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What is OPUS?

OPUS stands out as a premier no-code AI platform designed specifically for the industrial sector, enabling users to effectively model both equipment and processes. By utilizing OPUS, organizations can access a wide array of benefits including insights for process optimization, predictive maintenance, reduced energy consumption, enhanced productivity, precise forecasting, greater asset reliability, diminished maintenance expenditures, improved planning capabilities, and valuable ESG reporting with carbon reduction insights. Teams can leverage this platform without needing any programming skills, allowing them to extract insights from their data and anticipate future scenarios effortlessly. Dive deeper into your asset's data like never before, revealing unforeseen correlations that may not have been immediately apparent. Conducting root cause analysis on specific components can help direct your efforts where they matter most, ensuring efficient use of resources. The automated AI predictive insights provided by OPUS enable strategic planning for interventions and empower you to make well-informed business decisions confidently. With the capability for rapid deployment and receiving AI model results in mere minutes after creation, organizations can harness the full potential of their existing operational data, leading to tangible returns on investment with the expertise of their current team of asset engineers, operators, and maintenance managers. Transform your entire facility, plant, or work site by tapping into the remarkable potential of automated AI and witness the significant improvements in operational efficiency and effectiveness. Seize the opportunity to elevate your industrial processes to new heights with OPUS.

What is IBM Operations Analytics?

IBM® Z® Operations Analytics is an advanced solution crafted to streamline the exploration, visualization, and examination of vast structured and unstructured operational datasets in IBM Z settings, which include log files, event logs, service requests, and performance indicators. Leveraging your analytics platform in conjunction with machine learning can significantly improve enterprise insights, allowing you to detect workload complications, reveal underlying challenges, and accelerate the process of root cause analysis. The integration of machine learning plays a crucial role in establishing a standard for normal system operations, which aids in the swift identification of operational irregularities. Moreover, it empowers you to recognize emerging issues across a range of services, facilitating timely alerts and cognitive adjustments to shifting circumstances. This robust tool provides expert guidance for corrective actions, thereby improving overall service reliability. It also assists in detecting unusual workload trends and brings to light frequent issues that might be hidden within operational data. Ultimately, it reduces the timeframe required for root cause analysis, effectively leveraging the rich domain expertise of IBM Z and applying its insights within your analytics strategy. As a result, organizations can cultivate a more agile and resilient operational landscape, enabling them to respond more adeptly to challenges. By fully utilizing these capabilities, businesses can not only enhance their operational efficiency but also foster innovation and adaptability in a rapidly changing environment.

Media

Media

Integrations Supported

AVEVA PI System
AWS App Mesh
Aspen HYSYS
Azure Industrial IoT
ControlST
FactoryTalk Historian
Microsoft Entra ID
PowerLogic ION EEM
Siemens APM

Integrations Supported

IBM Z

API Availability

API Availability

Pricing Information

Competitive pricing structure based on number of sensors. Included unlimited users and AI model development.

Pricing Information

Pricing not provided
Free Trial Offered?

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

VROC

Date Founded

2016

Company Location

Australia

Company Website

vroc.ai/products/opus/

Company Facts

Organization Name

IBM

Date Founded

1911

Company Location

United States

Company Website

www.ibm.com/products/z-operations-analytics

Categories and Features

Artificial Intelligence

Machine Learning
Predictive Analytics
Rules-Based Automation

No-Code Development

AI-Assisted Development
Data Aggregation and Publishing
Deployment Management
Drag & Drop
Integrations Management
Iteration Management
Performance Monitoring
Templates
Visual Modeling

Root Cause Analysis

Not specified

Categories and Features

Log Analysis

Not specified

Operations Management

Not specified

Root Cause Analysis

Not specified

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