Organizations have been tracking and reporting on their assets – including people, processes, and physical things such as computers – for years. Companies use the information they get to help lower operating costs, reduce risk and outages, improve regulatory compliance and take control of capital expenditure planning.
But, like other fields, the rapid advance of technology is disrupting enterprise asset intelligence. Artificial intelligence technology like machine learning has been particularly influential. In this post, we’ll review what asset intelligence is today and how machine learning will impact it going forward.
What Is Enterprise Asset Intelligence?
Enterprise asset intelligence is a key aspect of enterprise asset management that starts with gathering information on business resources and their status. Information can come from maintenance reports, stock and merchandising, or regular updates from staff.
Today, many enterprise asset management and intelligence systems use IoT sensors to transmit information from enterprise resources, such as trucks used to make deliveries or equipment being operated in a factory. That information is combined with cloud technology, mobile devices, and mobile applications to provide real-time visibility.
Data from assets is analyzed to provide actionable insights, which is where asset intelligence’s biggest value lies. Asset intelligence uses the information collected to analyze and visualize an asset’s lifespan and risk, calculate the likelihood and consequence of failure, and simulate the impact of capital spending strategies.
If delivered to the right person at the right time, these insights can be leveraged to:
- Drive better, more timely decisions
- Pinpoint high-risk assets
- Prioritize maintenance and repair work
- Increase asset reliability
- Improve regulatory compliance processes
- Identify and maximize high-performing assets
Some industries and organizations already using asset intelligence for business benefits include logistics companies with fleet management priorities, natural gas providers, and other utility companies.
Machine Learning’s Effect on Asset Intelligence
Machine learning is the use of algorithms to help computer learn over time. The computers process data and learn from patterns in that data, becoming more “knowledgeable” over time. In machine learning, learning can be autonomous, without direct involvement from humans beyond algorithm creation and optimization.
Machine learning algorithms are being incorporated into enterprise asset intelligence and management systems to help computers sift through data quickly, learning to recognize what’s normal and what isn’t without additional help from humans. As the system processes more data, machine learning will become more accurate, flagging high-risk assets and yielding insights that enable organizations to better manage and optimize enterprise assets.
Machine learning can put organizations on the path to improvements in predictive maintenance and efficiency. More data leads to better insights and more informed decision making. According to PwC, the adoption of machine learning and analytics by manufacturers to improve predictive maintenance is predicted to increase by 38% in the next five years.¹
Asset Intelligence and Machine Learning Are Already Benefiting Industries Around the World
Thanks to ubiquitous connectivity, billions of sensors, and advances in machine learning, we’re well on our way toward revolutionizing enterprise asset management and intelligence. The manufacturing, supply chain, and transportation industries are just a few that have already seen the benefits.
The potential for these and other industries is huge, translating to healthier assets, more efficient operations, and reduced expenses for your business.