The market for predictive maintenance solutions is expected to exceed $14 billion by 2028. Solutions that enable real-time condition monitoring of equipment, better predict failures, and reduce downtime are employed today in many industries: from manufacturing, automotive, energy, oil and gas, and retail, to logistics, aerospace, and pharmaceuticals. All of these areas rely heavily on assets in their operations and have a strong interest in taking steps weeks or even months before any equipment failures occur. This is made possible by predictive maintenance software.
Understanding Preventive Maintenance vs. Predictive Maintenance
The reactive approach assumes that equipment is expected to operate until it fails and only then is the failure fixed. In the reactive approach, we try to mitigate these unexpected failures through regular, time-based service intervals, where the unit is stopped, and regular inspection and replacement of operating components is performed. This is supposed to prevent unexpected failures, but it often turns out that such a service stop was not necessary because the components were not yet worn out.
Before we go any further, it’s important to explain two terms that often appear in the context of equipment maintenance: preventive maintenance and predictive maintenance. How do they differ?
Predictive maintenance undertakings are referred to as proactive maintenance and are planned and scheduled according to equipment conditions based on sensor data analytics. Preventive maintenance is a subset of “proactive” but is less advanced than either “condition-based” or “predictive”.
Also read: Guide to predictive maintenance
What is condition-based maintenance?
The goal of Condition-Based Maintenance (CBM) is to monitor the actual condition of equipment. Its backbone lies in checking a machine for very specific indicators. The main benefit is that analysis is performed while the machine is working; thus, there is no need to schedule operational disruptions.
The Benefits of Predictive Maintenance Software
According to a study by Deloitte, more than 85% of manufacturing executives surveyed expect smart factory solutions to be the main factor driving competitiveness in the years ahead.
While innovation gives companies a competitive advantage, supply chain disruptions and costly unplanned equipment maintenance works are still major concerns. This is why companies are moving towards disruptive technologies, investing in the Industrial Internet of Things, Digital Twins, and real-time monitoring solutions that enable predictive maintenance. This proactive approach to equipment maintenance management brings significant benefits.
Analytics for Enhanced Decision-Making
According to research, 70% of operators are not aware of when to replace, upgrade, or schedule equipment maintenance.
Having data on asset performance and past failures is not yet the basis for drawing accurate conclusions about future incidents. Therefore, analytics and predictive models trained on historical data equip you with powerful tools to make better decisions when planning maintenance work.
Improving Asset Performance with Predictive Analytics
Industries for which assets are critical need to have solutions that help them drive revenue growth rather than struggle with asset maintenance costs.
According to data cited by Forbes, classical preventive maintenance addresses about 20% of failures. More than 80% of machine failures, in turn, happen with no pattern and are referred to as so-called “random failures”. Predictive analytics solutions based on condition monitoring allow you to increase the detection of such failures, repair important parts, and improve asset performance.
Reducing Downtime through Real-time Insights
If we accept the hourly median of downtime cost quoted by IoT Analytics, every 60 seconds of unpredicted asset outage means losses of over $1,600.
Condition monitoring analytical solutions can reduce these costs by 30 percent and minimize machine downtime by 50 percent, says a McKinsey report. A SoftwareAG report gives even more optimistic figures for the Cumulocity IoT platform: a 50 percent reduction in outage costs.
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Examples of the Best Predictive Maintenance Software
- SAP Asset Performance
An advanced predictive analytics platform integrated with SAP’s enterprise applications (SAP ERP). The system enables real-time monitoring with Machine Learning algorithms.
- Cumulocity IoT
The Cumulocity IoT platform provides all the capabilities you need for building condition-based maintenance solutions and integrates with 3rd party solutions for Machine Learning model training capabilities. The platform has been acclaimed as a Leader in the 2024 Gartner Magic Quadrant for Global Industrial IoT Platforms. The provider reports that the estimated time to implement the Proof of Concept based on their IoT platform is less than 1.5 months.
- Azure IoT
Azure IoT is a cloud-based service by Microsoft. The platform offers robust capabilities for predictive maintenance by leveraging the Internet of Things alongside advanced analytics, Machine Learning, and AI solutions. Azure IoT has been acclaimed as a Leader in the 2024 Gartner Magic Quadrant for Global Industrial IoT Platforms.
- PTC ThingWorx
PTC ThingWorx is a recognized platform that integrates IoT, AR, and AI to enhance equipment maintenance processes. PTC’s solution allows for quick application development and AR-IoT integration.
- Siemens Insights Hub
An IoT system with advanced predictive maintenance capabilities. The software allows real-time data processing, analytics, and integration with Siemens PLM software solutions.
Implementing an Effective Predictive Maintenance Strategy
When it comes to ensuring the smooth operation of machinery, implementing a strong predictive maintenance program is crucial. Follow these steps to gain optimal results.
- Leveraging Sensors and Real-time Data for Proactive Maintenance
In the proactive approach to maintenance, sensors play a crucial role in equipment monitoring. IoT sensors monitor the health and performance of equipment in real-time. They gather valuable data on aspects such as:
- temperature,
- vibration,
- pressure,
- speed,
- humidity,
- usage hours,
- fuel levels,
- air quality, etc.
In this way, machine degradation can be detected far in advance. Automated alerts allow you to gain ongoing insight into the health of your devices and undertake maintenance activities when needed.
- Choosing the Best Predictive Maintenance Software
Finding the best predictive maintenance software is not an easy undertaking, yet is a crucial part of implementing a predictive maintenance strategy.
One important factor you will need to consider is whether the software uses a CMMS (Computerized Maintenance Management Software) system. This integrated approach can streamline maintenance operations and improve efficiency.
It is also important to evaluate the features and capabilities of different options to ensure that you choose the right predictive maintenance software, aligned with the needs and goals of your organization.
- Ensuring You Have the Right Features in Predictive Maintenance Solutions
There are many solutions available on the market, and the above are only selected examples. There is no single answer to the question “Which software is the best?”, as the decision, as in many situations, is an individual matter.
Some aspects you may need to consider while searching for the best software:
- data analysis capabilities
- real-time monitoring capabilities
- predictive modeling functionalities
- intuitiveness of the interface
- compatibility with your existing systems
- costs and licensing models
- time to market
- customization possibilities
Summary
By using predictive maintenance solutions, companies not only prevent equipment failures but also reduce downtime and maintenance costs by 30-50%.
The available software tools allow you to increase productivity thanks to advanced analytics supported with AI and ML algorithms. You can wisely plan maintenance schedules to ensure that your maintenance teams are equipped with reliable monitoring tools. Predictive maintenance enables better decision-making, which is now a must-have in industries where the cost of each hour of downtime goes into the hundreds of thousands of dollars.