Leveraging AI-Driven Predictive Analytics to Elevate the Automotive Industry

The automotive industry’s success has always been predicated on passion and innovation. From the design and implementation of patented assembly line production to Detroit’s muscle car horsepower ‘arms race,’ the automobile has always been interwoven within the fabric of daily life. As our needs and demands have changed, the industry worked to evolve its designs and features.

Today’s automobiles feature software-based resources created to enhance the driving experience, respond to green environmental initiatives, and facilitate safety and convenience. Competition amongst marquee brands remains fierce, and the industry continues collaborating with software experts to elevate existing tech features. However, what new advantages could the industry gain if Artificial Intelligence (AI) and predictive modeling were fully optimized and deployed?

Predictive Modeling’s Versatility

The predictive maintenance market is forecasted to grow an impressive 28%, and software specialists play a crucial role because the technology requires a successful culmination of data science, detailed analysis, and Machine Learning (ML) to develop reports predicting future outcomes. Predictive modeling's value lies in its versatility. Critical vehicle components can be monitored for performance when integrated within a vehicle's onboard computer system, but software developers have created a more intuitive system.

Data captured by sensors enable identifying, monitoring, and communicating to vehicle owners via connected devices or in-dash alerts about which vehicle components are at-risk for premature wear or failure. It would be dismissive to believe this feature is equal to an on-dash check engine light because predictive modeling utilizes the complexities of ML to enable more detailed reports with the ability to predict vehicle component failure. If shop manuals state recommended maintenance intervals, then predictive modeling takes the recommendations and deploys them in real-time, ultimately giving owners and manufacturers greater insight into the condition and performance of vehicle parts.

Predictive modeling is not exclusive to end-users; or vehicle owners. This resource can be invaluable to the automotive manufacturing process. The emergence of Digital Twin Technology (DT) further modernizes the industry. Created and implemented by expert-level software developers, this resource is a digital-based model of a physical machine or asset. This is intended to simulate a vehicle’s or manufacturing asset’s real-time stress and operation during routine tasks and functions. Industry personnel can monitor their machinery's performance and make proactive adjustments, ensuring the vehicle manufacturing process functions optimally. What makes DT a compelling marvel is the model mimics the aging of the machinery asset or vehicle component it monitors. The predictive analysis component is vital in learning how the actual vehicle or manufacturing asset performs over time and during specified conditions.

Mitigating Repair and Recall Expenses

The National Highway Traffic Safety Administration reported over 13 million vehicles globally were recently recalled due to safety issues. Recalls can result in serious financial consequences for manufacturers, with one major U.S. manufacturer ordered to pay $900 million after reaching an agreement with the Justice Department regarding faulty ignition switches resulting in 124 deaths and 275 injuries. Officials, advocates, and consumers were left to question if preventative measures were available. Predictive modeling resources enable manufacturers to gain a unique insight into various vehicle component construction performances. Once fully optimized by software experts, it may become a new industry standard for more effective safety compliance.

Technology’s scope has broadened beyond innovation as it continues to be utilized to improve safety. Within the automotive industry, software-driven resources are crucial to optimizing manufacturing processes as industry officials look for new methods to bring vehicles to market quicker by reducing design and construction costs and delays.

Adhering to safety compliance standards also poses challenges due to continued legislative efforts to reevaluate vehicle safety. Growing environmental advocacy has led the auto industry to redirect efforts to meet new emissions regulations. Technology provides crucial resources to respond to these continued demands as well as mitigating costly recalls.

Digital Twin technology is utilized to simulate vehicle accident scenarios. Auto industry researchers create various simulations using software-enhanced models to assess virtual damages a vehicle may suffer under various road conditions. Researchers can create and run several tests as a cost-saving measure because traditional accident testing with real vehicles and tech-infused crash dummies has become a significant expense for the industry.

Modernizing Motor City

The auto industry is reliant on expert-level software designers because vehicles continue to be technology-driven. Today’s drivers are more demanding than ever because vehicle performance is no longer the sole focal point. Drivers seek ‘smarter vehicles’, vehicles that have full connectivity that enable modern safety features, improved economy due to rising fuel costs, and longevity. The evolution of the automobile was always reliant on risk and innovation and today’s modern vehicles are reliant on software-driven features to make them beyond roadworthy.

The automotive industry remains in a unique position in the post-pandemic world. The U.S. Department of Labor Consumer Price Index Report stated American consumers are paying 20% more for vehicles compared to 2019 data. Additionally, with Edmunds forecasting the sales of new vehicles to reach 14.8 million, the industry remains in marked contrast from other sectors still working to financially rebound. Predictive modeling and Artificial Intelligence can further assist the auto industry maintain this profit windfall as predictive analytics is used to monitor production costs and product longevity.

The adoption of optimized predictive modeling and analysis requires skilled software developers because IoT-based sensors enable full capabilities. The sensors would need to be seamlessly integrated with existing manufacturing assets or onboard vehicle computers. We see Artificial Intelligence and predictive modeling successfully implemented in other industries and within automotive, these cutting-edge resources enable unprecedented versatility for an industry that remains vital to our daily lives.