Practical applications of artificial intelligence for Automobile Logistics

Practical applications of artificial intelligence for Automotive Logistics

Barcelona, February 15, 2018.- We are accustomed to the fact that the technological advances applied to any industry always come from the United States or Europe. However, we forget China where there is a long list of car manufacturers. And the most advanced technologies arrive at the same time, or even go ahead. I have had the opportunity to read the following information and it has seemed very interesting to all of us who work in the automotive supply chain. Chinese technology for the automotive sector impresses. And by the way, and it is just an anecdote or a great marketing strategy, another American manufacturer puts into orbit towards Mars a convertible. Different ways to influence the market.

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I leave you with the reading of the article published in: All Things Supply Chain.

In the automotive world, the topic of artificial intelligence has received a lot of attention, especially in connection with self-driving vehicles. In the future, artificial intelligence will be used to further automate and improve the cars´ decisions and driving style. Thanks to the algorithms, they “learn” to drive themselves. But artificial intelligence, used in image recognition software for example, can also have a major impact on processes in automotive production and logistics. 

Artificial intelligence could also further automate

In the automotive industry’s production lines, robots are already doing a lot of work today. Robots are a great help, especially when it comes to hazardous tasks such as painting or welding. Artificial intelligence, starting with production, offers numerous possibilities for applications in the automotive industry. In production, it is possible to predict customer requirements on the basis of historical data, so that the development, design and production of new models can be tailored more specifically toward the customer. In addition, forecasts of sales and material requirements of car manufacturers can be made much more accurately.

Artificial intelligence could also further automate and optimize processes in automotive logistics. I took a look at what lies ahead and considered possible areas of application where artificial intelligence could increase efficiency in the future:

In the automotive industry’s production lines,.png

Thanks to an improved sales forecast, logistics processes can also be planned better. Especially in finished vehicle logistics, many basic conditions such as destination, means of transport and delivery date influence smooth processes. These parameters are used to determine both the transport routes and the parking slots on the yards. Means of transport and parking spaces on the vehicle yards could be planned much more precisely using artificial intelligence. In addition, unexpected incidents such as employee downtimes or missing parts could be better predicted. Additional data, for example, weather reports or changed legislation could also be included in the forecast. In this way, the existing optimization systems in automotive logistics could be further improved.

Another important factor influencing the optimization of routes and parking spaces in automotive logistics is the possibility of workshop jobs. Among other things, a final check of the vehicles in the workshop on the vehicle yard must be taken into account during distribution. Last repairs or modifications can still be implemented here.

Image recognition, which is becoming even faster

These inspections could be automated in the future with the help of artificial intelligence. Damage checks when leaving the ship would then be carried out with pictures. These photos could then be analyzed using artificial intelligence techniques. Examples that already implement this include the HailMaster Application. It is a tool that uses augmented reality and computer vision to precisely detect and measure the damaged area when dents are left on a car because of hail.

Image recognition, which is becoming even faster, could help car terminals check whether there is any damage to the vehicles. These images could be taken at electronic gates. In the event of damage, additional inspections would then be carried out.

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On the other hand, it would be possible to further improve network planning for vehicle distribution. An efficient network must be planned in a cost-optimized and operationally feasible way. However, restrictions such as contractual agreements must also be taken into account. In this case, artificial intelligence could automatically plan the network and consider all conditions while simultaneously integrating the network’s historical data.

The two most immediate challenges in the automotive industry are autonomous and electric cars. However, there are more technological challenges to follow, study and implement. This report on Chinese technology is just an example.




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