This session will explore OMYA's reliability management journey in embedding an AI infused predictive maintenance program across its global operations. OMYA, a leading producer of essential minerals and a distributor of specialty materials, with X plants worldwide, relies on large, critical equipment for its mining operations. Ensuring the operational efficiency of these assets through predictive maintenance is crucial for minimizing unplanned downtime and managing spare parts inventory.

Initially, OMYA faced the challenge of implementing predictive maintenance across various sites, ranging from small, remote operations to large, complex facilities. The transition from site-specific implementations to a centralized, digitally embedded organization using AI and machine learning required addressing significant hurdles, including varying levels of automation and data historization, as well as a cultural shift towards a standardized digital approach.

This session will provide an in-depth look at how OMYA overcame these challenges by developing a flexible framework where the central team establishes standards yet allows individual sites the autonomy to tailor predictive maintenance practices to their specific needs. We will highlight key successes, including the detection and management of critical issues identified by AVEVA Predictive Analytics under this new governance model.