From robots on the factory floor to autonomous cars on the road, AI is transforming the automotive sector. The new technology has plenty of room to expand, increasing efficiency, productivity, and safety throughout the process of automotive manufacturing. In the next five years, AI is expected to completely revolutionize the industry.
In this Q&A, DataProphet’s CEO talks about the impact of AI on the automotive industry, what steps manufacturers need to take to leverage the technology, and what the future of AI looks like.
AI is having a large impact on the automotive sector, with great attention on self-driving cars. However, our focus at DataProphet is to apply AI to the production of vehicles. Here, we see AI as part of Industry 4.0 initiatives, driving up efficiencies in manufacturing plants by improving overall equipment effectiveness (OEE), reducing defects, and improving automation on the line. These benefits are achieved using AI solutions that support predictive maintenance and active optimization of control parameters.
It is important to understand the different use cases and value adds that AI projects can provide to the line. Automating visual inspection helps reduce human error in the process and improve traceability. However, it does not reduce the chance of a defect occurring, only the chance of one being shipped. Predictive maintenance can help with overall efficiencies in the plant; however, we see that the greatest immediate value-added in production is in the optimization of control parameters. By actively optimizing control parameters with AI, production can expect a reduction in defects to occur, reducing the cost of non-quality.
Control parameters are often configured and only reviewed after a defect occurs. Even then, the review is often done with traditional analysis techniques that can not work on the full data available. Therefore, correcting the control environment is done very reactively, which can actually increase the variance of quality on the line. AI can prescriptively assign optimal control parameters to reduce the chance of the defect ever occurring and help reduce quality variance on the line. This result is achieved because there is no limit to the volume of data that AI can work with. AI actively works with the current data available to predict how future quality will be impacted and then prescribes proactive actions to steer production to achieve the optimal output.
Read the rest of the Q&A here to find out.
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