But let's back up a bit before we get into how predictive maintenance can help you avoid costly, time-consuming and potentially fatal breakdowns in production.
What is predictive maintenance?
Predictive maintenance is an approach that uses data from sensors and other sources to predict when machines or components are at risk of breaking down. Unlike reactive maintenance, where problems are fixed after they occur, and preventive maintenance, which follows fixed schedules, predictive maintenance is based on acting on real data and current needs.
According to McKinsey & Company, predictive maintenance can reduce maintenance costs by 30-50%, while reducing unplanned breakdowns by up to 75% and increasing productivity by 20-25%. This shows that the technology is not only an investment in operational safety but also in economic profitability.
Telling examples from the real world
Predictive maintenance has already shown its value in several sectors:
- Energy industry: A wind power company used predictive maintenance to monitor rotor blades and bearings. This resulted in a 25% increase in turbine availability and a saving of €1.5 million per year.
- Manufacturing industry: By monitoring production equipment with IoT sensors, an automotive component manufacturer reduced breakdowns by 40% and improved delivery accuracy by 15%.
- Pharmaceutical industry: A pharmaceutical company used sensor data to predict failures in filtration systems. The result was a 30% increase in uptime and avoided production losses worth millions of euros.
"Data helps us see what was previously invisible. With the right analytical tools, we can identify and fix problems before they become critical," explains Per-Åke Södergren.
Taking your business to the next level - Combining technology & training
To succeed in predictive maintenance, several key components are needed:
- Sensors and IoT technologies: These devices collect data on the state of the equipment, such as vibration, temperature and pressure.
- Data analysis and AI: Machine learning and advanced algorithms analyze collected data and identify patterns that indicate potential problems.
- Integrated systems: Connecting data from different sources to create a holistic picture requires integrated IT solutions.
- Skills development: Companies need to train staff to interpret data and use predictive tools effectively.
"It is crucial to combine technology with training. Predictive maintenance is as much a human issue as a technical one," says Per-Åke.
Recognize common pitfalls - and avoid them
Despite its benefits, there are challenges to overcome when implementing predictive maintenance:
- Lack of strategy: Without a clear plan, technology risks being underutilized.
- Poor data quality: Inaccurate or insufficient data can lead to incorrect conclusions.
- Resistance to change: Getting the whole organization to embrace new ways of working is often one of the biggest challenges.
An investment in the future
Predictive maintenance is more than a technological innovation - it is a strategic investment that enables companies to minimize risk, save money and increase competitiveness. With advanced technology and an understanding of the potential of data, industry can secure a future where breakdowns become the exception rather than the rule. As Per-Åke Södergren summarizes:
"Predictive maintenance is not just about preventing breakdowns. It's about creating production that is safe, efficient and ready to meet the demands of the future."
Can you afford a breakdown?
How much does a breakdown in your production cost, not only in direct costs but also in lost confidence and productivity? And how much time do you have to act before it's too late? The tools are there to secure your operations, avoid breakdowns and save time and money.
Investing in predictive maintenance is clearly a no-brainer.
Don't miss the podcast episode where Per-Åke Södergren shares over 20 years of experience from preventive maintenance in the process industry. Per-Åke dives into how saved historical equipment and process data can be used to avoid machine failures and breakdowns and what economic benefits data-driven analysis can contribute to. How does it work in practice when data is turned into insights and forms the basis for actual maintenance efforts? Find out about this and much more in this section.
