Fixed asset data assists in company evaluation and the generation of trustworthy financial reports through the application of financial analysis. Such reports may be used to evaluate a corporation’s financial standing, which can help creditors and investors determine whether to lend money to a business or purchase its stock. On the other hand, industrial establishments rely on colossal infrastructure to run their operations smoothly. They invest hoards of time, money and effort in asset management, especially maintenance.
However, The pervasive disruption of current asset management efforts is now an urgent need for modern asset managers in the industrial realm. Retrofitting asset management strategies that only consider the static degradation- or age-based decaying- of assets are meeting irrevocable losses due to resource shortages, otiose spending, human resource shortages and unforeseen asset surrendering. This is why modern asset managers are demanding better technology solutions to realise cost efficiencies while ensuring their fixed assets remain healthy and long-lasting even under the most extreme asset operating events.
This is where asset predictive analytics solutions make a bold emergence. This technology has allowed the back and middle offices of asset-oriented companies to keep pace by revitalising asset futures with the power of data. This article investigates the application of AI-driven predictive analytics in ensuring reliable asset behaviours and how it can benefit asset-dependent organisations.
Why Is Asset Reliance So Important?
The meaning of ‘Asset Reliability’ is exactly like the name suggests- it is the reliance and trust you keep on your assets. The reliability of asset peaks when it functions as expected by the organisation under unique operating conditions in a healthy, cost-efficient and high-performing manner. An asset with high asset reliance meets the requirements of three aspects: the stakeholders, the business and the regulatory obligations.
Meeting the peaks of asset reliance can mean an arduous effort of collaboration between teams across the company, careful decision-making and event-driven strategies that assess the assets’ condition- not their age. Therefore, it is financially sensitive and operational crucial to maintaining higher asset reliability standards. This is why asset reliability teams should be equipped with digital technologies that ensure effective and seamless reliability programs despite the changing codes, standards, regulations, environmental impacts, financial caps, dynamic machine operations and more.
AI and Asset Management
Solving immediate needs using reactive maintenance has shown many limitations for asset specialists. This is why most companies are building agility into asset management strategies by using the power of AI and ML models. This is why asset practitioners, decision-makers and other stakeholders must have a comprehensive understanding of how to translate future asset disruptions into core value.
It did not take too long for modern asset managers to solely rely on AI technology; it substantially optimised asset management, starting from asset planning, deploying, and maintenance to replacement. This is why the use of AI has now completely overtaken traditional asset management practices. AI allows technology experts to create unique models by collaborating with asset utilisers to detect hidden patterns and correlations of asset data. In other words, they use data from various historical and real-time points to train algorithms to predict future asset parameters. One of the main AI analytical models created for asset management is known as ‘Predictive Analytics Models’.
Asset Predictive Analytics and Asset Reliance
How does predictive analytics work in ensuring asset reliance? The answer is very simple; it basically has everything to do with asset reliance. As mentioned above, asset reliance mirrors the ability of the asset to function properly at any event at any time. For an asset to become failure-proof and healthy, it should be maintained before it fails or breaks down. This exposure is delivered to asset stakeholders via predictive analytics models.
The wealth of information is calibrated into a single cloud platform by IoT systems- in other words; it integrates valuable asset data from different sources like IoT sensors, cloud platforms, remote sensing satellite systems, geolocation maps, applications, databases, websites and more. This data will then be sent via AI-powered predictive models that are trained to identify regular and irregular data patterns and detect KPIs of asset performances.
The insights generated by the predictive analytics tools can be illustrated in interactive and scalable visual representations like charts, curves, diagrams and other smart visualisations tools. Predictive analytics models can study data streams to provide answers to a plethora of asset reliability questions. It can provide data-driven insights to many questions and ‘what if’ scenarios, like:
- How many maintenance people are required for the next evaluation?
- How much of the budget should be allocated to maintenance this year or the next decade?
- How soon will the asset performance degrade from criticality level 3 to 5?
- If it rains heavily, how easily will the asset installed in a hard-to-reach location fail?
- What is the remaining usable lifetime of your asset now or in a few days, weeks or months?
- When is the next maintenance check-up of a specific asset?
- When will your asset meet a risk or failure, and why?
A Reassured Asset Reliance for Our Future
Asset-intensive organisations are now majorly depending on data analytics models to improve asset reliability across expansive industrial footprints. With less expert intervention, cutting down otiose expenses and avoiding future risks even before they occur, modern asset utilisers are now adopting self-reliant asset management strategies by relying on expert data analytics tools. This is why all asset-intensive organisations should invest in innovative analytical solutions to ensure unperturbed asset reliability and OEE rates across expansive asset spreads in your industrial premises.
