Predicting the future sits within arm’s reach with predictive maintenance (PdM). This technology reads the real-time health of your equipment and alerts you to what needs attention before it fails, making it crucial part of a wider maintenance strategy that manages reactive, preventive, predictive and prescriptive measures.
Avoiding disruptive operations is critical for a business’s reputation and potential earnings, as a WSJ and Emerson report puts the annual cost of unplanned downtime to industrial manufacturers at around $50 billion.
The technology to tackle that figure is more accessible than ever. But the harder part is finding the most skilled people to run it. This article explains what role predictive maintenance has in the manufacturing industry and how to find the talent to manage it.
What is Predictive Maintenance in the Manufacturing Industry?
Predictive maintenance is a data-led approach, using IoT and AI, that monitors the condition of equipment during normal operation to forecast failures before they happen. In other words, it’s technology that monitors for anomalies and defects. It aims to alert systems to any potential repairs that could fall within a planned window, instead of an unexpected breakdown.
Where it started and how it works today
This technology can be traced back as early as WWII, when C.H. Waddington noticed that RAF aircraft failed more often right after scheduled servicing, a pattern later named the Waddington Effect. His answer was to match maintenance to the actual condition of the equipment rather than a fixed timetable.
Industry took up the approach in the 1990s through route-based manual data collection on heavy machinery, and Industry 4.0 turned it into the sensor-driven discipline we see today.
Machine learning was part of this transformation. Sensors now capture live signals from machines, and software compares them against normal operating patterns. The moment a reading looks wrong, the system flags it.
Several monitoring techniques are layered together to feed models that turn raw data into a forecast, including:
• Vibration analysis tracks rotating equipment such as motors and pumps
• Thermal imaging catches overheating in electrical systems
• Acoustic and ultrasonic monitoring picks up leaks and early bearing faults
• Oil analysis reveals wear inside gearboxes and engines
Deloitte links predictive maintenance to between 10% and 20% higher equipment uptime, 5% to 10% lower maintenance costs, and 20% to 50% less time spent planning the work.
Steps to Implement Predictive Maintenance
There are four steps in manufacturing where predictive maintenance can be implemented to reduce maintenance cost, improve operations and increase efficiency.
1. Prioritise the assets that matter most
Map your equipment against the cost of its failure, downtime or missed shipments, then rank the most expensive at the top. This identifies any operational bottlenecks and reduces the risk of safety issues, workflow disruption and financial loss.
2. Install IoT sensors and edge devices
Match the sensor to the equipment and failure mode, for instance, vibration and temperature probes for rotating equipment. Installing sensors in the correct place also impacts the data collection. Edge devices process readings on the machine itself, so alerts arrive in seconds rather than a round trip to the cloud.
3. Build and train the machine learning models
Historical and real-time data teach the models what trouble looks like on the manufacturing floor, and the predictions improve as more data becomes available. This is where the analysis happens, the stage that turns signals into trends and early warnings.
4. Integrate predictions into the maintenance workflow
A prediction is only useful if someone acts on it. Connect the system to your maintenance software so every alert schedules a repair or orders the parts automatically.
These four steps are essential for the technology to perform. Yet PdM is only ever as strong as the people who are qualified and experienced enough to build it and act on the data it returns.
How to Build the Team Behind the Technology
Data from Deloitte and the Manufacturing Institute project that manufacturing could need as many as 3.8 million new workers between 2024 and 2033, with 1.9 million of those roles at risk of going unfilled. Predictive maintenance sits at the sharp end of that shortage.
It calls for data scientists and machine learning engineers to build the models, controls and automation engineers to integrate them with live production lines, IoT systems engineers to run the sensor networks, and reliability engineers who interpret the data and what it means for a physical asset. These roles sit beyond the reach of most generalist recruiters.
Line Up Your Industry 4.0 Talent Early
Alexander Daniels Global is built to work as a partner to Industry 4.0 employers, mapping niche, candidate-scarce markets and placing the talent who turn a predictive maintenance investment into results. We also support individuals seeking their next Industry 4.0 role by offering exclusive global opportunities and personalized guidance.
Download our Industrial Automation Salary Guide for current benchmarks, or talk to the team about the roles you need to fill.
