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Using predictive maintenance technologies to optimise enterprise asset performance

What is predictive maintenance?

Predictive maintenance technologies let you hear what your enterprise assets are trying to tell you. The machines in your factories, your fleet of trucks, your industrial equipment – they’ve been talking to you for years. They’ve been telling you when they’re about to break down and what they need to run longer and more smoothly.  


Predictive maintenance allows businesses to anticipate failures and schedule maintenance when and where it’s immediately needed. It arms businesses with the information they need to push for peak performance from their valuable assets yet remain confident that they’re not pushing them too far and risking a costly breakdown.

Predictive maintenance definition: Predictive maintenance seeks to prevent equipment failure and downtime by connecting IoT-enabled enterprise assets, applying advanced analytics to the real-time data they deliver, and using the resultant insights to inform educated, cost-effective, and efficient maintenance protocols.

Why is predictive maintenance so important to today’s businesses?

Modern businesses are in a time of unprecedented change and competition. The Amazon Effect has led to a rapid rise in consumer demands for control, personalisation, and speed. A shifting trade and political climate has left many companies struggling to maintain affordable supply and manufacturing relationships. And as more and more businesses undergo digital transformation, competition is rising and the margin for error is increasingly slim. As a result, today’s business leaders are looking to gain a competitive edge through smart solutions, which predict when asset maintenance is needed, help increase cost efficiency, and streamline their often complex enterprise asset management requirements. 


Swiss Federal Railways use case

Hear how predictive maintenance helped achieve operational excellence.

What is the difference between reactive, preventive, and predictive maintenance?

The difference in these three maintenance models lies not so much in how the maintenance tasks are undertaken, but when.

  • Reactive maintenance: This is essentially the act of doing nothing until something breaks. As a maintenance strategy, this is not usually practiced by large companies for obvious reasons. However, it can be an unintended practice if certain parts and components are left out of the regular rotation of traditional maintenance schedules. Reactive maintenance always happens after the fact.
  • Preventive maintenance: This is informed by past performance and the knowledge and experience of engineers and operators. It includes routine, periodic, planned, or time-based maintenance. Indeed, it often prevents breakdowns, but unfortunately it can be inexact, which may lead to expensive maintenance before it’s needed or to unnoticed weaknesses in the maintenance process. Preventive maintenance happens at times that are pre-set, often long in advance.
  • Predictive maintenance: This is possible when Internet of Things (IoT) networks integrate all enterprise assets into a live ecosystem. The ability to transmit and analyse data in real time, means that live asset condition – rather than calendars – become the foundation for maintenance protocols. Predictive maintenance happens in real time, exactly when and where it’s needed.

The following chart (adapted from Deloitte) displays the progression of technological capabilities throughout industrial revolutions and the resulting impact on maintenance strategies and equipment effectiveness.

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How do predictive maintenance and IoT analytics work?

The first step in the predictive maintenance process involves the collection of real-time data and information from connected IoT network assets across the business. This data must then be stored and managed in such a way that it can readily be processed, accessed, and analysed. The “predictive” component comes into play when artificial intelligence (AI) and machine learning technologies are applied to the data to make it start telling a useful and actionable story.


There are four basic stages to the architecture of predictive maintenance and an Industrial IoT (IIoT) network:

The four steps of the predictive maintenance process

The predictive maintenance process

  1. Sensing and gathering data using predictive maintenance technologies (for example, thermal imaging or vibrations)
  2. Transmitting that data – in real time – across the network to a central business system
  3. Applying intelligent technologies like AI and machine learning analytics to that data, to get it to deliver the most useful and relevant insights 
  4. Taking rapid action on those data-driven insights to establish the maintenance and response protocols (both human and automated) required

Monitoring asset conditions and leveraging predictive maintenance technologies

Predictive maintenance is made possible through cyber-physical systems that help to integrate machines and enterprise assets into an intelligent IoT network. It starts by identifying the asset conditions that need to be monitored, then fitting sensors and establishing an IoT network, and, finally, gathering and analysing data from that network to deliver actionable findings and insights. Identifying these conditions to be monitored is the first crucial step toward transforming a business’s enterprise asset management through predictive maintenance. 


Monitoring conditions


Initially, managers must establish the conditions that need to be monitored for each machine. That analysis may be visual, auditory, thermal, or – most typically – a combination of those criteria and more. The technological step at this point consists of determining the correct sensors and monitoring tools to be fitted: 

  • Vibration analysis: Small changes in vibration patterns may indicate imbalance or misalignment, while high vibration levels may indicate impending bearing or other problems. Vibration analysis can give early warnings of failure and is particularly useful in detecting imbalance, misalignment, mechanical looseness, or worn or damaged parts.

  • Sound and ultrasonic analysis: Under normal operation, most systems create steady sound patterns. Changes in the reference sound pattern can indicate wear or other types of deterioration. Ultrasonic analyses can also give information about the overall health of the system by translating high-frequency sounds (such as those produced by steam or air leaks) into the audible range.

  • Infrared analysis: As with ultrasonic analysis, thermography also uncovers the hidden by using infrared analysis to translate temperature changes into a visible spectrum. Even very subtle changes to normal operational temperatures can warn of impending problems.

  • Fluid analysis: Beyond simply monitoring the levels and temperature, the physical and chemical analysis of fluids can give valuable information about the condition of mechanical components. By seeing the rate of degradation in coolants and lubricants, preventive steps can be taken as soon as these insights warrant.

  • Others: Other predictive maintenance technologies are specialised for various unique industrial needs. They include: laser alignment, electrical circuit monitoring, crack detection, corrosion monitoring, electrical resistance changes, and other industry-specific means of measuring corrosion or deterioration.
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Predictive maintenance technologies


Once the above criteria are established, the appropriate sensors and monitors must be fitted and connected to a central business system, most commonly an enterprise resource planning  (ERP) system, via a cloud-connected IoT network. Finally, the necessary AI-driven software solutions must be in place to support the various algorithms and analytics processes necessary to deliver actionable insights and recommendations from the data gathered.

  • IoT network:  When enterprise assets are augmented with sensors, processing ability, and other technologies, they are able to send and receive data – usually via cloud connectivity – to and from a central business system. This comprises an IoT network and underpins the predictive maintenance strategy.

  • IoT gateways: Many older assets still work perfectly well, yet their analog technology predates digital integration. These machines can be fitted with IoT gateway devices, which may include cameras, microphones, and thermometers, to gather and transmit real-time data on their operational states.

  • Cloud connectivity: Cloud connectivity delivers the on-demand availability of computer system resources. In an IoT network comprised of multiple industrial assets, it’s critical that multi-location data centres be integrated into a single database and system.

  • Modern database and ERP: Legacy disk-based databases are not well equipped to manage the voluminous and non-linear data that comprises Big Data and complex data sets. Furthermore, predictive maintenance uses AI and machine learning to perform advanced analytics on such data. This whole process is best served by a modern AI-powered ERP with an in-memory database that is fast, responsive, and almost infinitely scalable.

  • AI and machine learning: Pioneering computer scientist John McCarthy defines AI as “the science and engineering of making intelligent machines.” Machine learning is a subset of AI that uses algorithms to analyse and understand data. Predictive maintenance solutions are dependent upon AI and machine learning to not only sort, understand, and learn from enterprise assets’ operational data – but to extrapolate upon that knowledge with actionable recommendations and insights.

  • Advanced analytics: AI and machine learning power advanced analytics. Managers must determine the attributes and conditions to be assessed and the analytical outcomes that are desired. In this way, the algorithms that inform advanced analytics can be programmed to be as insightful and actionable as possible, and to best learn from data and new experiences over time.

  • Digital twins: A digital twin is just that: a virtual recreation of an actual physical asset. By creating digital twins, managers can visit any possible operational scenario upon the twin – without any risk of actual real-life damage to a costly machine or device. This helps to augment predictive maintenance by allowing machine learning and AI tools to incorporate and learn from experiences that have never even happened.

Examples of predictive maintenance use cases

  • Oil and gas sector: Oil drilling puts enormous wear on assets and can lead to great risk and danger in the event of a failure. Through real-time monitoring of changes in oil temperature and the speed of gearboxes in drilling equipment, predictive maintenance has greatly improved safety and reduced maintenance costs by up to 38%.
  • Automotive industry: On assembly lines, spot-welding guns perform about 15,000 spot welds each per day. By connecting welding guns around the world and collecting their operational data, auto manufacturers can gather millions of data points, leading to unprecedented predictive accuracy on the condition and state of these assets.
  • Domestic appliance manufacturing: Vibration measurements of the drum rotation in the dryer production process have helped predict malfunctioning or breakdown. This predictive maintenance application has eliminated manufacturing defects by 33% and reduced consumer maintenance costs by 27%.
  • Railroad asset management: “Voids” occur when an empty space develops under a track leading to potential delay or even derailment. Recent innovation has led to cab-based monitoring systems that can detect a number of variables as they roll over the rails. This has led to improved void detection and an overall rise in customer safety.
  • Steel industry: Anomaly detection is being used to gather real-time readings of the vibration, rotational speed, and electrical current (amperes) in the cold-rolling equipment used in steel processing. This application has led to a 60% improvement in equipment lifetime and greatly reduced losses due to downtime and delays.

Benefits of predictive maintenance

The implementation of predictive maintenance systems has led to impressive results across multiple industries.

Predictive maintenance increases productivity by 25%, reduces breakdowns by 70%, and lowers maintenance costs by 25%.


Interestingly enough, it’s not that the idea of predictive maintenance is anything new. For decades, businesses have strived to achieve greater predictability in their asset maintenance – but it has taken the rise of technologies like AI and modern ERP systems to deliver the capacity and functionality needed to achieve predictive maintenance solutions that really work. The benefits of which include:  

  • Better visibility across your entire operation: Increased visibility into field and other off-site assets. This allows OEMs and third-party service providers to offer better value and more informed services.
  • Lower maintenance costs and improved asset performance: Predictive maintenance consistently leads to the better use of existing resources, a reduction in downtime, and to the life extension of valuable assets.
  • More empowered teams: When asset operators, service providers, and supply chain managers are armed with data science and real-time analytics, they can develop maintenance schedules that work – they become planners and strategists rather than firefighters. 

Next steps to transforming your supply chain with predictive maintenance solutions

Many businesses have not changed their asset maintenance strategies in decades – despite having modernized other areas of their business. Changing longstanding processes is challenging and it can be difficult to get buy-in from your teams. The most successful business transformation plans begin with a good communication and change management strategy – to help engage your teams and break down silos. Speak to your software vendor to learn more about which tools and solutions will work best for your unique needs and to get you rolling with your road map and digital transformation journey.


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Maximise your asset performance with modern predictive maintenance tools.

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