Leverage the Power of Real-Time Edge Data to Keep Assets Running as Planned
With the average cost of unplanned downtime estimated at $100k per hour, manufacturing companies today require insight into the machine behavior that can take down operations without warning. Manufacturers looking to leverage real-time data for predictive maintenance need an edge platform that can automatically connect and collect data from ANY source, normalize and manage the data, perform analytics, and easily integrate with third party applications and cloud computing platforms. OT and IT teams trust Litmus to deliver the critical data they need to enable predictive maintenance to keep manufacturing assets and processes running as planned.
Connect to Any Asset with an Industry-Leading, Pre-Loaded 250+ Driver Library
Litmus delivers unmatched time-to-value by connecting to any PLC, DCS, SCADA, Historian or sensor to collect data for predictive maintenance in just minutes. Our industry-leading, 250+ pre-loaded driver library connects and collects data out-of-the-box with no programming required to monitor assets in real-time. Collect and normalize hundreds of custom data points from any number of assets or facilities with one platform.
Improve Response Time with Real-Time Alerts and Out-of-the-Box Data Analytics
Monitor real-time asset data, set up alerts and take action as needed to improve quality and make better maintenance decisions. Litmus offers pre-built and ready-to-use KPIs including asset utilization, uptime, downtime, anomaly detection and more. Ready analytics based on common manufacturing KPIs dramatically reduce manual setup and configuration time and enable predictive maintenance quickly and easily.
Feed Valuable Data into Big Data and Cloud Systems for Predictive Maintenance
Normalized data is immediately made available to both OT and IT systems via edge-to-cloud enterprise integration. Integrate the critical edge data collected and analyzed with leading third-party predictive maintenance applications. Run intelligent models and apply them back to edge devices to improve asset performance long-term and enable prescriptive maintenance.
The Edge Platform for Industry 4.0
Our flexible and scalable edge platform is built for any Industry 4.0 initiative or use case – from smart manufacturing and Industrial IoT to predictive maintenance and machine learning. Litmus provides the data collection, analytics, management and OT-IT integration necessary to increase asset visibility, performance and uptime at scale.
Asset Condition Monitoring
Litmus for Asset Condition Monitoring helps companies collect, analyze, manage and integrate asset data to take the guesswork out of asset condition and fully exploit equipment investment.Read More
Litmus for OEE harnesses the critical data buried with assets at the edge to dramatically simplify the calculation of Overall Equipment Effectiveness and optimize throughput, increase quality and improve performance.Read More
Litmus for Machine Learning allows companies to feed machine learning models with valuable, normalized data, and complete the feedback loop by running the new models at the edge for continuous optimization.Read More
Litmus for Edge Computing is purpose-built to connect to any industrial asset or data source, analyze and visualize data at the edge, and quickly and easily share valuable edge data with enterprise systems.Read More
Litmus for IIoT allows manufacturers to connect to any asset, collect and analyze data, and integrate with cloud and big data applications for advanced analytics, then run the models back at the edge for continuous improvement.Read More
Litmus for Smart Manufacturing is an all-in-one edge platform deployed next to the assets to collect, analyze, manage and integrate real-time data from the factory floor to meet business needs across the organization.Read More
Get Started With Litmus
Working with Litmus is fast and easy, we can help any industrial company derive value from the edge immediately for improved operational efficiency. Contact us to start your digital transformation today.