Maintaining equipment is expensive; in fact, McKinsey says that maintenance can account for anywhere between 20% and 60% of operational expenditure (OPEX). As a result – and particularly at a time when the economy is in fluxmany businesses are looking to optimise wherever possible. That means reducing costs, certainly, but it also means using resources more efficiently and addressing how maintenance is approached. 

Shifting to predictive maintenance can tackle these challenges but making that change can be complex. In this article, we’ll explore how that transformation can be achieved, and the role computerised maintenance management systems (CMMS) play in its success.


Using CMMS for Predictive Maintenance

What is Predictive Maintenance?

Reactive or scheduled maintenance is more resource-intensive than preventative maintenance. Think about it: if everything is reactive, then any downtime, production disruption or other breaks in operations are unplanned and have significant consequences for the wider business.  

Plus, reactive organisations may face delays sourcing parts, or be forced to keep a large inventory just in case. Little wonder that McKinsey estimates that reducing unplanned outages can contribute to increasing profitability by between four to 10%, depending on the organisation.

And while scheduled maintenance may make it easier to plan downtime and minimise production disruption, it’s still a break in operations that may not be necessary.  

Predictive maintenance removes that uncertainty and waste. It is a proactive approach to maintenance that uses a combination of real-time data from the machinery in question, historical performance data, and analytics to forecast when failure is likely to occur. In operating more predictively, businesses can then plan in maintenance pauses, safe in the knowledge that downtime is necessary. 


What Are the Benefits of Predictive Maintenance?

The benefits of predictive maintenance include: 


Minimising maintenance time 

By only acting when required, businesses spend less time on disruptive routine stoppages which may have a significant impact on machinery performance. Being able to predict when maintenance is required, means production can be planned around the downtime, or rather, the downtime can be planned for a time when work isn’t as busy, or outside of working hours.  


Minimising the need to stock and store parts just in case 

Storing anything costs money. There’s the sunk cost of having acquired the parts sitting idle, and then there’s the ongoing expense of storing them appropriately. Just in case does mean parts are always on hand, but it’s still extremely inefficient and indicative of a wasteful approach to maintenance. Of course, depending on the rarity of the part, how easy it is to get hold of, and how often it is required, it might make sense to have a stock of certain items even with a predictive maintenance approach. Most parts, however, shouldn’t be stored just in case. 


Maximise production hours

Downtime is money, so manufacturers need to minimise the periods in which their machinery sits idle. Predictive maintenance allows businesses to plan when they will have downtime and adjust schedules accordingly so that there is as little disruption as possible.


Maximise machinery lifespan

Maintenance isn’t just about fixing problems, but about extending the lifespan of the machinery. Predictive tools use data and analytics to not only predict a potential failure, but first and foremost, to provide a visual indication of a change condition from a monitored asset. By monitoring your assets with sensors, your organisation will have the ability to collect data and send an alert/notification when the asset condition changes, allowing you to take the appropriate action in advance before it can fail. 


What Are the Obstacles to Implementing Predictive Maintenance? 

Of course, nothing is perfect. Implementing predictive maintenance comes with its own challenges. These include: 


Lacking the right data 

Predictive maintenance needs two main types of data to work effectively: real-time and historic. The obstacle is, some businesses haven’t previously captured data, so they don’t have the historic information required, while others might not have a way to comprehensively capture real-time data.  


Lacking the knowledge to implement it 

While predictive maintenance uses technology, ultimately there needs to be a human workforce with the capabilities to not only implement the processes and tools effectively, but also to understand what the data is telling them. These data skills are in short supply – one study found that 74% of employees report feeling unhappy when working with data.  

There are a few factors contributing to this knowledge gap. For one, new technology is constantly being implemented in many corporations – often without comprehensive training. This makes the experience of working with within the software confusing and often frustrating. Secondly, the Boomer generation is consistently leaving the workforce and retiring, taking their “tribal knowledge” with them.  


Lacking the resource to manage it 

Building on the lack of knowledgeable talent is the challenge of not having enough resources to manage predictive maintenance. While it is an approach that optimises resources, it still needs that human element to make sure that activity is being actioned – something that again can be in short supply. 


How Do Computerised Maintenance Management Systems Help Predictive Maintenance? 

The benefits of implementing a predictive maintenance program might be clearbut so are the challenges. How, then, do manufacturers overcome these to implement predictive maintenance effectively? 

One approach is via a dedicated computerised maintenance management system. Put simply, a CMMS is a system that stores maintenance data for centralised access, automates tasks for improved efficiency, and helps manage maintenance processes. It can be used for all sorts of maintenance, including factory equipment, fleets, handheld devices and production machinery.  

That said, in order to utilise a CMMS to create a truly predictive environment, all assets and systems must be connected and working together in the system so users can share, analyse and act upon data. This requires advanced systems and practices, including:  

  • Real-time monitoring of asset condition and performance 
  • Analysis of historical and real-time work order data  
  • The Internet of Things (IoT) connecting sensors and assets on the floor  
  • Accurate MRO inventory data 
  • Fully connected systems and tools so work orders contain all pertinent information  

And that’s just scratching the surface. Unsurprisingly, there’s a lot that organisations get wrong when aiming to implement such a system. That said, if you get it right, a CMMS provides multiple benefits, including: 

  • A way to capture and store data so that it can feed into multiple maintenance programmes while providing central oversight 
  • The ability to automate certain tasks, freeing up operators to focus on more important work (such as analysing data outputs) 
  • Transparency of multiple activity streams, allowing for optimising resources and ensuring that any planned downtime is used efficiently 


Learn More About Computerised Maintenance Management Systems

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To find out how you could implement a CMMS to support your shift to predictive maintenance, get in touch with one of our experts.