_________________________________________________________________________________________________________ AI
+ CBM
Meanwhile, generative AI vastly simplifies the process of getting that technician to the right place. Often, the administrative work associated with maintenance is more time intensive than the actual repairs; Work orders need to be filled out, failure codes need to be looked up. Generative AI can automate much of this, drawing on historical data to analyze an issue, suggest a failure code, and help fill out the necessary paperwork. Generative AI can also be utilized for Failure Mode and Effects Analysis( FMEA), the important process of fully understanding potential component failures and assessing their larger impact. Generative AI allows FMEAs to be quickly created, customized and automated for specific asset classes, drastically boosting reliability engineers’ productivity.
Agentic AI takes these processes to the next level. Some manufacturers are already experimenting with AI agents that analyze asset data, conduct a root cause analysis,
determine the best course of maintenance, and then initiate a work order – all with limited human supervision. Agentic AI will compound the productivity gains and cost savings that machine learning and generative AI already provide.
Maintenance will always be a constant in the manufacturing world. But by coupling CBM and AI, manufacturers can create another constant: reliable and automated insights that simultaneously reduce maintenance costs and improve manufacturing production. ■
Scott Campbell www. ibm. com / products / maximo
Scott Campbell is Global Leader, Product Management ESG & Asset Management at IBM. IBM is a leading provider of global hybrid cloud and AI, and consulting expertise. IBM helps clients in more than 175 countries capitalize on insights from their data, streamline business processes, reduce costs and gain the competitive edge in their industries.
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