Over the years, technology has shaped the automotive industry in unimaginable ways. It has allowed auto manufacturers to work more efficiently and has given vehicle owners access to sustainable innovations, new designs, and enhanced safety features. 

But crucially for both parties, it’s changed how we view maintenance. While traditional practices were mostly always reactive—taking place after an issue has surfaced—modern methods use a combination of cutting-edge technology to predict the future and nip issues in the bud before they turn expensive.

Automotive predictive maintenance has the potential to lower costs, increase vehicle lifespans, and reduce downtime. Let’s explore this in further detail.

Why Predictive Maintenance is the Next Big Thing 

A recent survey found that one in three car owners in the US cannot afford an unexpected auto repair bill without taking out credit. At the same time, nearly two-thirds of fleet owners say high insurance premiums, expensive fuel, and rising fleet maintenance costs are the industry’s biggest threats. 

In this context, predictive maintenance emerges as a crucial lifeline. It helps bring down costs by preventing potential failures and allows rental companies, OEMs, insurers, fleet managers, and others to be more efficient and offer better services.

It’s no wonder that the automotive predictive maintenance market is expected to be worth $100 billion by 2032, a staggering rise compared to 2022 figures. Much of this growth boils down to three major factors: 

• Increased complexity: Today’s vehicles are more complicated than ever. Identifying problems in advanced machines requires modern solutions.
• Increased costs: Fleet maintenance costs, which are at an all-time high due to complex vehicles, labor costs, and expensive materials, must be brought down. 
• Increased data: The rise of IoT-enabled sensors has given manufacturers and owners access to vast amounts of data, which enables predictive maintenance. 

How Predictive Maintenance Works

The first step of any predictive maintenance strategy involves collecting data related to a vehicle’s performance. This can include information about engine performance, tire pressure, various electrical components, and more. The information is collected by various IoT-enabled devices, maintenance logs, onboard cameras, and more. It is then scrutinized in real-time with the help of AI algorithms to find anomalies.

This process aims to minimize maintenance frequency, optimize schedules, and eliminate unforeseen downtime. 

Key Technology Driving Predictive Maintenance

Cutting-edge technology is crucial for predictive maintenance to work. Here’s a breakdown of some key innovations that drive the practice.

The Internet of Things and Telematics

Modern cars can be fitted with any number of IoT-enabled devices that track and collect data. From sensors that map fluid levels to on-board computer vision cameras that provide real-time updates on a vehicle’s condition, manufacturers and service providers have access to a rich repository of accurate information to predict potential failures.

Big Data Analytics

Once this massive volume of data is collected, it must be broken down to understand a machine’s behavior. Leveraging advanced machine learning algorithms, OEMs can predict equipment failures with incredible accuracy. Manufacturers can also analyze historical data to create schedules and alert the driver when it’s time for a service.

Business Intelligence

Business intelligence is the process of turning data into actionable steps that manufacturers, providers, and fleet owners can take to streamline maintenance practices. This process can be further streamlined with auto repair software equipped with a seamless real-time dashboard and advanced reporting features.

Digital Twins

A digital twin is a highly detailed visual model of a real object or system. Using data from various sensors, it mirrors the object in real time. In this context, an automotive digital twin can monitor crucial aspects of a car’s functioning, such as fuel efficiency or the vehicle’s health, and craft appropriate maintenance schedules.

Automotive digital twins also provide insight into a vehicle’s performance in various climates, road conditions, and routes. This has proven especially vital for fleet managers to understand the risks to their assets and formulate timely actionable plans.

Advancements in 5G services, cloud offerings, and machine learning algorithms will also further enhance predictive maintenance. 

The Benefits of Automotive Predictive Maintenance

It’s clear that automotive predictive maintenance has immense potential. But how exactly does it benefit various players in the industry? Here’s a brief breakdown. 

• Dealerships: Predictive maintenance allows auto dealers to reduce the likelihood of making a bad sale and improve customer satisfaction.
• OEMS: Original equipment manufacturers save costs by reducing product recalls and warranty claims.  
• Fleet managers: Benefit from more durable vehicles, lower fleet maintenance costs, and remote monitoring capabilities.
• Consumers: Similarly, owners benefit from safer vehicles, streamlined maintenance schedules, and lower expenses.

Predictive Maintenance in Action: Real-Life Examples 

In the last few years, almost all major car manufacturers have embraced predictive maintenance in some form, including names like BMW, Ford, and Toyota.

Skoda’s Computer Vision

Recently, Skoda introduced Magic Eye, a computer vision technology to monitor crucial equipment in its factory. As a car moves through the production line, six cameras collect 450,000 photos, which are then analyzed by ten artificial neural networks. 

The results are stored on the Amazon Web Services cloud computing platform. If any irregularity is detected, the team is promptly informed.

Volvo’s Network of Sensors 

Volvo has also benefited from predictive maintenance. By analyzing data from sensors on board 175,000 trucks, the manufacturer has been able to predict component failures with increased accuracy. Since the system was launched, it has reduced unplanned downtime by 25% and diagnostics time by nearly 70%. 

BMW’s Digital Twins 

In 2023 BMW announced that it had documented over five million of its cars and planned to make automotive digital twins for every one it produces moving forward. These twins will exist on the cloud, be updated in real-time, and ensure a more sophisticated maintenance experience for BMW’s millions of customers.  

What’s Next for Automotive Predictive Maintenance?

Technologies like automotive digital twins, intelligent auto repair software, and computer vision have come a long way. But this isn’t the end of the road.

Automotive predictive maintenance is expected to grow into a staggering $100 billion industry in the next decade, meaning it won’t be long before we see even more advanced machine learning algorithms and IoT-enabled sensors, and digital mechanic shop organization ideas streamline operations further. In fact, at the rate predictive maintenance is growing, it won’t be long before we remove the guesswork from the equation altogether.

Steven Meyers is a writer at Business Tech Innovations, focusing on the intersection of automotive technology and repair expertise. His work helps consumers and professionals alike navigate the complexities of modern vehicles, offering practical insights and actionable advice.