From the earliest electronic ignitions to today’s self-driving vehicles, automotive tech has come a long way. But, the future of the industry is far from settled.
To thrive, OEM development organizations must reinvent themselves for a new era of automotive technology. That requires an integrated, comprehensive approach to electronics engineering.
Machine Learning and Predictive Technology
Machine learning is a subset of artificial intelligence that involves the analysis of large data sets to determine patterns. It can use a variety of algorithms, including linear and nonlinear regression, neural networks, support vector machines, decision trees, and random forests.
Predictive analytics uses data along with statistical and machine learning techniques to create predictive models for forecasting future events. This type of analytics is used to predict a range of outcomes, from customer behavior to product sales, and other business factors.
Businesses can utilize predictive analytics and machine learning to improve operations, reduce downtime, mitigate risk, and increase sales. For example, a retailer can use a predictive model to determine optimal pricing strategies based on factors such as consumer trends, seasonality, competitors’ actions, weather forecasts, and news events.
Companies in virtually any industry can benefit from a system that can study and learn from huge data sets. This iterative process makes these systems increasingly capable of uncovering hidden insights, historical relationships and trends, and revealing new opportunities.
Artificial Intelligence (AI)
AI, or artificial intelligence, has transformed the way that cars are designed and manufactured. From assisting drivers to automating supply chains, vehicle inspection, and insurance, AI is helping companies improve efficiency, safety, and flexibility.
The ability of AI to make decisions based on data is called “machine learning.” Machine learning relies on an algorithm that analyzes and builds a model of how the system should work.
A robot that is equipped with AI can perform a task and produce accurate results every time without error. This saves money and reduces time in the production line.
In the automotive industry, AI-based robots can design small to major parts and perform assembly tasks in a factory line, working together with human. This makes it possible for automakers to create a personalized vehicle for customers that meets their unique needs. It also helps in reducing production costs and improves the customer’s experience with the product.
Big Data and Analytics
Big Data and Analytics allow businesses to make better decisions by analyzing information immediately. They also help companies understand the viability of products and keep up with trends.
Automotive manufacturers can use big data to develop new cars and services that cater to changing customer needs. They can analyze data sets ranging from social media chatter to customer conversations and identify in-demand features that would sell well in the market.
It can also be used to push timely OTA updates for software and firmware, improving driving experience. It can also be used to improve manufacturing processes and ensure quality control in the supply chain.
Using advanced supply chain analytics, automakers can predict when problems are likely to arise and prepare for them with lean production methods and sound supply chain practices. Streaming big data analytics can help them identify and resolve issues before they disrupt operations, so their cars get to market quickly.
Connectivity enables all the various technological systems, applications and platforms we rely upon in our day-to-day lives to communicate and exchange data. In the automotive industry, connectivity is essential for a vehicle’s ability to collect telematics data and share this information with dealers and manufacturers.
As connected vehicles continue to gain in popularity, the need for high-performance and secure connectivity is growing. Autonomous driving, over-the-air updates and integration of third-party services will all require fast and reliable mobile networks with low latencies.
The automotive industry is a highly digital industry, so it makes sense that connectivity should be at the forefront of vehicle advancements. For example, the Car Connectivity Consortium(r) is developing Digital Key, a new open standard that will allow drivers to lock and unlock their cars and share access with friends or valets.