HomeFrom 1 Year to 1 Day: How We Scaled Condition Based Asset Monitoring Across PlantsBlogFrom 1 Year to 1 Day: How We Scaled Condition Based Asset Monitoring Across Plants

From 1 Year to 1 Day: How We Scaled Condition Based Asset Monitoring Across Plants

From 1 Year to 1 Day: How We Scaled Condition Based Asset Monitoring Across Plants


A large manufacturing company created an automated work order based on live monitoring of asset conditions, powered by Litmus Edge – in just a single day.


The manufacturer had their entire asset monitoring system (for hundreds of assets) built on a soon-to-be-retired component that periodically queried only a single type of database. This meant slow development times (12 months!), limited scalability, and a blind spot for valuable data. Change was imminent.

They adopted Litmus Edge to replace clunky code with rules, built using our intuitive no-code interface. Today, each rule seamlessly connects to multiple data sources, regardless of their type, extracting insights and triggering work orders in the customer’s CMMS system via a REST API.


The manufacturing company used the following Litmus Edge features to build their solution:

  • Digital Twins
  • Flows
  • Historian and Database Integration
  • CMMS Integration


  1. The manufacturer used Litmus Edge’ prebuilt connectors and integrations to bring in data from every kind of database and/or historian on their plant floor.
  2. Once the data was inside the platform, they leveraged the Digital Twins feature to create specific rules.

Each rule contains the following:

  • Well-defined parameters to query the historian or database.
  • The regular expression to compare the query findings for a match.
  1. Every single rule interacts with the rule engine, built using Flows – another feature in the same Litmus Edge platform.
  2. The Rules Engine supports multiple historians and databases, each of those being of different types.
  3. Based on the rule, when a match is found, the rules engine triggers a REST API call to the manufacturer’s CMMS system.
  4. The CMMS can generate an automated workorder in their system.

Replicating this process is as simple as creating a new Digital Twin with the required parameters. Templates allow this process, including the creation of Digital Twins and Flows-based rule engine to be repeated easily across other plants and sites.

This Litmus Edge solution was up and running in just one day! 


No more data silos.

By seamlessly integrating with multiple types of data sources, the manufacturing company was able to harmonize their data streams into actionable insights. Pre-defined rules act as automated conductors, triggering REST API workorder creation with precision and speed, preventing issues before they escalate.

Scalability on steroids.

This solution reduced what took a year with legacy methods and tools to just a single day – not only solving the challenge at hand but opening doors to more data intelligence applications right where the data is generated – at the edge.

Efficiency amplified.

By proactively identifying all contributing data trends and automating workorders, the manufacturing company can minimize downtimes, reduce maintenance costs, and optimize assets for peak performance.

Ask for a demo to see how Litmus Edge could help you with your OT data challenge.

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