To improve the safety and dependability of increasingly electrified automotive vehicles, automakers are turning to predictive and preventive maintenance solutions such as highly advanced vehicle diagnostic analytics tools that are available in the market.
In auto repair and maintenance, on-board vehicle diagnostics tools are frequently utilized for self-diagnosis and reporting of any faults that may arise or have already occurred within the system. This allows for the monitoring of data regarding pollutants, mileage problems, engine temperature, vehicle and engine speed, fluid levels, gear shifts, battery status, etc.
Regardless of whether a car is propelled by an internal combustion engine or an electric motor, the number of chips inside the car has constantly been increasing as automakers substitute or augment mechanical parts with electronics. This includes more batteries with advanced power management, wireless and networking circuitry, electronic control units (ECUs), as well as many additional components for safety and diagnostics. This makes the maintenance process of automotive vehicles more crucial and complicated for consumers as well as manufacturers.
Therefore, to predict when a vehicle requires maintenance, onboard vehicle diagnostic analytics and predictive maintenance approaches intend to assist in assessing the status of a car's operational equipment. To forecast when maintenance operations will be necessary, this strategy applies the statistical process control concepts. A few prediction procedures are carried out while the equipment is in use, minimizing the impact on regular system operations.
Moreover, the growing demand for vehicle diagnostic analytics in fleet management, rapid adoption of connected, autonomous, shared, and electric (CASE) strategies by original equipment manufacturers (OEMs) in the automotive industry, increasing focus on predictive maintenance for cost-savings and enhanced safety, and growing application of vehicle diagnostic analytics by OEMs/automotive dealers in warranty analytics are a few crucial factors that are leading to significant growth in the vehicle diagnostic analytics market.
According to the BIS Research report, the vehicle diagnostic analytics market is projected to reach $4,425.6 million by 2031 from $1,827.4 million in 2021, growing at a CAGR of 9.22% during the forecast period 2022-2031.
In this article, we will further dive into the case study analysis of how vehicle diagnostic analysis is becoming more crucial in the automotive industry to increase the safety and reliability of vehicles on different ends.
Four Case Studies of the Critical Applications of Vehicle Diagnostic Analytics
1. Engine Failure Prediction Background: One of the biggest issues for automotive sector suppliers of vehicle engines is engine failure. Through alliances and collaborations, the engine suppliers have been working on ways to improve their engine diagnostics solutions to lower the rate of engine failure and hasten vehicle maintenance and repairs. The engine failure inspection procedure is laborious and time-consuming. In order to improve the diagnostic tools used by engine providers, it is also necessary to send engine failure prediction updates in real time.
Solution: The diagnostic analytics solutions for automobiles can now forecast engine failures with an accuracy of over 80%, some even reaching more than 90%, due to developments in vehicle diagnostic analytics technology. With the help of these technologies, automotive OEMs and component suppliers may successfully spot suspicious signals and aberrant areas in engine diagnostic data, allowing service professionals to discover and catch more engine issues while spending less time looking into them.
2. Reduction in Warranty Claims: Customers are becoming more demanding because of the wide range of options available across all car classes today. Due to this, automobile OEMs have been concentrating on the cost of after-sale warranty claims and build quality. A sizeable portion of the annual income of the biggest car OEMs in the world is spent on warranty claims.
OEMs have been attempting to address this by raising the caliber of the cars they sell, which benefits both brand perception and the company's bank sheet by lowering warranty costs. For OEMs, flaws in their design or production methods are the main cause of quality problems with their automobiles.
When an OEM introduces a new car model, it is essential for the OEM to put countermeasures in place that can reduce the number of problematic vehicles they sell. Therefore, the introduction of countermeasures to deal with subpar standard quality cars directly affects the reduction in warranty claims.
Solution: OEMs are increasingly relying on car diagnostic analytics tools to decide what preventative actions to take and how to put them into practice in order to lower warranty claims. These analytical solutions classify quality issues quickly and precisely using artificial intelligence/machine learning (AI/ML) approaches. The prioritization of quality issues and administration of implemented quality measures are then handled using advanced analytical techniques.
3. Usage-Based Insurance of Vehicles: The global automotive industry has embraced telematics quickly, which has increased competition in the automotive insurance sector. The emergence of AI/ML analytics solutions has had a tremendous impact on the automotive insurance business, allowing insurers to offer consumers individualized and flexible vehicle insurance plans.
Vehicle insurers are investing heavily in connected vehicle solutions to provide straightforward and adaptable insurance products and pricing schemes to address significant challenges, such as remaining competitive in the auto insurance industry and keeping up with the expanding digital customer base.
In present times, automotive insurance providers require vehicle analytics solutions for ingestion, transformation, enrichment, analysis, and storage of vehicle telematics data in real time to develop their own end-to-end analytics platform that can be used to offer dynamic usage-based insurance plans to their customers.
Solution: Analytics platforms or solutions that can be utilized for the real-time ingestion of sensor and telematics data, as well as the calculation of usage-based car insurance, can be developed using vehicle diagnostic analytics. These systems allow motor insurers to provide their clients with flexible and tailored insurance policies and price options. Additionally, they can assist insurers in forecasting client needs for vehicle breakdown and repairs, as well as providing predictive maintenance services.
4. Predictive Maintenance in Connected Vehicles: OEMs all around the world have been making significant investments in vehicle electrification and related technologies to meet pollution limits. Automobile OEMs foresee the need to automate vehicle analytics in order to keep up with operational cost and scalability difficulties as the number of commercial cars increases and to identify potential breakdowns before they cause inconvenience for the end user. OEMs require vehicle diagnostic analytics tools that will enable them to instantly assess the operational performance of critical vehicle components.
Solution: OEMs have been collaborating closely with important players in the car diagnostic analytics sector, such as cloud service providers and vehicle analytics solutions. With the help of technological developments in vehicle analytics solutions, OEMs can create their own telematics platforms that provide a thorough technical understanding of the operation of specific vehicle components in real-time, either internally or in collaboration with external stakeholders.
Some of the well-known market participants in the global vehicle diagnostic analytics market already provide sophisticated analytics-based telematics platforms that may give end users (automotive OEMs) insightful vehicle diagnostic data. With the aid of these insights, they may be able to create models for predictive maintenance and implement immediate preventative measures for their line of vehicles and the parts that make them.
Conclusion
Vehicle diagnostic analytics solutions are witnessing rapid adoption across the automotive industry. With the rapid technological advancements within vehicle diagnostic analytics, these solutions are expected to become more sophisticated.
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