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Equipment Criticality In The Digital Age: Harnessing Data For Decision-Making

20 Mar

In the realm of industrial operations and asset management, the reliability and performance of equipment are paramount. Equipment failures can lead to costly downtime, production losses, and safety hazards. To address these challenges, organisations often turn to Equipment Criticality Assessment (ECA), a systematic process that prioritises assets based on their importance to overall operations, facilitating effective resource allocation and maintenance planning.

Leveraging AI and Data Science in Equipment Criticality Assessment:

In today’s digital age, the integration of AI and data science enhances the Equipment Criticality Assessment process. By understanding the distribution and relationships within the dataset, data scientists can identify patterns and trends that are crucial for assessing equipment criticality. These insights serve as the foundation for developing predictive models that estimate the criticality of assets based on their historical data.

In this process, established thresholds play a key role in categorising assets into high, medium, and low criticality levels. Data science techniques, including machine learning algorithms, are applied to build models that predict the criticality of assets, taking into account various factors such as failure rates, maintenance costs, and operational impacts.

At AIE, the integration of AI and data science into the Equipment Criticality Assessment process propels proactive asset management. The predictive prowess of data models enables the anticipation of potential issues, empowering preventive measures and substantially reducing the occurrence of unexpected failures. Moreover, the insights derived from analytics contribute significantly to the optimisation of maintenance strategies. This optimisation, informed by real-time monitoring, ensures a finely tuned allocation of resources, maximising efficiency and enhancing overall operational resilience. The marriage of AI and data science not only refines criticality assessments but also lays the foundation for a future where assets are managed with foresight and precision.

Optimisation and Improvement Results:

Our thorough Equipment Criticality Assessment has enabled:

  1. Optimised Maintenance Scheduling:

    Our in-depth Equipment Criticality Assessment played a pivotal role in revolutionising our client’s maintenance practices. By targeting maintenance planning on high critical assets, we are able to ensure maintenance efforts are precisely tailored to uphold their optimal performance and reliability. This strategic focus not only extends the lifespan of our high criticality assets but also contributes significantly to our overall operational resilience. We are able to embrace a proactive maintenance approach that has proven instrumental in minimising our client’s downtime and averting unexpected failures for their most critical assets. We have proven track record of supporting clients reduce the unplanned downtime of most critical assets by 25%

  1. Optimised Inventory Strategy for Low Criticality Assets:

    Implementing a nuanced strategy for lowest criticality assets, we adopted a “run-to-failure” maintenance approach. This strategic decision has not only streamlined our clients inventory management but also resulted in substantial cost savings for them. By minimising unnecessary costs associated with pre-emptive maintenance for low criticality assets, we have achieved a more cost-effective and sustainable inventory strategy for our clients.  AIE has supported clients achieve a 30% reduction in non-critical spares holdings.

  1. Enhanced Reliability models through Watchkeeping:

    The integration of digital operational surveillance, known as Watchkeeping, through the use of digital tablets has emerged as a transformative outcome of our Equipment Criticality Assessment. This innovative approach facilitates real-time monitoring, delivering unparalleled insights into the condition and performance of our client’s assets. With watchkeeping in operation, teams responsible for operational and maintenance surveillance can promptly address emerging issues, ensuring proactive intervention to minimise potential disruptions. The daily data gathered through watchkeeping acts as a valuable resource, providing a continuous stream of information that, when integrated with our data science models, contributes to the refinement and optimisation of the Equipment Criticality Assessment outcomes. This iterative process enhances the accuracy and effectiveness of our assessment model, allowing for dynamic adjustments based on real-world operational insights.

AIE’s Industry-Leading Equipment Criticality Assessment:

At Asset Integrity Engineering (AIE), our commitment to operational excellence is exemplified through our industry leading ECA service. Globally recognised, we leverage world-class subject matter experts and cutting-edge in-house software, combining data science and AI to deliver unparalleled insights. Our innovative approach has optimised maintenance costs for clients by up to 35%, demonstrating a harmonious blend of expertise and technology.

From offshore assets to onshore mines and nuclear power plants, our ECA service prioritises maintenance activities based on criticality, empowering clients to reduce downtime, minimise unexpected failures, and enhance overall operational reliability. As industries evolve in the digital age, AIE remains at the forefront, creating a future where every asset is optimised for success through our pioneering Equipment Criticality Assessment service.

Click here to know more about our Equipment Criticality Assessment service.



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