Prioritising maintenance work is crucial in an environment where resources, time, and budget are limited. We must continuously adapt our approach to the ever-changing operational landscape to answer critical questions such as:

• Which assets are the most critical to our operations?
• Which lower-criticality equipment can have their maintenance deferred or moved to run-to-failure (RTF) strategies without increasing risk?
• How does the failure of a single piece of equipment impact the overall system?

At Woodside, the traditional A/B/C equipment classification system proved insufficient for managing the risk of failure across our global fleet of over half a million assets. This method lacked the precision to prioritize truly critical equipment and assess potential risks to both people and the plant.

The Solution:
I was part of a team that developed and implemented a dynamic, data-driven Equipment Criticality Analysis (ECA) program. This new approach evaluates equipment based on 14 key parameters, assigning a score out of 100 to quantify its criticality. What sets this ECA apart is its live, dynamic nature—integrating with real-time data sources to automatically adjust criticality scores as operational conditions change. For example, if a piece of redundant equipment fails, the criticality of its backup automatically increases.

Key Achievements:

• Successfully conducted ECA on over 200,000 pieces of equipment.
• Transitioned from static to dynamic ECA, ensuring criticality scores remain relevant in real-time.
• Leveraged ECA to reduce overall maintenance hours while maintaining operational safety.
• Currently exploring ways to further integrate ECA into maintenance planning to optimise resource allocation.