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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%

 2. 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.

 3. 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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