AI adoption in the automotive sector

Artificial intelligence (AI) has become synonymous with self-driving cars in the automotive sector. This is in part due to Elon Musk’s tireless crusade to make driverless cars the norm on our roads.

But AI is driving so much more change than autonomous vehicles in the sector.

In an industry traditionally dominated by established brands and flawless design, tech has shaken things up. AI-based solutions and high-performance software are now the differentiators in success.

These thinking computer systems can be the catalyst for widespread change at the heart of the industry and how it operates going forward.

As today’s vehicles become more complex in design and build, AI will be an essential component to boost productivity, streamline design and manufacturing processes, assist with supply chain demand, and improve overall passenger safety.

Here’s our take on AI trends in the sector as it stands…

A leaner supply chain

There’s a direct correlation between automotive supply chains and the cost of manufacturing. A lack of visibility on specific parts can cause significant delays and inventory shortages, resulting in serious loss of revenue.

The average car consists of around 30,000 separate parts, and these parts are increasingly sourced from various vendors around the world. With a multitude of intermediaries and many variables at play, a minor delay at one stage can have a major knock-on effect.

The ability to accurately forecast is critical to ensuring supply meets demand.

However, forecasting and replenishment systems have historically been overwhelmed by reams of data and just-in-time production procedures which eliminate inventory cushions.

With the help of AI, original equipment manufacturers (OEM) and their partners can automate processes like equipment and tool requests, predict demand more accurately, and improve inventory tracking.

AI-powered supply chains can help meet this demand by generating more accurate forecasts, adapting and responding to unforeseen events and incorporating near-real-time data on advertising, weather forecasts, and even pricing.

The outcome of a seamless supply chain operation is improved levels of communication and better delivery of service. This means businesses in the automotive sector can enhance their customer experience and satisfaction rates, enabling them to plan more effectively to meet changing customer expectations.

Eventually, AI will empower fully automated self-adjusting systems to make supply chain decisions autonomously. Delivery routes and order volumes will be adjusted to meet predicted spikes in demand, and prices will be altered simultaneously to reflect this.

Improving passenger safety

The majority of road accidents are caused by human error; slow reaction time, poor anticipation of a hazard, and violation of the rules of the road, all contribute to deaths of road users that are preventable.

So it should come as no surprise that the automotive sector will be looking to leverage machine learning where humans fall short to help create a safer experience for all road users.

Advanced driver-assistance systems (ADAS) feature an AI component which monitors, analyses and recognises potentially dangerous behaviour before it becomes life threatening. Distractions or lane deviation can both be identified with a real-time alert being issued to warn the driver of the imminent danger.

Driving long distances is incredibly tiring for us humans – it’s why we’re advised to take regular breaks as we drive on motorways. But what mere mortals struggle with, artificial intelligence excels in.

By combining intelligent software with appropriate sensors, car manufacturers are able to monitor fatigue and driver reaction time to reduce the number of accidents on the road. Features such as temperature control, music selection and interior lighting can be automatically altered on a journey to prevent drivers from drifting off into a drowsy state.

Increasingly, car manufactures are trusting machine decision making over the impulsive decisions made by humans, which are often clouded by emotion or lack of experience.

Incidents that stem from vehicle blind spots or slippery surfaces are being brought to a peaceful conclusion by removing humans from the decision making process altogether.

Earlier this year, Volvo launched a fleet of electric vehicles with AI-driven super computers embedded in them. This new technology means AI will intervene immediately when it sees a risk rather than simply warning, which the manufacturer hopes will save lives and reduce injuries.

Helping drivers take better care of their cars

Buying a car is a huge investment, and it’s likely to be an investment that doesn’t yield any profit. AI can’t wave a magic wand to change this, but what it can do is help drivers take better care of their car and get the most out of their investment.

The cars we drive today are already pretty smart; they can alert us when fuel is running low or if something under the bonnet needs our immediate attention.

The automotive industry’s digital transformation has seen the emergence of the “connected car” – the newer, more sophisticated cousin of the car most drivers are familiar with. Crucially, the connected car has one main differentiator: predictive maintenance.

Rather than notifying drivers on a reactive basis, these intelligent motors use AI to monitor sensors built into the car to identify potential problems before they even occur. Now that’s one smart car!

AI gathers accurate information about the physical condition of the vehicle and compares this to historical data, using this as the basis for detecting faults before they occur. This feature not only saves time and money spent on diagnostics, but it also helps to improve safety and ensure dangerous faults are repaired in good time.

BMW are strong proponents of predictive maintenance in the industry and have incorporated this technology into the fabric of their production line. If stock levels of specific parts are too high in certain locations and too low in others, they are able to identify this anomaly and replace potentially faulty components before they cause operational problems.

Enhanced productivity through robot-human collaboration

Much like self-driving cars won’t see the end to drivers behind the wheel, the use of AI in car factories won’t see the end to humans on the production line. Instead, they’ll collaborate with machines to work more effectively and refocus their expertise.

By detecting faults within large data sets, algorithms free up engineers to focus on data interpretation and fault resolution, rather than searching for source information. Data gathered by vehicle sensors makes it possible to identify untimely braking scenarios, understand their causes and correct them. Without the algorithms, this process would be significantly more complex.

In-car quality control systems predominantly rely on processing data and analysis, while solutions used in manufacturing utilise image recognition and sound processing AI solutions. This ensures possible flaws in parts are detected before installing them, saving manufacturers time and money.

Industrial robots working in car factories is nothing new, but artificial intelligence has allowed companies to introduce a new generation of devices that work directly with people.

AI-controlled robots can be worn by humans to move materials, perform tests, and package products, adjusting their motions to avoid serious injury and making production more efficient.

Could artificial intelligence help drive change in your organisation?

Here at 6B, we’ve partnered with businesses in the automotive sector to help them realise the power of tech, improving overall passenger safety, creating a leaner supply chain, and maximising productivity.

If you’d like to know more about how AI could be the catalyst for a safer, quicker and more reliable manufacturing process, get in touch with us today.

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