The future of asset management: creating more value out of data

Are you ready for the challenges and opportunities of managing applications in a multi-vendor, multi-cloud, hybrid environment? If the answer to the above is no, it may not just be a matter of a poor competitive advantage. It could be a failure to understand that the future is data-driven, adaptive, agile and connected.

Successful companies understand the need for enabling informed decision-making processes over the lifecycle of an asset. Traditional and data-driven asset managements have a common objective—to reduce and optimize asset lifecycle costs across all phases, from asset investment planning to operations and maintenance support.

Cloud-based platform revolution

Automation is reshaping the entire asset management process. Cloud-based platforms will enable companies to automate the end-to-end maintenance management processes and get the visibility essential to make informed decisions. Cloud-based platforms enhance collaboration among maintenance teams while supporting them to identify, analyze, and resolve disruptions and ensure compliance and adherence to performance and safety standards. The future will enable digital technology, mainly predictive maintenance, remote monitoring, "Big Data" analytics, Internet of Things (IoT), and Digital Twin tools.

Asset-intensive industries worldwide are searching for better ways to cost-efficiently manage the sum of its moving parts across supply chain and inventory while performing maintenance, repair, and overhaul activities. Maintenance support accounts for a large part of the annual operating costs and any savings would be significant. Additionally, some assets are critical to the successful running of the enterprise and any failure will cause major disruption to operations and reputation.

Reducing asset downtime from failures and shortening repair and maintenance times are critical to industries such as power generating wind farming, mining & resources and manufacturing. There are excellent examples of best practices such as the Airline and Mining industries making a science out of getting the most production from an asset.

By applying predictive analytics for intelligent asset management, organizations can realize asset lifecycle cost reduction while improving their decision-making accuracy, allowing them to better plan and prioritize maintenance activities

Predictive analytics

Manufacturers' preventative maintenance schedules are not enough to help companies avoid critical asset failures. They are guides to structure the early years of operations, but they can't account for rough running conditions, defects or over/underuse.
Using a wide array of data, maintenance professionals can make informed decisions about the machine's functioning to schedule maintenance appropriately. This type of maintenance schedule based on asset condition, known as predictive maintenance, helps reduce labour and maintenance costs.

Preventive maintenance is maintenance that is scheduled at regular intervals. Data Management processes can better inform necessary actions and build costs awareness while widely sharing and communicating across the enterprise. The goal needs to be a move from single sources of knowledge and into collaborative sharing environments.

Sensors and data collection networks have been improved and can generate a large volume of equipment data. Today, industrial organizations employ a wide variety of data-driven, connected devices such as sensors, nodes, cameras and access points that are constantly producing data. The Internet of Things technology can automate the data collection process and simplifies data mining, pre-processing and cleaning processes.

By applying predictive analytics for intelligent asset management, organizations can realize asset lifecycle cost reduction while improving their decision-making accuracy, allowing them to better plan and prioritize maintenance activities. This streaming data will be combined with operational data such as service life records, warranty data, and other information to predict equipment breakdown accurately.

Managing ageing assets pose additional challenges but imposing data management functions across the ageing asset will help to identify more effective and efficient processes using intelligent, connected devices—and to do so with high safety, reliability and compliance assurance.

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About Gary Nasers
AMPS PLM Portfolio Lead – B&PS Australia
Gary has over 30 years of experience in Defence Acquisition and Sustainment. He has worked on many reform initiatives to reshape Asset Management and Maintenance operations by introducing overarching improvements to enterprise planning. These improvements were enabled by better use of existing planning tools and better collaboration on data management and business practices. He was awarded a Masters in Sustainment from the University of New South Wales, Australia, in 2019 and is currently working as the Portfolio Lead for the Asset Management Planning System.