Fleet News

Fleet maintenance: Let predictive analytics take strain

Jennifer Roubaud

By Jennifer Roubaud, Dataiku's UK and Ireland country manager 

Predictive maintenance can have profound positive effects on fleet management.

By detecting impending failures that could never be seen by human eyes, safety across manufacturing, aerospace and automotive industries can improve drastically. 

Our eyes cannot see small defects such as micro cracks in machinery, but predictive maintenance, combined with the internet of things (IoT), can alert us to these problems.

The amount of downtime is also drastically reduced which, in turn, minimises costs.

First and foremost, predictive maintenance can help with safety in fleet management.

By predicting when a machine or vehicle will malfunction, managers can take appropriate steps to ensure this does not happen while the machine or vehicle is in use. 

Fleet managers can use real-time insights to make actionable decisions.

The cost savings are important as well. Fleet managers can use predictive maintenance on an asset-by-asset basis to utilise traditional maintenance methods when needed. 

The automation that predictive maintenance allows opens up resources once bogged down by menial tasks.

This automation frees up time and resources that can help improve fleet efficiency.

For example, Traffilog America utilised predictive maintenance to monitor driver safety, track trends, as well as how a driver drives a truck.

By monitoring pedal position, the company saved a bus fleet 30% on fuel.

For another company, they reduced brake pad replacement by 30%.

Traditional maintenance techniques no longer work as, inevitably, repair costs drive up expenses. 

Furthermore, these techniques are not improving safety overall.

Traditional maintenance does not provide an overall assessment of how a piece of machinery performs which is why equipment breaks down unexpectedly. 

Companies that use outdated legacy systems should consider updating their technology and investing in a system that can predict failure, instead of having to react when a failure occurs.


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