Imagine you purchased a new economy car three months ago. To get around, you alternate between riding a bike, walking, and driving your new car. So in the three months since you’ve owned the car, you’ve driven about 1,000 miles on it. However, the car’s manual says to get the oil replaced every three months or 3,000 miles. Since it’s been three months, you dutifully take your car to the shop for a costly oil change that is supposed to keep your car running in great condition. This is an example of preventive maintenance.
Now imagine, you own a luxury car. Maybe it’s a BMW that is equipped with condition-based service indicators for your engine oil and air filters among other parts. You drive the car six months, put around 5,000 miles on it, and then an alert comes on that says you have 500 miles left before you must change the oil. This is an example of predictive maintenance. It prevents breakdowns and gives you service reminders that are a reflection of how much you actually drive your car with enough advance warning to repair the issue before the machine fails.
In manufacturing, machine, and energy companies, preventive maintenance is already popular and can be used in conjunction with mobile workforce management software, but predictive maintenance has its own set of values that it delivers to companies too. Here’s how to tell the difference between the two types of maintenance:
How Maintenance is Triggered
Preventive maintenance is triggered by time, events, or meter readings. The age of a piece of equipment in addition to manufacturer service recommendations is also taken into consideration for preventative maintenance. Another way to say preventive maintenance is planned scheduled maintenance. However, like in our car example, this time-based maintenance approach may not accurately reflect the usage of a piece of equipment, and could lead to unnecessary maintenance fixes regardless of the actual state of the equipment or parts.
On the other hand, predictive maintenance is based on the actual condition of the equipment rather than time or age factors. It is used to predict machine failures before they occur, and also gives company enough time to schedule a future service appointment in advance. This allows field service technicians the opportunity to service the machine and prevent the failure before it actually occurs.
Differing Maintenance Techniques
With preventive maintenance, since fixing machinery is based on time and the breakdown rates of similar parts, the field service tech will replace or repair parts based on their expected failure dates. This date is not based on performance data of the specific machine.
For predictive maintenance, various advanced techniques including infrared thermal imaging, vibration analysis, and oil analysis can be used to predict failures. For example, if your business uses mechanical equipment and electrical systems, thermal infrared imaging can scan, visualize, and analyze the equipment’s temperature. You can literally see which parts on the equipment are “running hot,” which is invaluable information to both manufacturers and field service technicians.
Costs and Savings
As far as maintenance costs are concerned, preventive maintenance costs $13 hourly pay per annum while predictive maintenance costs $9 hourly pay per annum, making predictive maintenance a cheaper option. Also, given that preventive maintenance sometimes means parts are replaced when there is no need, if the technician happens to cause damage while servicing the machine during an unneeded service call, unnecessary maintenance can be even more costly.
But predictive maintenance has several cost savings that range from minimizing the time equipment is not working to cutting down on the price of spare parts and supplies.
While the investment into a predictive maintenance program can be costly to install, it results in reduced maintenance costs and downtime and delivers 10 times the ROI for the oil and gas industry alone, according to research strategy consulting firm Roland Berger. For example, predictive maintenance is often used for wind turbines since wind farms often traditionally have high operational costs. And field service software can be used along with predictive maintenance tools to make sure your machinery keeps running efficiently.
Predictive Maintenance and the Internet of Things
With the increased networking of machines and manufacturing facilities in the Internet of Things, predictive maintenance is becoming more and more important. Sensors facilitate the easy monitoring of machine conditions, cloud storage systems and data bases enable the long-time archiving and analysis of machine data for diagnostic and maintenance purposes.
By means of real-time monitoring, a comprehensive data pool, and state-of-the-art analytical methods, machine-specific malfunctions can be predicted by 70 %. This enables field service technicians to intervene in time to effectively prevent failures as well as unnecessary maintenance measures, downtimes, and costs.
However, the benefits of the Internet of Things and predictive maintenance are not confined to new facilities. Older machines with life cycles of 50 or 60 years can also easily be integrated into predictive maintenance schemes by means of targeted retrofit measures.
Predictive maintenance is an excellent way to reach a higher level in field service
However, offering predictive maintenance alone is not enough, as customers expect service in real-time. The main challenge in offering predictive maintenance in real-time is in having a workforce large and flexible enough to meet this demand.
The Evolution Of Field Service describes the innovative approach companies are using to fulfil real-time service expectations: