Digital and data services overview
Artificial Intelligence and machine learning allow industries dependent on machines for operations to predict failure for preventative maintenance. The predictive maintenance market is expected to grow from $3B in 2019 to $10.7 in 2024. With advances in big data, predictive analytics and IoT, traditional sectors like industrial, transportation and travel have seen increased adoption of technology to streamline operations.
In these environments, it is critical to maximise uptimes of assets such as machines, vehicles and other infrastructure. The challenge is to increase uptimes while reducing the costs associated with repair and maintenance. If done right, this will lead to increased revenues, reduced cost and world class customer experience.
A technology ecosystem of big data, IoT and predictive analytics could be used for condition-based monitoring/predictive maintenance to predict with a high degree of accuracy on when an asset/infrastructure could fail. This has advantages over reactive maintenance by reducing downtime/costs and differs from scheduled maintenance by optimising costs.
Industry and functional applicability
While sectors such as industrial, transportation and travel seem to be the frontrunners for predictive maintenance given that they are asset/infrastructure heavy, it could also be used for other industries such as communications and technology, to predict equipment failure, and IT, to predict network failure/congestion. Implementation of these predictive maintenance solutions involves data collection through sensors/telematics, data processing and model building using machine learning and, last but not least, the visualisation of analytics through UX/UI to help drive actionability and operationalisation.
How can a major industrial/transportation company reduce the spend on unscheduled downtime events resulting from mechanical breakdown through a predictive analytics model?
- Collection and analysis of data, both static data such as the maintenance, repair and operation records of an asset along with dynamic/real-time data collected through sensors
- Understand/impute the features that impact a failure – asset type, asset model (truck model), model year, miles since last service, number of breakdowns in the past, etc. Additional sampling validation on data using cross validation
- Rerun model with various input fields to improve performance and fine tune our feature selection
- Cost reduction through advanced detection and servicing of component versus encountering expensive replacement/repair of a component
- Downtime reduction by swapping out the component versus experiencing prolonged downtime after component failure
- Improved customer experience by better catering to operational and maintenance metrics such as MTTF (Mean Time To Failure) and MTTR (Mean Time To Repair)
We will continue to delve deeper into technology solutions that address predictive maintenance along with case studies to support technology-driven predictive maintenance work. For more information, please contact email@example.com.
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