Price is perhaps the most effective tool to influence a purchase decision and the high cost that results from unplanned downtimes can destroy this competitive advantage. Wall Street Journal quantifies this cost by highlighting that industrial manufacturers lose over a whopping $50 billion each year to unplanned downtimes. It also brings to light the fact that equipment failure is the cause of this downtime 42 percent of the time.
Traditionally, manufacturers have tried – albeit unsuccessfully most of the time – to handle equipment downtimes by either replacing parts much before their ‘end of life’ or making calculated guesses, based on previous experiences, to predict downtimes. Suffice to say that the approach is both cost intensive and fraught with risks. However, IoT and advanced analytics are fast disrupting the existing maintenance paradigm, pushing us towards the age of predictive maintenance.
Tradeoffs have no Place in the Predictive Maintenance Era
Traditional maintenance strategies – reactive, planned or proactive – have one thing in common and that’s a tradeoff:
- Reactive: Max utilization of machine parts versus potentially damaging the whole machine
- Planned: Lesser risk of machine breakdown versus increased part replacement costs
- Proactive: Longer machine part life versus increased maintenance cost
Predictive maintenance eliminates these tradeoffs. Being reactive has no place in predictive maintenance. It minimizes planned downtime and backs proactive maintenance with data so that associated costs can be contained.
Predictive Maintenance Analyzes Physical Assets Digitally
Internet of Things powers predictive maintenance by facilitating a cyclic flow of digitized data from physical assets. It’s a physical-digital-physical cycle.
Here’s how it works:
- Sensors attached to the machine generate data pertaining to its functional soundness.
- This data is digitized and analyzed to generate insights.
- The insights are then relayed back to the machine so that physical actions can be taken.
This cycle enables organizations to take actions in advance based on the data harnessed from the machines. The approach makes organizations more agile and their operations smoother without disruptions. It also offers a holistic view of all the assets within the manufacturing setup.
How Predictive Maintenance Achieves Operational Excellence
The obvious benefit of adopting predictive maintenance is its ability to identify and manage risks associated with assets that can negatively affect operations. In addition, it helps in the following ways:
- Make use of engineering, science and math principles to bring dependability and efficiency to products and production processes.
- Analyze multiple operational data sources and types – streaming or at rest, structured or unstructured, batch or real-time.
- Create a continuously improving predictive model using data from IoT sources as well as EAM systems that factor in thermography, vibrations, lubricants, infrared, passive ultrasound, and motor circuits.
- Build a data-based multivariate metric pertaining to ‘remaining life’ that is statistically significant.
Predictive maintenance today is industry-ready and empowers organizations to scale new heights of business success. With the advent of new technologies, the time is right for enterprises to make the predictive-maintenance transformation.
The ITC Infotech Advantage
ITC Infotech helps its customers transform their predictive and preventive maintenance approach. We achieve this through our experience and expertise in IoT and data analytics combined with our engineering expertise. Our asset health solutions ensure that manufacturers extract the most out of their production equipment. By leveraging tested platforms such as IBM Watson, we make adoption of predictive maintenance 4.0 a seamless process.