Plant maintenance is commonly viewed as cost. Many companies put any form of maintenance on hold in order to save money.
Plant maintenance is commonly viewed as cost. Many companies put any form of maintenance on hold in order to save money. These companies end up having higher maintenance costs than their peers who plan and/or predict maintenance activities. Predictive maintenance allows companies to spend a defined amount of money in the beginning that will lead to big savings in the end.
It is common practice for maintenance teams at manufacturing plants to perform what is known as “reactive maintenance” tasks, which refers to fixing a machine only when a repair situation appears. These types of repairs can sneak up on a plant causing a domino effect on the whole production line. As Kenneth D. Peoples of IDS-Boeing Wichita describes, during the machine’s unsuspected and unscheduled downtime, it could “begin to cost your operations money in lost productivity, upstream process backup of inventory, downstream process delays to finished product, asset operator labor to rework the part, operator labor lost to machine downtime during the failure, maintenance labor to perform reactive failure corrections, and so on and on.” This kind of “if-it’s-not-broke-don’t-fix-it” mentality can cost a plant up to four times as much as maintenance work that is planned.
…Run-to-failure is a form of reactive maintenance that is extremely expensive!
Run-to-failure is a form of reactive maintenance that is extremely expensive. Instead of taking any preventative measures, according to R. Keith Mobley, the author of An Introduction to Predictive Maintenance, the machine is left to run without any major repairs made until it completely breaks down and a new piece of equipment needs to be purchased. Since maintenance teams will not have a way of knowing what kind of maintenance will be needed when a failure comes, they must keep a large, costly stock of parts ready for any type of reparation. The other option, according to Mobley, would be “to rely on equipment vendors that can provide immediate delivery of all required spare parts.” Many compare this method to never changing the oil in your car and driving it until it breaks down. If you do regular oil changes, there will be no need to have to buy a new engine.
Preventative maintenance, on the other hand, is done on regular time intervals in order to try to catch a failure before it actually occurs or affects the entire equipment. This method, though, may lead to unnecessary maintenance bringing in high costs and loss of production from taking the equipment out of service so often. With predictive maintenance (PdM), maintenance is performed on equipment when needed, but not when it’s too late. Using special condition monitoring tools and methods such as vibration analysis and thermography, one is able to determine and schedule the ideal time to take equipment out of service for maintenance, instead of interrupting its daily production. Though there may be immediate costs with training employees to use predictive maintenance technology and renting or purchasing condition monitoring equipment or service, with a predictive maintenance program in place, the high cost of resources from reactive and preventative maintenance is significantly reduced along with the cost of lost production and labor.
ROI of Predictive Maintenance (ACQUIP)
Number of Machines 100
Average HP 50
Days of Operation in a Year 320
Average Cost of Power (kw/hr) $0.045
Average Cost of Labor/hr $50.00
Estimated Annual Savings
Power loss due to Misalignment (1% efficiency improvement) $12,890.88
Machine damage due to excessive vibration ($2.50/hp) $12,500.00
Production Loss due to unplanned failure (5% increase in uptime x $0.50/hp-day) $40,000.00
Labor Savings due to unplanned Repairs $19,200.00
Mobley, R. Keith. An Introduction to Predictive Maintenance. 2nd ed. Woburn, MA: Butterworth-Heinemann, 2002. Print.
Peoples, Kenneth D. “Understanding and Implementing Predictive Maintenance Excellence.” Reliability Web 2010: n. pag. Web. 25 Aug 2010.