The maintenance of tools and equipment is a massive global industry. In 2021, companies and organizations spent $626 billion on global maintenance, repair and operations (MRO), and that market is expected to grow to nearly $750 billion over the next five years.
Although the MRO sector may not make headlines as often as the click-bait industries like aerospace, automotive, and green energy, every industry on the planet functionally relies on MRO to sustain itself. With the advent of IoT predictive maintenance and machine learning, the MRO industry is experiencing a renaissance of technology and never-before-seen operational efficiencies.
The benefits of predictive maintenance are many, and the world's most visible industries already rely on it to maintain systems and profits.
What Is Predictive Maintenance?
Predictive maintenance is a methodology that utilizes technology to collect data points about historic usage and models. That data can indicate when a maintenance event will likely occur.
An analogy between maintenance and weather forecasting is commonly used to show the distinction between maintenance schemes. Traditional maintenance is akin to buying an umbrella every day that it rains. Preventative maintenance uses historical data to understand if it normally rains, so you only buy an umbrella on days it has historically rained.
Predictive maintenance employs machine learning to gather applicable data over time. The machine learning AI models predict when it will rain so that you already have your umbrella ready.
Predictive Maintenance for Buildings
Maintenance of indoor spaces is a critical aspect of our everyday lives. For example, global health and safety standards dictate that human-occupied spaces require specific amounts of fresh, oxygenated air during periods of occupation. In places where outside temperatures can be extreme, HVAC-R systems may be essential to maintaining comfortable occupational conditions. When HVAC-R systems require unexpected maintenance, there can be serious impacts to occupational and operational conditions that negatively impact the building’s occupants.
IoT predictive maintenance solutions are commonly deployed in HVAC-R and similar building infrastructure applications. These solutions often contain multiple sensors and are deployed in droves throughout a facility, allowing building engineers to detect and predict maintenance scenarios and cut costs directly and indirectly from their operations.
To learn more about how Arrow’s partners are using predictive maintenance to cut costs and increase reliability by creating ‘smart’ buildings, be sure to check out this article.
Predictive Maintenance in Manufacturing
In 2019, the last full year before the pandemic, manufacturing in the United States produced an output of $2.3 trillion. Within the global economy, manufacturing is one of the most essential pillars to our modern way of life. Manufacturing and industrial IoT predictive maintenance technologies are essential because any improvement to the efficiency and reliability of a manufacturing process directly contributes to the global economy.
Given the high asset costs of manufacturing equipment—and the extensive cost and lead times associated with maintaining such equipment—the long-term reliability and minimization of asset failure is essential to both a company's productivity and the global supply chain.
For example, Haas, a globally respected manufacturing equipment supplier, has implemented IoT predictive maintenance technology into their CNC machines. If a Haas CNC was responsible for manufacturing mission-critical satellite hardware and did not utilize predictive maintenance technology, unexpected downtime could halt the production schedule of that satellite, potentially costing the satellite manufacturer millions of dollars.
Predictive maintenance in the energy industry, which can be considered a subset of the manufacturing industry, can save energy producers billions of dollars per year, directly influencing the global cost per unit of fuel and electricity. For example, Schlumberger announced a predictive maintenance product in 2016 that had the potential to save the company $30 million over three years in direct maintenance costs alone.
The Future of Predictive Maintenance
In its current infancy, predictive maintenance methodologies already span many global industries. As predictive maintenance technologies grow in diversity and decrease in capital expense, more industries will implement their methodologies.
It's likely that predictive maintenance in the automotive industry will not be restricted to automotive manufacturing but instead may be deployed to automobiles themselves. Instead of scheduling oil changes every 5,000 miles in your automobile, you could be notified when your car thinks it may need servicing or other maintenance based off of a variety of sensor and performance data.
Likewise, elevators will notify building managers that their pump or cable systems will likely need maintenance, and downtime will be scheduled outside of operating hours.
Unfortunately, predictive maintenance technologies are still cost prohibitive for smaller industries, and efficiencies in those industries have yet to be realized. However, as edge AI continues to become cheaper, more intelligent, and easier to train, these smaller industries will likely see a renaissance of their own in the very near future.