This presentation explores the transformation of maintenance management within a multinational division, highlighting the shift from siloed, legacy practices toward harmonized, digitally mature systems across countries. Building on lessons from obsolescence management and first-generation deployments of core CMMS platforms, the session demonstrates the progression to a second generation of standardized, predictive approaches. Drawing from a network of 18 sites — employing a “sister-site” model to overcome both inter- and intra-site silos — the case illustrates how harmonization, sensor-driven insights, and generative AI can unlock new levels of operational efficiency, resilience, and knowledge accessibility. It also addresses practical challenges such as integrating predictive maintenance with legacy systems and improving access to critical maintenance knowledge. You will learn how to:

• Advance from first-generation CMMS deployment to predictive, harmonized maintenance systems across countries.
• Use sensor and process data to detect anomalies and optimize maintenance strategies, transitioning from reactive to predictive interventions.
• Break down organizational silos with a sister-site collaboration model and leverage generative AI to improve knowledge retrieval and sharing, even in contexts of restrictive information.

In this forward-looking panel, industry experts will discuss how AI is shaping the future of maintenance practices within the pharmaceutical industry. As companies aim to drive operational excellence across global sites, AI is emerging as a critical catalyst for optimizing maintenance strategies—from asset lifecycle management to resource planning and operational support. This panel discussion will delve into how AI can enhance predictive maintenance practices, extend asset lifecycles, improve resource allocation, and support consistent and compliant operations across all sites. Panelists will share insights into their approach, lessons learned, best practices, and the evolving role of AI in achieving maintenance excellence that empowers pharmaceutical success. The following topics will be discussed in the panel:

• How AI-powered tools can drive maintenance excellence by enabling proactive, precise maintenance practices that maximize asset value and reduce downtime.
• Integrating AI into asset lifecycle management to extend asset lifespan, optimize replacement strategies, and support effective lifecycle planning across global sites.
• Enhancing resource planning and allocation with AI by using predictive insights to streamline maintenance schedules, optimize resource distribution, and align workforce capabilities with maintenance needs.
• Building consistent operational support through AI-driven insights to improve compliance, maintain quality standards, and streamline decision-making across geographically dispersed sites.

• What are the main barriers in your current maintenance operations — including legacy systems and organizational silos — that prevent harmonization and digital maturity?
• How could sensor data and process analytics be leveraged to anticipate failures and optimize maintenance strategies across multiple countries?
• In what ways could cross-site collaboration (e.g., sister-site models) strengthen knowledge sharing, consistency, and operational resilience?
• How could generative AI or other digital tools support technicians in accessing critical maintenance knowledge efficiently, especially when information is difficult to retrieve?
• What practical steps and lessons learned from pilot deployments could help integrate predictive maintenance while balancing legacy systems and standardization goals?

In the Harvesting Session the World Café moderators will summarize the results of their discussions and highlight key findings.