Predictive Maintenance: Ensuring Business Continuity with AI

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Predictive analytics and artificial intelligence in maintenance can help businesses make sense of the swathes of data available to them and, more importantly, use it to make strategic decisions.

As technology evolves, the old saying “Why fix something that isn’t broken” is no longer valid.

In today’s world of “always on” production, where factories and production equipment run 24/7, any failure results in significant disruption to production that sometimes even creates a cascading effect to other downstream businesses. To ensure operational reliability, performing adequate maintenance is key. Businesses already know that — so it’s not a question of why, but a question of when. (Also read: How Digital Transformation Can Bring Resilience During Disruptions.)

As organizations and operators embrace internet of things (IoT) technologies, including various kinds of robots, cameras and sensors, the amount of data they collect will only continue to grow.

In fact, the amount of devices worldwide that are connected to each other to collect and analyze data and autonomously perform tasks is forecasted to nearly triple from 9.7 billion in 2020 to over 29.4 billion by 2030.

Such an explosive amount of data presents a challenge for humans, as our brains cannot parse the right information and process it in a timely fashion. While data provides businesses with an unprecedented amount of insight into their operations, not being able to make sense of it and act accordingly renders the advantage obsolete.

This is where the use of predictive analytics and artificial intelligence (AI) in maintenance comes in.


What are Predictive Analytics?

Predictive analytics allows users to predict future trends and events with probabilities determined through historical data collected.

It forecasts potential scenarios and determines the probabilities of each one, helping drive strategic decision-making. These predictions could be for the near future — such as predicting the failure of a piece of machinery later in the day — or further out in the future, like forecasting the budget required for maintenance operations for the year. Forecasting empowers businesses to make better decisions and formulate data-informed strategies.

Using AI for Predictive Maintenance

One of AI's most valuable features is its ability to digest information from multiple sources at once, calculate the probability of various possible outcomes and make recommendations based on a variety of reasons — all without the need for human input. Such an ability enables predictive analytics to take advantage of the wealth of data available in many modern enterprises. (Also read: The Top Ways AI Is Improving Business Productivity.)

As the world churns out more and more data — be it from the thousands of IoT sensors, from shipping data that shows delivery time of raw materials and parts or from open-source weather data collected at weather stations worldwide — AI is maturing at a perfect time to help humans make sense of all the information. It can sort out the signal in a sea of noise to make actionable decisions.

With proper AI configurations, businesses with an AI-enabled, ERP-integrated operation can act on what they glean from the data.

How does this all factor into performing maintenance? Currently, there are three types of maintenance:

  1. Time-based maintenance.
  2. Reactive maintenance.
  3. Predictive maintenance.

Time-based maintenance is when the user performs the maintenance based on a schedule — usually the machine’s expected life cycle. It is fine in theory, as the user can determine maintenance needs based on other similar devices. However, it’s mostly theoretical, given that each machine functions differently depending on many factors — including usage, location, wear and tear. With a time-based approach, organizations run the risk of performing too much or not enough maintenance on the machine.

With reactive maintenance, on the other hand, maintenance is performed when needed, meaning there will be unscheduled downtime which disrupts production activities.

Predictive maintenance solves all these issues. It is a type of condition-based maintenance that monitors devices' and tools' conditions through sensors that supply data which is used to predict when the asset will require maintenance. Therefore, maintenance is only scheduled when specific conditions are met — and before the equipment starts to fail.

As AI technology matures and organizations deploy more and more IoT tools, the use of AI-enabled predictive maintenance is on the rise. (Also read: What AI Can Do for the Enterprise.)

Predictive Maintenance in Action

While virtually all businesses that operate machines requiring regular maintenance can benefit from predictive maintenance (depending on the cost of machine downtime), some see greater benefits than others.

Businesses in field services, for instance, benefit a lot from predictive maintenance due to the remote nature of their operations. With assets such as oil rigs and wind turbines located in far-flung locations susceptible to strong weather, reacting to a failed machine can significantly disrupt production.

Worse still, performing the maintenance after the fact presents significant costs, as spare parts need to be ordered and maintenance crews need to be deployed to those remote locations quickly. With predictive analytics, however, field service organizations can perform necessary maintenance on a wind turbine part before it fails to ensure consistent power generation. (Also read: The 6 Most Amazing AI Advances in Agriculture.)

Through analyzing the machine’s vibration, acoustics, and temperature, for instance, operators can discover potential problems because of issues like imbalance, misalignment, bearing wear, inadequate lubrication or airflow.

Another example is an alarm serving as a signal/fault code from a piece of equipment that has gone down. The system can analyze previous maintenance work done for that type of equipment as well as that particular signal/fault code. Based on history, the system determines the last set number of times it saw that combination (previous maintenance work and the particular signal/fault code). A technician will then be dispatched at an appropriate time ahead of any actual failure, armed with applicable spare parts recommended by the system to complete the repair. Predictive analytics allows operators to track the machine’s wear and tear and potential defects more precisely, and more importantly, allows them to act before the machine breaks down.

By using historical trends and weather patterns, combined with information from sensors on the equipment and forecasted supply chain delivery times, maintenance can be preemptively performed in advance. The crew has more control over where and when maintenance takes place, as opposed to rushing to the rescue after the fact — allowing them to pick and choose their battles.


While there is no surefire way to predict mishaps, AI can get us as close to it as we possibly can.

The same way people in coastal regions might stock up on bottled water and backup batteries once a hurricane is spotted, an AI-integrated maintenance system allows businesses to perform maintenance as needed before any issues manifest themselves as real problems. (Also read: 6 No-Code AI Platforms That Are Accessible to SMBs.)


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Kevin Miller
Chief Technology Officer
Kevin Miller
Chief Technology Officer

As the incoming Chief Technology Officer of IFS in North America, Kevin Miller will be responsible for driving the leading product and industry solutions that deliver true business value to IFS customers and partners in the United States and Canada.Kevin joined IFS in May 2021 as Associate Vice President of Pre-Sales Solutions. After a successful year and a half of leading the pre-sales team of North America in shaping and selling successful outcomes, he is taking on the role of Chief Technology Officer for the North American region. Outside of IFS, Kevin has successfully retooled and built global presales teams…