Companies that operate with complex machinery, robotics and technology within their supply chains know that maintenance is a must-do. Continual efficient working of machines cannot continue without it. Maintenance is frequently a juggling act, trying to balance production levels with downtime and the cost of downtime. Therefore, it’s much more efficient and effective to plan maintenance. This ‘preventative maintenance’ is undertaken with the view to reducing down time by giving machines regular reviews and maintenance work.
This is the state of play with which we are familiar. However, there is a new common practice called ‘predictive maintenance’ which is emerging. This, once again, is about reducing the impact of machinery maintenance and eliminating inefficiencies in the maintenance process. Ultimately, it’s also about cost-effectiveness.
What is Predictive Maintenance?
Predictive maintenance is an approach combined with technological capability. It’s about carrying out the maintenance required on machines and robotics before they actually break down. It’s about being able to see which machine is soon to pose a problem in the future, and fixing it now, rather than waiting for the inevitable to happen. In many ways, it’s about dealing with maintenance in real-time.
Alongside this comes scheduling. Whereas previously we focused on regular maintenance scheduled to occur, now the schedule reflects when it actually should occur. This means maintenance only happens when it needs to happen, rather than when it might be needed.
Inevitably, this requires technology itself in order to work, predominantly the internet of things (IoT). Various sensors are wirelessly connected to a wider console for the purpose of collecting and utilising data. Machine learning allows analysis of the data to predict when maintenance will be required.
The data which is collected is quite variable, but all with the aim of predicting changes in the efficiency and working of the machine which signal a wider problem. This may include elements such as vibrations occurring, or thermic sensors to predict temperature changes, sensors to monitor lubrication, and even ultrasonic detection.
What’s notable, are the changes which the sensors aim to pick up are incredibly subtle. They aren’t necessarily ones which could be picked up by human eye or knowledge. This is why it must go hand in hand with machine learning.
Machine learning is what enables us to take the data collected by the sensors and compare it to the usual efficient data for that machine. It can then analyse the data to be able to predict exactly what the problem will be, and even when it may occur. This allows maintenance to not only be predicted, but also scheduled more appropriately for business needs.
The Positive Impact of Predictive Maintenance on Supply Chains
Some of the benefits of predictive maintenance in supply chains are easy to see at first glance. Overall, predictive maintenance should easily reflect an excellent return on investment simply because it is managing downtime much more effectively and efficiently.
It’s not only good practice. Predictive maintenance is about taking a proactive approach to the usual bottlenecks created by machinery downtime. This contrasts with the old-school reactive approach that can be considerably harder hitting for businesses.
Businesses employing predictive maintenance techniques aren’t wasting time on regular maintenance which isn’t necessarily appropriate or well-timed. Problems are solved when they are smaller and less impactful. Repetitive and unnecessary costs are reduced and attention is only given where and when it is needed.
There are additional benefits to using the IoT in this way to enable predictive maintenance. With this use of data comes a clear audit trail whereby you can see exactly what has happened, and what needs to happen. This could even prove particularly useful in the event of needing to make claims against insurance or warranties.
Of course, it follows that the more machines that are involved, and the more complex the system, the higher the initial introductory costs. However, as the technology becomes more easily available, you can implement predictive maintenance on a scaled basis according to your prioritised needs.
It’s therefore sensible to start by introducing predictive maintenance on your most critical machines, the ones on which everything else hinges. These machines, the ones which would cause most disruption to businesses in the event that they break-down, need your primary attention. This is where to invest the money to start with.
It’s also important to realise that predictive maintenance sensors aren’t suitable for all machines. They are most useful on machines where the IoT sensors can easily pick up changes which can then be analysed in light of machine learning.
Furthermore, it is important to ensure you also have the labour skills to utilise the information created by the sensors. Whilst the vast quantities of data can be handled by a machine, you still need someone with the right understanding and insight to know how to act on the information. This is a skilled role involving skills including both technological knowledge and intimate knowledge of the specific machines.
The Future of Predictive Maintenance
We’re familiar with things changing rapidly in our warehouses and plants. Keeping pace with change is a challenge in itself for businesses which want to thrive. Predictive maintenance is just one change and it is not enough by itself. However, used with other developments and as just one facet of embracing enhanced technology, it can help us to move forwards with the future.
At the moment this isn’t being widely adopted. Many businesses aren’t knowledgeable of their complete assets, and how they impact on one another. Without this, it is impossible to even begin considering predictive maintenance. However, it’s becoming more sought after.
An example of predictive maintenance in action is In.advance™ software developed by Tend. US manufacturers are saying that this software could save over $58 billion in downtime costs each year. It’s therefore definitely one trend to keep an eye on with a view to using it to help efficiency in your business.