How CIOs Can Utilize Predictive Analytics For The Connected Car Industry

Auto Tech Outlook | Thursday, August 06, 2020

Predictive analytics is evolving into a strong mechanism for the automotive industry to solve a vibrant array of problems.

FREMONT, CA: Predictive maintenance in the automotive industry enables to evolve of the traditional market ecosystem through different technical data analytics and statistical methodologies into an intelligent manufacturing unit. The predictive maintenance analysis provides “correct data at the right moment.” It also lays the groundwork for various innovative processes to save costs.

Networked cars are the next sizeable digital project in the automotive industry that by introducing independent driverless cars is sure to generate a technological revolution. These self-driving cars require a complicated sensor circuit where the efficient devices connected to them make the machine-to-machine communications a reality.

Stay ahead of the industry with exclusive feature stories on the top companies, expert insights and the latest news delivered straight to your inbox. Subscribe today.

Data and software have become essential fabrication components and stretch far beyond vital vehicle production. There is information from riders and travelers inside the vehicle, outside the car. Algorithms play the central part in influencing the next millennium of automobiles because of the significance of information collection, artificial intelligence (AI), data analysis, and then learning from it. As linked vehicles are progressively streaming information from telematics devices, infotainment systems, and the vast range of intelligent IoT devices into the cloud, each connected vehicle is capable of producing over 25 gigabytes per hour as more cloud-based facilities are available. The crucial element in wisely using this information deluge lies in car statistical analytics, and in specific predictive analytics.

Effective and Predictive Maintenance

The task for developers is how to gather, store, and process stunning quantities of information as the cars become faster. Automakers should use AI, information management, and electronic workflows to attain these innovative capacities while accelerating experimentation and simulation in the life cycle of product development. Predictive maintenance seeks to detect problems with car maintenance before they happen. By harnessing details from warranty maintenance using present car sensor information, predictive data analytics can find significant correlations that would be hard to discover for a person. An efficiency anomaly that may seem trivial when noted on a single vehicle could be a red flag when collated with dozens or hundreds of other cars having the same issue. Systems for predictive maintenance analytics can draw details from nearly every vehicle of a specified year and design and contrast that information with developments in-warranty repair.

This predictive maintenance performs a crucial part in preventing unplanned downtime, increasing income, and reducing the price of maintenance facilities. It offers a profound understanding of the health history of the automobile from when it had its first full service to the present situation of the car.

Geological Monitoring intelligence

Location intelligence in manufacturing allows automakers to manipulate place data from a portion of a fully produced item and then add intelligence to it. Various sub-assemblies produced by technologically advanced components help in manufacturing vehicles. Recognizing the physical place of these sub-assemblies and elements is essential at any time to educate the production chain and ensure that the correct sub-assemblies and components consistently reach the correct assembly line. Destination management allows employees to monitor event sites based on RFID and sensor technology information in real-time. Location intelligence also aims to ensure transparency and consistency in assessing and predicting issues before they become serious problems.

Predictive avoidance of collisions

 Technology provides drivers no function that is perhaps more valued than schemes for preventing imminent collisions. By using sophisticated detectors, large and rapid information, and car-to-car interconnection, predictive analytics technology can one day make car accidents outdated. By using sensors on the front of the automobile, the system is able to examine the distance and speed of the car traveling ahead of the vehicle, as well as the speed and direction of the next preceding vehicle typically outside the visual field of the rider. The mechanism notifies the driver with a verbal warning and clear message when either of the two following cars acts in a way that could cause the rider to brake unexpectedly. A signal is also sent in the event of contact to lock the safety belts momentarily. As designers generate apps that enhance communication between linked cars, more complicated and more efficient collision avoidance systems will arise depending on driver behavior prediction.

Connected automobile cybersecurity

With specialists predicting that there will be a half-billion linked cars on the highway by 2020, it is simple to see why they are worried. The reality is, connected vehicles are no less vulnerable to cyber assaults than any other internet-connected device, but the implications of a safety violation could be far more disastrous. One can only imagine the independent attraction cars posing for modern-day cybercriminals in particular.

To render their cars safe from cyber attacks, automakers must use technology that remains a step ahead of cybercriminals. Predictive analytics provides the ability to do that. Whether cybercriminals aim linked cars for the pure effort to profit or as a type of activism, their strategy is the same: discover deficiencies in channels and structures within the attached car environment and use them to obtain private data from passengers or to obtain command of automobile facilities. They always bring proof of their existence as cybercriminals "hack" into a scheme. Classical safety policies are relatively efficient in identifying an intruder's evidence when using the same methods as other intruders used to obtain access.

The potential to recognize metrics is what makes predictive analytics efficient in ensuring linked vehicles, where standard safety policies may fail. At some point, the behavior of each attacker will be different from the action of an authorized user. While being a very over-simplified explanation, the significant thing to understand is the ability of probabilistic data analytics to understand standard behavior models and spot divergences from such activities.

In a way, all linked apps in auto analytics constitute data management instances. Whether we are speaking about using predictive information to enhance servicing, advertising, safety, or other associated operations in the connected vehicle industry, the data must be handled in a manner that makes it helpful for the desired intent.

More in News

Automotive startups must implement innovative strategies, concentrate on emerging trends, and optimize their operations to succeed in a highly competitive market with significant entry barriers. Although the automotive industry poses challenges, it offers opportunities, particularly in electric vehicles (EVs) and autonomous driving technologies. Adopting lean manufacturing practices, focused on reducing waste, enhancing efficiency, and fostering continuous improvement, can greatly reduce production costs and increase profit margins. Therefore, automotive startups must embrace these lean principles to streamline their production processes. Technologies like AI, ML and IoT into production and business operations can enhance efficiency and profitability. AI and mL can optimize supply chain management, predict maintenance needs, and personalize customer experiences. IoT can enable real-time monitoring and data collection, improving operational decision-making and product development. Automotive startups can achieve higher profitability by targeting niche markets that larger, established companies underserve. It could include specialized vehicles like luxury electric cars, high-performance sports cars, or customized fleet solutions for specific industries. The practices help minimize waste, reduce inventory costs, and improve product quality, increasing customer satisfaction and profitability. Startups can differentiate themselves and command higher prices due to their unique value. Providing exceptional customer service, engaging with customers through social media, and offering loyalty programs can foster strong relationships and repeat business, contributing to sustained profitability. Diversifying revenue streams can enhance financial stability and profitability. Automotive startups can explore various alternative revenue sources, such as offering subscription-based services for software updates, maintenance packages, and telematics services. Collaborating with established automotive manufacturers, technology companies, and research institutions can accelerate product development, reduce costs, and enhance market penetration. Alliances can open doors to joint ventures, co-branding opportunities, and shared R&D efforts. Startups that adopt eco-friendly practices, such as using sustainable materials, reducing carbon footprints, and ensuring ethical supply chains, can attract environmentally conscious consumers and investors. The focus differentiates the brand and opens up the potential for grants, subsidies, and tax incentives related to green initiatives. Startups can optimize pricing strategies, marketing efforts, and production planning, leading to increased profitability. Leveraging data monetization by providing insights from vehicle data to third parties can create new income opportunities. Automotive industry startups have immense potential for profitability if they adopt innovative strategies and remain agile in a rapidly evolving market. By focusing on emerging technologies, lean manufacturing, niche markets, and sustainability while leveraging data and forming strategic partnerships, these startups can survive and thrive, achieving greater profits and establishing a solid foothold in the competitive automotive landscape. ...Read more
Mobility is a key element of both economic and personal freedom, making it essential to prioritize reliable roadside support as the number of vehicles on the road increases. An effective roadside assistance network is crucial for helping vehicles that encounter issues like breakdowns, flat tires, and dead batteries. As technology and consumer expectations evolve, these systems need to become more intelligent and interconnected, enhancing their efficiency, safety, and consistency. This shift is transforming roadside assistance into a more responsive, scalable, and transparent service. Increased Dispatch Efficiency and Real-Time Communication One of the significant advances of roadside assistance networks is better dispatch coordination. They mainly comprise call centers, paper records, and manual routing, which increased wait times and reduced service visibility. Using digital platforms, one can track and automate job assignments and streamline communication between dispatch centers, field technicians, and vehicle owners. Integrated systems apply location information to connect requests needing service with the nearest available provider, thereby slashing response times and streamlining resource deployment. Such automation enables service providers to serve larger volumes with uncompromised quality and gives customers a better view of arrival time and service status through mobile applications. This two-way communication added by these systems creates room for live updates, reducing the range of uncertainty for the ultimate experience. The dispatch system can analyze patterns in service requests and traffic conditions to forecast peak demand periods and adjust resource allocation. The operational improvements bring speedier resolutions while promoting optimized workforce utilization and reduced overhead. Data Integration and Predictive Maintenance Support Modern roadside pro-back-up solutions are becoming the core of the larger ecosystems of vehicle support. Linking such applications as telematics and vehicle diagnostics systems is a massive key towards proactive service strategies. For example, sensor-equipped vehicles connected to platforms communicate real-time information to alert of upcoming trouble before a severe failure occurs. Predictive awareness will avoid road incidents, allowing one to remind users of battery replacement, tire rotation, or any other preemptive action. From a customer perspective, technicians come to the scene informed or "armed" with all the appropriate tools or replacement parts necessary to fix the issue during their first visit, empowered by the presence of such health data concerning the vehicle. It also reduces diagnostic time at the scene since it enables better fault determination. In some cases, remote assistance may be feasible through technical support or system resets, allowing the user to avoid waiting for an actual intervention. Such features not only improve customer satisfaction but also reduce operational costs. Scalability and Customization along Multiple User Lines Roadside assistance network solutions are versatile and can work across an extensive range of users, including individual drivers, fleet operators, insurance providers, and vehicle manufacturers. Each group has specific needs and operational goals; therefore, these modern platforms support customization based on various service levels, billing models, and performance metrics. For example, roadside assistance is not merely a support service to fleet operators but an integral part of uptime management. Roadside data should then be integrated with fleet management software for continuous monitoring and speedier interventions to keep vehicles rolling and minimize downtimes. On the other hand, insurance providers would benefit from roadside assistance data to understand risk profiles better and innovate additional services for improved customer loyalty, such as tiered support packages or integration of roadside events with claims processing. Thus, the driver receives help without turning in a separate service request. It therefore creates an integrated ownership experience for the driver, having roadside support within warranty or connected car services. Scalability is also important to provide growth and meet seasonal fluctuating demand. Roadside assistance platforms, supported by cloud-based infrastructure and modular service options, can enlarge or shrink according to what is used at a given moment, while ensuring uniform performance without incurring excess cost. Because third-party service providers can be integrated into a network through standardized APIs, seamless growth of networks occurs while ensuring that controls over operations remain unified. This means that network operators can expand service geographically while controlling service quality across regions through a consistent oversight by centralized performance monitoring. These developments represent the more general trend towards smart mobility and the overall digitization of transport systems. Viewed as a reactive service, roadside assistance is now becoming proactive and is enabled using data to allow it to travel more safely and reliably. Improving coordination, emerging predictive technologies, and building strategy-scalable solutions tied directly to the numerous user needs will ensure that roadside assistance readies itself for the future of connected transportation. ...Read more
Maintaining consistent quality and structural integrity remains a fundamental principle in the evolving landscape of automotive manufacturing. Spot welding is a crucial method for joining sheet metal components in vehicle body assembly. Ensuring the strength of these welds is not only a matter of quality assurance but also vital for vehicle safety and performance. As designs become increasingly complex and the use of lightweight materials expands, the evaluation methods for spot welds have adapted to meet new technical and regulatory requirements. New Approaches to Spot Weld Quality Testing The most traditional methods to test spot welds have been mostly destructive, relying heavily on peeling testing and cross-sectioning. While these methods certainly give the most direct impression of weld strength and nugget formation ideas, they also destroy or significantly mark test specimens, increasing costs and time. Thus, manufacturers and designers are looking more toward non-destructive techniques (NDT), which preserve the integrity of the component but give an accurate reading. Ultrasonic and electromagnetic tests are widely practiced for the real-time assessment of welding quality in manufacturing. The ultrasonic systems recognize discrepancies, voids, or incomplete fusion using sound waves reflected throughout the weld material. The electromagnetic technique measures changes in electrical conductivity, which allows manufacturers to conduct tests online as production is being carried out. With improvements in imaging technologies and sensor integration, other approaches for spot weld testing were developed, allowing surface and subsurface analysis. Such tools foster data-oriented quality assurance, complying with industry standards and adjusting processes. Material Variability and Joint Complexity As automotive manufacturers increasingly utilize high-strength steels and lightweight alloys, spot weld testing challenges are growing. Such materials present unique issues concerning weld formation and heat, thus impacting the accuracy and reliability of testing methods. For instance, high-strength steel may have small weld nuggets that are more technically challenging to detect and measure. Conversely, aluminum alloys require close monitoring of consistency in welding because of the high thermal conductivity and oxide layer formation. Furthermore, multi-material joints and advanced joining techniques require testing solutions capable of populating a spectrum of material behaviors and properties along various weld configurations. Consequently, engineers need to consider the adaptability of testing protocols for geometry, thickness, and surface treatment modifications. This calls for increased attention to simulation and modeling tools with which the design phase predicts weld performance and potential for failure. Such predictive capabilities suggest a much more focused approach towards inspection strategies by reducing the scope of testing and moving towards a more productive testing scenario. The other pertinent factor influencing spot-weld testing arises from the ongoing build-out of production automation systems. Robotic welders with intelligent monitoring facilities can monitor real-time process variables like current, pressure, and time. The combination of this data and the outputs from testing allows manufacturers to correlate their understanding of weld integrity with process performance. However, giving testing systems versatility from different points of automation systems and cultures of varied production speed is an area to keep focus on continuously. Data Management and Analytics Another field that remains paramount in achieving long-term quality goals is managing and interpreting the test data, away from the technical aspect. As such, inspection systems generate more input data, and the need to organize, analyze, and act on this data grows. Current software platforms provide a holistic aggregation of weld test results across different production lines and locations, thus presenting a centralized real-time monitoring dashboard where the data can be historically viewed. Utilizing statistical prosthetics and machine-learning algorithms, trends can be tracked, deviations quickly apparent, and corrective actions undertaken by manufacturers. These applications ultimately form an underlying strategy for predictive maintenance, whereby test data is utilized for early warning to call for maintenance regarding preventing defects. Thus, the data obtained through spot weld testing are measures of present quality and a measure for improving future performance. Proper data integration would assure compliance with documentation and regulations attached to customer specifications. Manufacturers could use the detailed record of weld test results for traceability to demonstrate that they adhered to the specification. This transparency increases competitiveness among suppliers and is also essential for obtaining certifications for safety-critical applications like structural frames and crash management systems. Notwithstanding changes in automotive production, spot weld testing will remain an integral aspect of quality assurance. The continued development of ever more refined, responsive, and intelligent systems for conducting tests commits itself now to precision and absolute reliability at every stage of the manufacturing process. In addressing the technical and operational challenges of contemporary vehicle assembly, advanced testing of welds adds critical value to ensure that structural integrity remains uncompromised at every moment. ...Read more
Automotive vehicle coverage continues to be a core component of the ownership experience, playing a crucial role in protecting consumers and supporting long-term value retention for manufacturers and dealers alike. As vehicles become more complex with the integration of advanced electronics, software-driven features, and alternative powertrains, comprehensive coverage solutions are becoming increasingly important. For businesses in the automotive ecosystem, offering well-structured coverage plans is not only a value-added service but also a strategic differentiator that enhances customer trust, loyalty, and satisfaction. Protection against Unexpected Costs and Repairs One of the primary benefits of auto insurance is financial protection against the rising expense of unplanned repairs. Because modern cars rely so heavily on complex systems like integrated infotainment platforms and advanced driver assistance technology, a component breakdown or malfunction can be expensive. Extended warranties, service agreements, and mechanical breakdown protection packages help to cover these costs, easing the financial load on the vehicle owner. This protection is particularly enticing for used automobile buyers, who could otherwise be exposed to higher maintenance risks once the original manufacturer's warranty expires. Giving dealerships and banking institutions this kind of coverage improves their whole value proposition and could increase customer confidence at the moment of sale.   Enhancing Retention and After-Sales Engagement Plans for auto insurance also contribute significantly to the development of enduring bonds between the client and the dealership or service provider. Scheduled maintenance, roadside assistance, or preferential service rates are standard features of coverage plans that encourage clients to visit approved repair facilities over the course of ownership. In addition to providing chances to upsell more goods or services gradually, this ongoing interaction aids companies in maintaining a steady flow of income from after-sales services. Additionally, a coverage plan's frequent maintenance promotes the health and residual value of the car, which benefits both buyers and resale channels. Supporting Resale Value and Marketability Another significant benefit is the favorable effect that auto insurance has on trade-in and resale value. In the secondary market, potential purchasers frequently find vehicles backed by transferable warranties or maintenance plans more enticing. The existence of a coverage plan lowers the perceived risk for potential owners and indicates that the car has probably been well-maintained. This makes it easier for dealerships to confidently resell their used inventory, frequently at a premium price. Furthermore, providing overage-plan-backed certified pre-owned programs can increase brand recognition while reaching budget-conscious consumer groups. In today’s automotive market, vehicle coverage solutions are more than just optional add-ons; they are integrated elements of a broader ownership and customer retention strategy. From protecting customers against costly repairs to enhancing brand loyalty and improving vehicle resale potential, coverage plans deliver meaningful value across the entire vehicle lifecycle. As automotive technology continues to evolve, the scope and flexibility of coverage offerings will become even more critical in meeting customer expectations and supporting business growth. ...Read more
follow on linkedin Copyright © 2026 AutoTech Outlook. All Rights Reserved | Privacy Policy | Subscribe | Sitemap | About us | Feedback Policy | Editorial Policy