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.

Maintenance and reliability professionals are increasingly turning to data-driven approaches to enhance operational efficiency and asset longevity. This panel discussion delves into the two paramount challenges we're facing as we navigate the complexities of data utilization: data quality and integration, and the skill gap in data analytics.

Ensuring data quality and seamless integration still poses a significant hurdle. Maintenance and reliability operations generate vast amounts of data from diverse sources, including sensors, equipment logs, and maintenance records. However, inconsistencies, inaccuracies, and fragmentation in data can undermine decision-making processes. The panel will explore strategies for improving data quality, standardizing data formats, and integrating disparate data sources to create a cohesive and reliable data ecosystem.

The skills gap in data analytics also remains a critical issue. While advanced data analytics tools and techniques offer tremendous potential, the lack of personnel with the requisite expertise to leverage these tools effectively hinders progress. This discussion will address the need for upskilling existing staff, attracting new talent with data science proficiency, and fostering a culture of continuous learning within maintenance and reliability teams.

Join us as industry leaders share insights, experiences, and practical solutions to these pressing challenges, paving the way for a more data-savvy and resilient future in maintenance and reliability.

Reliability & Asset Engineers are integral to digital transformation. In this XCHANGE session we will focus on the role of reliability and asset engineers in driving digital transformation initiatives within their organizations. We’ll facilitate an exchange of ideas regarding the digital tools being utilised across different industries to enhance performance, efficiency, and reliability. Exploring digital initiatives such as AI, predictive maintenance, digital twins, and real-time monitoring being used by others. We’ll look to share success stories and outcomes from various organisations in their digital journey and the challenges being faced with digital transformation.