TECHNOLOGIES DRIVING CHANGE IN ENTERPRISE ASSET MANAGEMENT
THE THREE DISRUPTIVE
Businesses in every industry are currently undergoing massive digital transformation, creating opportunities to leverage emerging technologies and enable innovative approaches to business practices. But these are not just flash-in-the-pan technologies – they are long-term shifts in the way we use technology, requiring a strategic approach if they are to provide bottom line benefits. Colin Beaney, together with Patrick Zirnhelt from IFS North America, pick out three of the top technologies set to disrupt the enterprise asset management landscape – the Internet of Things (IoT), machine learning and the next generation of mobility.
The Internet of Everything Businesses are reaping the rewards of IoT – by 2020, the number of connected devic- es worldwide will top 50 billion. IoT is allowing organisations to collect more information, quickly respond to changes and act on new business intelligence. But, as we move into a world where everything is connected, a new ‘smart infrastructure’ will need to be put into place, with plan- ning and asset management tools capable of dealing with the scale and lifecycle of dispersed – but connected – assets.
THE CONNECTED EAM — PROACTIVE ACCIDENT AVOIDANCE This is where modern enterprise asset management shows its worth. For exam- ple, IP-enabled remote cameras are start- ing to make their way into the cockpits of
large earthmoving vehicles used in mining. These cameras can be connected to cen- tralised software which uses facial recog- nition to monitor for signs of tiredness and either trigger an audible alarm to alert the driver or produce a response from HR to pull the operator from active duty. While this will minimise the risk of
serious accidents, it will of course have knock-on effects on operations with increased downtime as a machine sits unmanned. With the right EAM in place, however, a dynamic scheduling tool can automatically adapt and quickly resched- ule a suitably qualified and available alter- native employee. This is the type of sce- nario that will play out across the entire enterprise. With more real-time data facil- itating real-time operational decisions, it’s the EAM’s job to produce business actions that minimise disruption to operations.
THE NEW FORMULA: CBM + IOT IoT is taking this approach one step fur- ther with condition-based maintenance (CBM), enabling the EAM solution to automate intelligent responses to potential faults. CBM monitors the health of assets to determine if any maintenance is required and create a maintenance history for ongoing analysis. Sensors in the asset monitor for specific indicators which sig- nal asset deterioration or performance decrease. This data can be captured, shared and
analysed before being fed directly into the EAM to get an enterprise-wide view of asset status and automatically schedule work-orders – all in real-time. In addition to this, it allows organisations to build up aggregate data sets on performance and operations which can be analysed to inform repair or replace and other asset lifecycle decisions.
Machine Learning and
Predictive Analytics IoT is expanding rapidly, but the impor- tant question decision makers need to ask is: ‘What actionable intelligence is it pro- ducing?’ If IoT is the capture, exchange and storage of information, then it is the analytics capabilities of enterprise solu- tions which will be providing the answers to that question. EAM software today must not only accept incoming data from connected devices – it must also put execu- tives in control of that information, enable them to drill through to actions taken as a result in the IFS software, and configure observation workflows.
14 IFS WORLD
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32
