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Strategies for Using Predictive Analytics in Asset Tracking


Big data has made predictive analytics possible, but what does predictive analytics mean when it comes to asset tracking? Predictive analytics can be a powerful tool for asset tracking, but you need to have the correct platform to leverage the raw data you have available. We look at what predictive analytics involves, its benefits, and how it can be used for asset tracking.

What’s predictive analytics?

In simple terms, predictive analytics uses past and current data to predict future trends. Predictive analytics achieves this by using models and algorithms to come up with possible future scenarios. Typically, you’ll perform predictive analytics through software, which might be a stand-alone platform or integrated with an asset management platform, for example. If you’d like to see how predictive analytics can be used in an asset management platform, contact SmartAsset to book a free demo today.

Predictive analytics in asset management often focuses on goals like optimising processes and anticipating process risks and anomalies. You can use predictive analytics to take preventative action or to better plan for maintenance, upgrades, and repairs. The raw data can be collected from sources such as maintenance reports, logs, sensor readings, and observational data.

The benefits of predictive analytics

Predictive analytics can augment your risk management, improve asset usage and planning, and support deeper data insights. It could improve reliability and availability while reducing ownership costs. These can drive higher ROI and an improved bottom line.

  • Anticipate risks - Predictive analytics empowers you with data insights to identify and address operational risks before they turn into issues.
  • Effective preventative maintenance - You can use predictive analytics to build models on asset failure and then use these models to predict asset status and performance in the future. These models can inform your preventative maintenance and replacement strategies.
  • Safety and compliance – The deeper insights you get from predictive analytics could enhance safety and compliance, leading to better risk-management outcomes at the same time.
  • Failure and downtime – Predictive analytics, when appropriately implemented, can successfully provide early warnings of potential equipment issues and failure risks. In turn, it can help businesses avoid costly shutdowns, catch problems before they become chronic, and save millions in repair and downtime costs.
  • Asset performance - Predictive analytics – through deeper insights and better decision making about maintenance - can help optimise asset performance while reducing downtime. This can lead to business process improvements, production efficiency gains, and better ROI on inputs overall.
  • Resource utilisation - When used right, predictive analytics lets you direct your attention and resources on the most valuable maintenance and asset-management strategies. For example, you could avoid having to carry out unnecessary maintenance and repair work.
  • Ownership costs - Thanks to better resource deployment, you could enjoy lower asset-ownership costs, as well as enhanced equipment reliability.
  • Human resource optimisation – With a clearer view of asset status trends, you could also better direct your human resources around these predicted outcomes. This can take the form of improved work planning, prioritisation, and job scheduling around maintenance and repairs, for example.

How predictive analytics is used in asset tracking and asset asset management

From utilities and transport to healthcare and manufacturing, a wide range of industries can gain from integrating predictive analytics into their asset management. Predictive analytics can give you a dynamic whole-asset-life cycle perspective and offer exhaustive insights for your asset management strategy.

In asset management, predictive analytics can start with root cause analyses and failure pattern recognition so you can better understand past behaviour. From this you can derive predictive and statistical models, which are used to work out the likelihood of outcomes and risks. At this point you can use the models to work out the asset’s health status and then make decisions based on predicted life cycle and failure rates. These decisions can encompass maintenance plans and capital allocation. Finally, you can proceed to specific action steps and work orders to manage risks and possible outcomes.

You can leverage predictive analytics to identify patterns in asset maintenance and areas where you can make quality and reliability improvements. This is true for both asset-level and individual-component failure risks. You can also use these insights to zoom into variables affecting ownership cost, downtime, and working life of assets. Predictive analysis could support efficient data mining when it comes to things like logs and technician reports, and so enable you to better use the data in these reports to make smarter asset-management decisions.

So, you can systematically use predictive analytics to turn large data pools into actionable insights to inform decision making. This can make your asset management more precise, rigorous, and specific, so you know the best possible maintenance and management steps to take.

The availability of big data today offers a great opportunity to make smarter decisions about managing one of the most important aspects of your organisation - your assets. Predictive analytics can give you granular insights across the whole asset lifecycle, allowing you to better plan everything from asset usage to maintenance and upgrades.

Finding the best asset management strategies for your business

SmartAsset is a best-in-class asset management solution with comprehensive asset management tools and features, including predictive tools. To find out more, contact us today toschedule a free demo at your convenience or feel free tocontact our friendly team for a discussion about your business needs

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