Maintenance & Reliability in Asset Management


Shane Scriven

Shane Scriven Shane has over 10 years’ experience as a reliability engineer and asset management practitioner. Shane started his reliability career working for SKF travelling Australia conducting vibration analysis, lubrication analysis, laser alignment, root cause analysis and bearing fitment and removal. Throughout his career he has gained experience in asset management activities across a variety of industries and specialises in the development and implementation of risk based reliability strategies. Shane is looking forward to building on the great work of the MRiAM Special Interest Group to date and is keen to engage with the broader asset management community to both promote the MRiAM Special Interest Group and to provide valuable learning opportunities.

Data Better Idea

Throughout the month of December, we’re taking a closer look at predictive maintenance, with an article each week to showcase its importance in relation to asset management. If you missed the first one you can find it here, or read on to enjoy our next thrilling instalment.

As noted in our first article, predictive maintenance is all about forecasting future failures in your assets. In other words, predictive maintenance offers a view of what was previously unseeable, and therefore take maintenance operations to the next level. 

That’s all very well, but how is predictive maintenance best implemented? The answer lies in data. 

 Data has changed the world. Big data is complex, defined by Gartner as, ‘data that contains greater variety arriving in increasing volumes and with ever-higher velocity1'. As a result of the three Vs: variety, volume and velocity, many businesses struggle to keep up with the data at their disposal. It is stored, but not capitalised upon. It is used, but not in its entirety, nor is it exploited to the advantage of the business. However, the leveraging of data in maintenance, and particularly predictive maintenance, significantly increases the value within your business. 

In terms of predictive maintenance, leveraging big data through sophisticated and advanced analytics will gauge an asset’s condition, usage and maintenance history, and ultimately extend the lifecycle of your assets. 

It’s not an easy, speedy or smooth journey, however. It is a costly and time-consuming process to find beneficial ways to exploit the data at your business’s disposal. It is also confounding; as noted above most organisations have more data than they know what to do with. Often, the first step is the most difficult one to make, especially when the road ahead is congested with confusing and copious amounts of analytics. Building a business case to access big data for predictive maintenance can be difficult; competing budgets and priorities amongst departments is often the biggest stumbling block. But stride on through, because at the AM Council, we know predictive maintenance offers greater business outcomes in the long run.

Leave a comment to let us know how your business is protecting its assets – we’d love to hear from you. Perhaps you’d like to join our Maintenance and Reliability in Asset Management Special Interest Group to network with others in the sector.

Visit Forum