How Will Prediction Models Impact the Future of Vehicle Maintenance?

predictive maintenance market

Equipped with predictive maintenance solutions, fleet owners and OEMs will enjoy reduced maintenance prices, improved sustainability, and greater overall reliability.

Leading a healthy lifestyle and getting regular checkups is essential to preventing major health complications in our bodies. The same is true for vehicles.

Nevertheless, unexpected malfunctions often cause even more expensive on-the-road repairs and costly downtime. We see our vehicles in the same light, but are we wasting money trying to figure out what’s wrong with our vehicles? The standard vehicle inspection and preventative maintenance practices are expensive and time-consuming. As a result, fleets, OEMs, and Tier-1 suppliers are looking at new opportunities to reduce costs. Amongst them is the added value of predictive maintenance.

Maintenance costs rose between 3% and 5% in 2021 due to constrained new-vehicle inventory caused by the global microchip shortage, which has led to vehicles remaining in service for longer than initially anticipated. In addition, the current inflation crisis and the war in Ukraine will likely exacerbate these constraints. Other factors that impact the cost include the rising price of labor, manufacturing, transportation, and synthetic oil. All of which are now required by most OEMs.

Then there are increases in the costs for commodities – crude oil and rubber among them – which are up across the board. Repair facilities are also struggling to keep parts in stock and employees on staff. So as we advance, fleet operators should expect a 5% to 10% increase in operating costs for this year, the next, and potentially beyond.

Fleets that wish to drive down their total cost of ownership (TCO) and OEMs who strive to improve the quality of their vehicles must progress from traditional preventative and reactive maintenance routines.

The key is predicting when it’s time for vehicle maintenance and avoiding unexpected failures.

See also: Predictive Maintenance: The Continuous Intelligence Killer App

What is predictive maintenance?

Let’s return to the doctor’s office. Just as an experienced physician can examine a patient and use their symptoms to predict problems that may arise if left untreated, vehicles also need a prognosis to prevent issues from degenerating and even affecting the productivity of an enterprise due to the unexpected downtime of an asset.

A solution alerting fleet owners that a problem may develop in the near future will enable them to proactively bring in their vehicles for repair, when needed, as needed, and on a convenient schedule. In addition, alerts can prompt fleet managers, for example, to conduct engine tune-ups, replace defective fuel injectors, or repair a leak in the engine cooling system before more significant problems arise.

These developments present a vital transition from traditional maintenance (which is more reactive and periodic) to predictive maintenance solutions, which leverage technologies such as artificial intelligence (AI) and connectivity to identify necessary actions before costly, lengthy breakdowns occur.

Data is key

Knowledge is power, and the automotive sector is no exception.

In order to predict when vehicles will need maintenance, we must fully understand vehicle health. How do you understand vehicle health? Fleet owners, Tier 1s, and OEMs should take advantage of the mass data available on-board the vehicle and utilize modern AI-based analytics technology to generate insights from the data.

AI technology, and specifically deep learning, has matured significantly over the past few years and, together with powerful computing technology deployed on-board modern vehicles, creates a huge opportunity for major advancement in predictive maintenance.

While there is still much work to be done separating the wheat from the chaff, a growing amount of quality, health-relevant data is being collected and processed, and valuable insights can be delivered to users across the globe. Advanced AI and deep learning become key to making the most out of the growing amounts of data. These technologies, coupled with the knowledge and prowess of experienced players in the automotive industry, can determine which of a vehicle’s parts or processes may require maintenance with great accuracy.

Take tires, for example. The U.S. Department of Energy (DOE) states that every 1 psi (pounds per square inch) missing from a set of four tires correlates to a 0.1% decrease in gas mileage and 10% faster wear and tear. By contrast, properly inflated tires can improve gas mileage by 3.3%. This contrast means cars getting 30 mpg could simply see a 1 mpg benefit with proactive tire maintenance. Moreover, if a fleet manager could predetermine which tires have a high risk of imminent deflation, fuel economy throughout the fleet would vastly improve.

Predictive Maintenance Doesn’t Stop at Tires

Repairing other underperforming parts also results in increased fuel efficiency. For example, the DOE points out that repairing a car with an out-of-tune engine or one that fails an emissions test can improve the vehicle’s performance by up to 4%. In cases where a severe engine problem is repaired, fuel efficiency may jump by up to 40%.

Additionally, predictive warnings can help avoid potentially costly malfunctions, such as engine overheating from a faulty cooling system and associated downtime.

Driving down downtime

Downtime seems to be one of the biggest enemies of commercial fleets today. Unexpected repairs are one of the main contributors to vehicle downtime. Research shows that roadside truck breakdowns occur as often as every 10,000 miles.

It gets worse.

Truck downtime can cost transportation companies over $700 per vehicle per day. A total of 63 billion USD is spent on fuel costs, labor costs, wear, and in-traffic wear and tear, making a massive dent in the industry. The solution of predictive maintenance is essential to curbing those losses.

The Vehicular Future knows the Future

Predictive maintenance has a hidden benefit for automotive manufacturers. When problems are identified early, OEMs and Tier 1s are able to perform software updates over the air to avert the crisis and even modify their manufacturing methods and improve the build quality, performance of future vehicles, and their components. The benefit is clear. Equipped with predictive maintenance solutions, fleet owners, Tier 1s, and OEMs will enjoy reduced maintenance prices, improved sustainability, and greater overall reliability.

Erez Lorber

About Erez Lorber

Erez Lorber is CEO of Questar. He is a highly accomplished executive with over 20 years of domestic and international management experience in strategic planning, operations management, technology and business development and sales with both private and public organizations. Previously COO of StoreDot, developer of ultra-fast charged batteries for EVs; CEO of Tri-Logical (Acquired), mobile assets management solutions; CEO of BackWeb Technologies (NASDAQ:BWEB) and CEO of Deloitte Israel..

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