Asset management: moving to a smarter, data-driven future
Monitoring and managing ageing assets cost-effectively and in a timely manner are challenging organisations to identify more effective and efficient processes using intelligent, connected devices—and to do so with high safety, reliability and compliance.
Traditional and smart asset management have a common objective—aid in the reduction, minimisation and optimisation of asset lifecycle costs across all phases, from asset investment planning, all the way to operation and maintenance.
Optimising the costs associated with each phase will remain among the key objectives of an asset-intensive modern organisation.
At present, preventative maintenance schedules suggested by manufacturers are not enough to help companies avoid critical asset failures.
In order to improve customer satisfaction, organisations need to work towards avoiding unexpected downtime, managing asset risks and maintaining assets before failure strikes.
Reducing outages and shortening restoration times are among the most significant challenges faced across industries such as mining and resources, energy sourcing and distribution, and manufacturing – with facility and operations managers recognising the need for a mechanism to predict equipment failure.
More than ever before, facilities are leaning towards predictive analytics to extend the life of their assets, as well as to bring greater predictability to performance and organisational health.
By applying predictive analytics for smart asset management, organisations can realise asset lifecycle cost reduction while improving the accuracy of their decision-making, allowing them to plan and prioritise maintenance activities.
Industrial organisations these days employ a huge variety of data-driven, connected devices such as sensors, nodes, cameras, and access points which are constantly producing data.
As the organisation ingests this data, it can be either used for real-time reporting or stored for future use.
However, data that is stored away becomes less productive almost as soon as it reaches a data centre – the more current the data, the more benefit can be derived from it.
But this is just part of the overall dataset required for this exercise.
In order to accurately predict equipment breakdown, companies need to combine this streaming data with operational data such as service life records, warranty data, and other information.
The streaming data coming in from sensors and connected devices and the operational data are of different types and reside in different source systems, further complicating the process of data integration and transformation.
Therefore, organisations who are looking to predictive maintenance for proactive field maintenance and optimising asset performance need to leverage data streams as soon as the data is available, in order to maximise its use.
Traditional data integration methods such as ETL often fall short, because bulk or batch data transfer is both slow and expensive, and also they replicate data into yet another repository to perform transformations.
