Many industries rely on having equipment that is fully functional day after day. If there is any downtime it can cost money, and that’s why companies are always seeking to improve warehouse efficiency and to keep their factories up and running.
In the past, machinery maintenance was done after something broke. Sadly, it was a case of ‘locking the stable door after the horse had bolted’. Nowadays, predictive maintenance is a way of monitoring equipment to try and identify potential breakdowns BEFORE they happen. In this article, we will discuss what predictive maintenance is, and how it works to benefit companies.
What It Is
Predictive maintenance is a process where the machine learning (ML) algorithm looks at the historical data for each specific machine. It will take time before your system learns what works best for each individual piece of equipment.
Each manufacturer or part supplier may have their own definition of what PM means but generally speaking there are two types: PdM (Predictive) & PM (Preventative). Whilst many people think it’s a case of one type versus the other in terms of effectiveness, both can provide significant value when properly implemented into your operations strategy.
There are many companies with an online presence that can provide this valuable work function for you. You can enjoy predictive maintenance as a service, harnessing the benefits of both machine learning and artificial intelligence (AI). You can read more on their specialist websites, watch webinars and read related posts (e.g. on bridging the gap between predicted maintenance and diagnostics).
How It Works
Because predictive maintenance enables you to anticipate when your equipment will break down, you can schedule repairs to occur off-hours (when it’s not being used during work time) in advance. This can help reduce the impact of downtime on production and increase throughput on an ongoing basis.
You may be able to prevent breakdowns altogether by taking proactive steps in response to preventive maintenance guidance. It uses big data analytics and artificial intelligence combined with historical failure rate information to achieve this.
One way it aims to identify potential failures before they happen is through tracking anomalies in machine performance metrics such as vibration, temperature and pressure readings. The analysis looks at these numbers against how the machines have performed previously under similar operating conditions. This helps it to forecast future reliability and determine if changes need to be made proactively instead of waiting for a breakdown to happen.
An Example
A specific example of how predictive maintenance works is by using wear and tear models. They are equations that help you determine the service life expectancy for equipment with certain types of usage profiles. They can be harnessed in conjunction with your machinery’s operating history data to calculate future failure probabilities.
This information will let you know which assets need attention soonest so you don’t risk losing production or revenue when the machines malfunction. You might also want to do some digging into exactly what’s causing these malfunctions if they keep happening, as this could point towards bigger problems inside the machine itself.
The Need For Replacement
It’s also possible to look at how often certain components need replacing, such as tires on a forklift. In some cases, the frequency of these replacements may tell you that your equipment is being used in an environment beyond its design specifications (e.g. too much and too often!). For example, if tires are wearing out faster than their suggested lifespan, it may be because your employees have been driving over rough terrain to get from one area of the warehouse to another. This could help you to make some valuable changes to your premises or work practices.
The data you receive could indicate that it could be time for new vehicle maintenance procedures and training so that your workers understand how best to handle the existing equipment. In turn, this could minimize unnecessary damage and the need for future replacement parts to be purchased.
The Benefits
Predictive maintenance is a way of helping your business provide the best products and services. If it can reduce downtime for machinery repairs, it will also mean less waste and more profit for you! PM also enhances safety in the workplace, which feeds into staff morale. If you are taking care of the equipment so that it always functions correctly, there will be less frustration and they will feel you are investing in their safety and daily work experience.
Customer Satisfaction
Predictive maintenance can also generate an increase in customer satisfaction with your brand. This applies if they know their needs will be met at any time because nothing has broken down or failed. This creates an ongoing relationship with your clients, leading to better retention rates and higher profits.
Preventing Escalation
This type of technology can identify a problem and find out what is wrong before it becomes a bigger issue. This allows technicians to fix small problems cheaply and quickly. In turn, it helps prevent larger scale failures from happening which would inevitably result in more money being spent on larger repairs.
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Disadvantages
Predictive maintenance can be costly to implement and maintain. It often requires additional capital investment in the form of new equipment, software or personnel. It also takes away focus from the core business activities that provide revenue for your company. Further drawbacks include fluctuations in results due to environmental factors like weather changes.
There is also the need for long-term maintenance contracts, which can be especially problematic if you are unable to fill these positions. This could result in using contract workers who may not have the same degree of skills. Finally, variations between locations/factories make it challenging for a company’s systems to effectively manage them all from one location.
Whilst there is a financial investment required and certain challenges that come with it, predictive maintenance is definitely here to stay. Thanks to modern technology and recent innovations, it has become an effective tool to help companies continue to operate at their optimum both now and for many years to come.
