Business organisations are no longer required to simply react — they are also expected to predict and avoid. And with data available to all the competition with just a click away, mistakes are costly. With predictive maintenance, companies can go beyond learning the whats and whys in the business, and discover more insights about the future trends.
A recent study has shown that organisations spend around 80 percent of their time reacting to issues rather than preventing them.
Predictive maintenance systems — software solutions or tailor-made-programmes — help in avoiding costly downtime and thus reduce overall costs. Driven by predictive analytics, most of the maintenance solutions are capable of even minor anomalies and failure patterns to determine flaws. The assets and operational processes that are at the greatest risk of problems or failure are pointed out immediately. This early identification of potential concerns helps you deploy limited resources more cost effectively, maximise equipment uptime and enhance quality and supply chain processes.
How Businesses Are Using Predictive Maintenance:
In an event organised by NASSCOM and GE Digital in July this year, industry experts highlighted the importance of building a digital business technology platform across a global organisation. They illustrated the use of predictive maintenance, along with Industrial Internet of Things (IIoT) in the shipping industry. While data visualisation also played a huge part in the process, the experts said that a cargo ship the size of the Titanic could work very well with just eight employees because of this technology.
“The fact that while the human eye sees just a machine the digital eye augments that with analytics, deep insights and connects it to people,” said a speaker.
Predictive analysis goes hand-in-hand with data collection, data modelling and data visualisation. Some of the key pointers that the business organisations may get from predictive analytics and maintenance systems include fraud detection, optimisation of marketing campaigns, improvement of operations and minimisation of risks.
Why Do Businesses Need Predictive Maintenance:
Some of the key reasons why medium as well as large scale business organisations require predictive maintenance analysis are:
- Reductions in the total cost of maintenance
- Fewer urgent and emergency interruptions to operations due to equipment breakdowns
- Level workloads and a stabilised work force
- Reductions in the total labor needed to maintain facilities in the required condition
- Controlled reductions in the inventory of materials and spare parts
- Increases in the volume of work that can be planned and scheduled repetitively, and a decrease in high priority, randomly occurring and unscheduled work
- Reduced unnecessary damage to equipment
Predictive Maintenance vs. Preventive Maintenance:
The two systems usually go hand-in-hand in medium to large scale industries, where a moment’s delay or problem may cause huge losses. The difference between Preventive and Predictive Maintenance is that preventive maintenance tasks are usually completed when the machines are shut down and predictive maintenance activities are carried out as the machines are running in their normal production modes, that is, in real time..
Areas Where Predictive Maintenance Is Crucial:
Depending on the complexity of a business or a product, predictive maintenance systems are used in co-ordination with systems based on predictive analysis, machine learning, internet of things and artificial intelligence. Some of the key areas where predictive maintenance can be crucial are:
- Avoiding costly disruptions by predicting equipment malfunctions
- Tracking insights from sensor data to improve product quality and reliability, Optimising resource management
- Prevention of unplanned downtime
- Scheduling maintenance in larger organisations
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Prajakta is a Writer/Editor/Social Media diva. Lover of all that is 'quaint', her favourite things include dogs, Starbucks, butter popcorn, Jane Austen novels and neo-noir films. She has previously worked for HuffPost, CNN IBN, The Indian Express and Bose.