Guide to predictive analytics

Keeping equipment in prime condition is essential for heavy industries, but it can often be challenging. Some machines may not show outward signs of wear before a breakdown and stopping for repairs takes time. Predictive analytics can help.

Applying predictive analytics to equipment maintenance is a relatively new practice, but it’s already shown impressive results. Here’s a closer look at the role this data-based field plays in maintaining heavy machinery.

What is predictive analytics?

Predictive analytics aims to assess what will happen in the future based on previous and current experience. Machine learning algorithms analyse trends from the past to learn what causes various scenarios. They can then use that information to analyse existing data and predict if and when one of these scenarios will occur in the future.

Applying this practice to repairs is called predictive maintenance. It starts by installing sensors in machines to monitor factors such as their temperature, vibrations, airflow, stress and more. These sensors then feed this data into predictive models that can use it to determine when a machine will need attention.

Machine learning algorithms are typically much better than humans at connecting data points to see similarities and anomalies. As a result, predictive maintenance can detect potential equipment issues before a human would.

Predictive analytics presents several advantages over traditional maintenance approaches. Here are a few of the leading benefits.

Preventing machinery breakdowns

All maintenance should aim to prevent unexpected equipment breakdowns. Even small issues will eventually get to a point where the machine can no longer function properly without regular upkeep. It typically requires costly repairs to get everything back in working order when that happens.

Repairs are a crucial part of total ownership costs, so these breakdowns can drastically increase a company’s operating expenses. Predictive maintenance helps reduce those costs by preventing these problems from happening at all.

Predictive analytics can determine which issues are more likely to cause substantial problems in the long run by analysing past data. It can then alert workers when these issues arise, directing them to solve them before they worsen. Performing maintenance early reduces repair costs and prevents more expensive problems.

Reducing maintenance downtime

Companies can prevent breakdowns without predictive analytics, too. Preventive maintenance, which involves regular, schedule-based checks and repairs, can also solve issues before they become larger, more expensive problems. However, it also involves a considerable amount of downtime.

Regularly scheduled upkeep prevents breakdowns, but machine maintenance needs are rarely regular. Following a set, unchanging schedule inevitably means equipment receives repairs and downtime it doesn’t need. Predictive maintenance aims to remove this inefficiency.

Workers only stop to repair equipment when the predictive system alerts them to. That way, they only perform fixes that a machine actually needs. The resulting savings can be substantial, considering construction equipment downtime costs US$25 an hour in a best-case scenario.

Predictive analytics can reduce maintenance costs by up to 40% by eliminating unnecessary repairs. This is a considerable improvement over traditional preventive and reactive maintenance.

Improving operator productivity

This reduction in downtime also plays a role in improving employee productivity. Workers will spend less time maintaining machines, so they’ll have more to focus on other work. Those savings compound with the extra time they’ll have from avoiding breakdowns.

Unplanned downtime rates of 20%-30% are common throughout the construction industry. One downed machine will stop at least one employee from accomplishing their work and could cause the entire team to pause. As a result, construction workers frequently lose dozens, if not hundreds, of hours a year.

Predictive maintenance eliminates unplanned downtime and reduces planned stoppages, so it vastly reduces those lost hours. Some teams may get the equivalent of several extra work weeks a year. Workers can also maintain peak efficiency for longer stretches with fewer disruptions interrupting their productivity.

Predictive analytics could revolutionise maintenance

Predictive analytics in maintenance typically comes with high upfront costs, but the savings are remarkable. The construction industry could become far more productive and profitable as these practices gain mainstream adoption.

Maintenance is often one of the highest ongoing costs for companies in heavy industries. Predictive analytics could change that. That could bring considerable benefits in an industry where flexibility and agility are rare.

Evelyn Long is a writer, editor and the co-founder of Renovated, a web magazine covering property and construction trends.