Question 1 of 30
Techtronics, a large manufacturing plant, has recently implemented an AI-powered predictive maintenance system for its machinery. This system uses machine learning algorithms to forecast potential equipment failures, allowing for proactive maintenance and minimizing downtime. Techtronics is also ISO 50001:2018 certified and is preparing for its next ISO 50003:2021 audit, focusing on the Energy Management System (EnMS). The company aims to effectively integrate the data generated by the AI system into the EnMS\'s management review process to demonstrate the AI\'s contribution to energy efficiency and compliance with ISO 50001. Considering the principles of ISO 50003:2021 related to performance evaluation and management review, what is the *most* effective approach for Techtronics to incorporate the AI system\'s predictive maintenance data into the EnMS management review?
Establish clear key performance indicators (KPIs) that directly link the AI system's predictive maintenance data to energy performance improvements and include these KPIs as a standard input in the management review process.
Present the raw output data from the AI system (e.g., predicted failure rates, maintenance schedules) directly to the management review team without specific KPIs related to energy performance.
Conduct an ad-hoc review of the AI system's data during the EnMS management review only when the AI system predicts a major equipment failure that could significantly impact energy consumption.
Limit the review of the AI system's data to the IT department and have them provide a summary report on the system's performance, which is then included in the EnMS management review documentation.

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