This case study examines the role of noninvasive equipment usage monitoring in pharmaceutical laboratory environments, focusing on how real-time data collection and AI-based analysis can inform maintenance strategies and operational planning. The session presents a practical example involving flow cytometry instrumentation, highlighting how usage deviations correlated with premature wear and contributed to avoidable costs and variability in results. Through empirical data, the study outlines technical approaches for detecting overutilization, refining maintenance schedules, and supporting consistency in scientific outputs — without disrupting core workflows. In this session, you will learn how to:

• Identify usage patterns that correlate with mechanical strain and unplanned interventions using noninvasive data collection methods.
• Analyze equipment utilization data to inform evidence-based maintenance strategies that reduce service costs and operational interruptions.
• Improve data integrity and process reliability by aligning equipment performance trends with scientific output requirements.