Prioritising maintenance work is crucial with limited resources, time, and budget. At Woodside, traditional A/B/C equipment classification proved insufficient for managing failure risk across our global fleet of over half a million assets, lacking precision to identify truly critical equipment.

Our solution was developing a dynamic, data-driven Equipment Criticality Analysis (ECA) program that:

• Evaluates equipment based on 14 key parameters, assigning a score out of 100
• Integrates with real-time data sources to automatically adjust criticality as conditions change
• Has successfully analyzed over 200,000 pieces of equipment
• Transitioned from static to dynamic analysis, ensuring ongoing relevance
• Reduced overall maintenance hours while maintaining operational safety
• Optimised resource allocation through improved prioritization
• Generated cost savings amounting to hundreds of thousands of dollars

As asset-intensive organisations adopt increasingly sophisticated technologies—from ERP and APM systems to IoT sensors, drones, and mobile inspection tools—they face a growing challenge: data overload. While the capacity to collect information has grown exponentially, the ability to convert this data into actionable insights remains elusive. According to recent studies, 78% of organisations gather more maintenance and reliability data than they can analyse, and only 23% consistently turn that data into business value. This panel will explore how organisations can overcome the barriers of fragmented systems, poor data quality, and limited analytical capability to unlock the strategic potential of their asset data. Key challenges such as contextual intelligence gaps, system silos, skills shortages, and information lifecycle mismanagement will be discussed, alongside real-world examples of how leading organisations are tackling them.

Panelists will share proven strategies including implementing robust data governance, integrating contextual data with asset performance metrics, and building cross-functional teams that blend maintenance expertise with data science. Insights will be drawn from Australian and international benchmarks, highlighting the tangible performance and cost benefits achieved by organisations that adopt mature data practices—such as a 37% increase in asset performance and 29% reduction in maintenance costs. Attendees will gain practical guidance on building a data-to-decision pipeline, improving data literacy across teams, and selecting technology platforms that support long-term asset intelligence. This session will equip maintenance and reliability leaders with the knowledge to transform data from a burden into a strategic asset.

Reliability and asset engineers play an increasingly critical role in successful digital transformation initiatives. This session explores how these professionals can most effectively drive and support technological change. We'll discuss the evolving skillsets needed, practical approaches to pilot program implementation, and methods for demonstrating value through business outcomes. Participants will share experiences with emerging technologies like AI, predictive maintenance, digital twins, and real-time monitoring. Join peers to examine success stories, implementation challenges, and strategies for building organisational digital capabilities.