Whether your business is small, medium or large, effective equipment maintenance is crucial. The CMMS market is flooded with solutions promising cost and time savings but also demanding a solid investment. Every maintenance professional will tell you that predictive maintenance and preventive maintenance are absolutely worth it and of highest importance for asset management.
But how to choose the right maintenance strategy for your equipment according to your budget?
Latest Trends Reshaping the Industrial Maintenance Sector
Industrial Internet of Things (IIoT), big data, PdM and CMMS are already completely revolutionizing maintenance through their powerful proactive approach. Ensuring that an asset will function in the required manner with a minimum of maintenance over its life cycle is more than enough as an argument for many business owners to get all the latest available resources and follow the latest automation trends.
Industry 4.0 makes the factory from today smarter, stronger and more flexible than ever before. Equipment condition monitoring has been available for many years thanks to different controllers, sensors and other tools and methods (e.g. vibration and oil analysis). But what is really new now and is tremendously emerging is the capability to analyze big data and create predictive algorithms with the help of next-gen CMMS to anticipate and prevent a possible failure. This is obviously the absolute game changer in the maintenance world.
Mobility Work’s calendar feature allows you to schedule all your preventive and predictive maintenance tasks.
Should you then just forget about your established preventive maintenance routines and put all your efforts and money into a new predictive strategy? Preventive and predictive maintenance, rather than opposed strategies are complementary.
Predictive Maintenance vs. Preventive Maintenance
The table below shows the most important features of both most important maintenance types.
Preventive maintenance | PdM | |
What is the aim of it? | To maintain equipment based on the assumption that it will degrade within a given period of time, defined by the manufacturer or expected life statistics. The equipment will be replaced or rebuilt before the expected failure point. | Tracks trends that forecast circumstances; predict when equipment failure might occur and prevent its occurrence by maintenance actions. |
Who is performing it? | Maintenance technicians and machine operators | Condition monitoring experts, analysts, maintenance technicians |
How often? | Regularly performed on a specific schedule to avoid the risk of machine failure | Tasks are performed only when warranted on pre-specified scheduled dates |
Needed resources | CMMS | Equipment to conduct predictive methods as thermography, vibration analysis and others; analytic tools, CMMS |
Production cycle impact | A piece of equipment is shut down on a regular basis. | A piece of equipment is only shut down right before a failure. |
Can Every Business Afford PdM?
Predictive maintenance is hard and costly to implement and therefore it is mainly adopted by large enterprises. Skilled professionals spend a lot of time to master the proper predictive model and continuously update it to ensure its accuracy and efficiency. If not properly implemented and followed, a predictive model can easily lead to mistakes, which can be critical for a wide range of industries as aerospace, automotive, defense, energy…
On the other hand, every effort to adopt predictive maintenance is worth it and the potential gain is huge. It improves plant reliability, machine availability and production safety. It totally prevents catastrophic breakdowns, delays and other bottlenecks.
Why tend to turn more to Preventive Maintenance?
It is obvious that predictive maintenance is quite expensive for many small and some medium sized companies. It requires a lot of resources (material, time, expertise). Regarding preventive maintenance startegies, some companies appeal to external service providers. In the long term this might not be the best solution. Appealing to an external maintenance service provider slows down the production cycle and finally costs just a bit cheaper than setting a preventive maintenance routine for a small business. Therefore, establishing in-house preventive maintenance service should be one of the first priorities for small-sized companies owners.
Add tasks to your equipment from your mobile app.
Mobility Work will cost you only 30 euro/per month/ per user to manage maintenance services, schedule interventions, provide the whole machine’s history, ease spare parts management and many many more. Mobility Work doesn’t require any additional training or set up and its adoption in the workflow is fast and easy.
Even if recently predictive is far trendier than preventive, SMEs should first master the preventive model to establish strong and reliable routines. Regular or time-based maintenance is highly efficient and significantly reduces the chance of system breakdowns. Following the manufacturer’s recommendations on equipment maintenance can increase machine’s life by 30 percent.
Why Should Large Businesses Turn to PdM
Predictive maintenance analytics are based on machine condition monitoring data and combined with highly efficient CMMS as Mobility Work, turn into the Holy Grail for every company.
PdM and CMMS: How to Get the Best Out Of It
Large businesses have already implemented solid preventive routines and should be ready to leverage their maintenance level in order to improve their competitiveness. Predictive analytics have already proven their power for solving real-world problems in economics and science. Now, the industrial world has the great opportunity to benefit from their accuracy and great potential.
Choosing the right equipment maintenance approach depends a lot on your budget and the size of your business. Predictive and preventive maintenance are non-interchangeable and are both part of the successful maintenance program. Mobility Work can adapt to the needs of every company, whatever the size, budget and industry background.