requirements and social responsibilities to go green and accelerate sustainability improvements of companies focusing on manufacturing, innovation, or services, and enables you to:

• Monitor and analyze energy consumption, energy cost, and carbon emissions with dashboards and built-in key performance indicators (KPIs), leveraged by Oracle
• Business Intelligence Enterprise Edition (OBIEE) technology.
• Integrate with smart meters, environmental management systems.
• Build automation systems and collect energy consumption and emissions data.
• Identify specific opportunities for improving energy efficiency and reduction in carbon emissions.
• Normalize and correlate energy consumption to operating conditions and production variables, and enable energy-aware manufacturing.
• Leverage as a certifiable data repository for energy usage reporting based on Global
• Reporting Initiative (GRI) framework and Green House Gas (GHG) protocol.

Technology Overview

• Oracle Data Warehouse Builder (OWB)
• Oracle Business Intelligence Enterprise Edition (OBIEE)
• Oracle E-Business Suite (EBS) Foundation

Connectivity

Oracle MOC supports heterogeneous systems including device data and provides wide range of options for collecting shop floor data, directly from Programmable Logic Controllers (PLCs), Supervisory Control and Data Acquisition (SCADA) systems, Meters, Sensors and Distributed Control Systems (DCSs). Oracle MOC leverages Oracle Warehouse Builder (OWB), the data warehousing tool to extract and process data. A concurrent program is used to process meter readings for sustainability aspect consumptions.

Setting Up Manufacturing Operations Center

The Oracle Manufacturing Operations Center (MOC) setup steps span several Oracle systems including Oracle Business Intelligence Enterprise Edition (OBIEE), Oracle Warehouse Builder (OWB), and the MOC setup pages:

• OBIEE provides analytics from data spanning enterprise sources and applications.
• OWB provides data extraction, and transformation and loading from source systems into the MOC environment.
• The MOC Administrator role is used to setup Sustainability Aspect, Emission Factors, Tags, Meters, Entity Hierarchy, and setup contextualization methods for business context, and create and associate attribute groups and attribute data entities. It can also be used to create meters, run concurrent program to process meter readings, and use extensible attributes workbench to setup classifications and attributes for user-defined attributes.
• The MOC User role is used to view and integrate data collection, and perform analytic reporting. User can use extensible attributes workbench to create attributes and attribute groups, and associate them with classifications.