The Rise Of Plug And Play Analytics For Industrial Asset Performance And Maintenance

The Rise Of Plug And Play Analytics For Industrial Asset Performance And Maintenance

Quick ROI, rapid time to value and fast start are terms which are typically not associated with implementing analytics software to improve industrial asset maintenance as well as performance. However, recent asset management software vendor strategies have focused on delivering just this to industrial firms. The increasing application of AI coupled with the proliferation of IIoT devices has provided an impetus to this trend. A recent Verdantix research identified that the asset maintenance analytics needs of customers vary based on users – with executives in operations and maintenance functions preferring black box solutions to gain insights quickly, while engineering and data science teams require open, customisable solutions.

While industrial asset management software providers have multiple go-to-market strategies to differentiate from competition, plug and play asset management analytics providers have focused their product strategies to expedite implementation and time to value by offering out-of-the-box connectors, interfaces and rich library of pre-configured, industry-specific analytics templates for high-value use cases. Witness Arcadis Gen’s easy-to-implement Water AI Pipe Predictor to predict pipe failures, wastewater collapses and flooding and pollution incidents or C3 AI’s ready to use software applications for energy management, production optimization, inventory optimization and reliability. Similarly, Falkonry in May 2020 introduced ‘Falkonry Clue’, a plug-and-play solution to predict potential inefficiencies from real-time data for manufacturing production operations, while Uptake offers solutions to detect anomalies, predict failures and optimize maintenance for industries such as fleet, wind, power grids and government. In February 2021, Senseye released Senseye Ready – a partner ecosystem with sensor manufacturers that ensures direct integration of these sensors with Senseye’s predictive maintenance software.

What does this growth in plug and play analytics mean for industrial data management? The sharp rise in AI-based analytics is driving data management and industrial data operations (DataOps) platforms from providers such as Cognite, HighByte, MachineMetrics and Uptake since getting data into a usable format in terms of collating, tagging and contextualising is the most time-intensive aspect and DataOps platforms help to accelerate this process.

For more information on AI analytics register for the Verdantix Virtual Event ‘Next & Best Practices: Succeeding With AI For Industrial Operations – Reliability, Maintenance, Safety and Environment