What ChatGPT can do for automotive operations

However, we assume that technical solutions will become available to address these challenges, and so it makes sense for operations teams to start looking at potential use cases now. We have identified seven areas of operations where generative AI could offer significant benefits:

1. Concept phase: how can we learn more about what the customer thinks?

OEMs spend millions learning about how customers perceive a car and its features. In the development stage, potential customers are brought in to carry out interior design reviews and test drives. Nevertheless, errors often seem to fall through the cracks. Think of the mockery German car manufacturers had to endure when they first introduced cupholders in their cars for the US market: they were much too small and the wrong shape for customers there.

A generative AI tool could be used to assess large volumes of customer test drive and review responses, spoken or texted directly into an app. A very accurate survey of customer sentiment could be generated based on this. A developer could then feed their latest drawings into the software and ask the generative AI whether the design would be appreciated by customers, based on its assessment of the responses.

2. Requirements engineering: can generative AI streamline supplier interactions?

When OEMs buy parts from suppliers, they start by transferring a list of requirements. In practice, the requirements are often taken from the previous version of the module or system, and adapted for the new order. This means that in most cases, that number of requirements increases from one generation of cars to the next, as engineers add more requirements but rarely delete any of the old ones.

The sheer number of requirements, and the limited relevance of some of the older ones, leads to situations where suppliers don’t actually read and understand the full list before they start developing a product. For a part such as a battery inverter, a generative AI application could be used to crosscheck which requirements actually apply to the specifications for the new version, and to highlight the legacy items which can be cut. Both OEMs and suppliers would benefit from more precise requirements and more focus in the development stage.

3. Quality improvement: how can we really access lessons learned?

All automotive OEMs and most suppliers have databases in which they document faults and problems over the lifecycle of a product, as well as the measures taken to solve those issues.

These databases contain tens of thousands of line items, with lessons learned from many engineering projects. However, this valuable knowledge is buried in not very user-friendly programs and cumbersome to use. The result is that during both the development phase and series production, engineers often don’t have the right information to hand, despite the fact it is held by the OEM. This leads to avoidable design faults, and fault resolution during series production takes longer than necessary.

Generative AI applications could be a user-friendly interface between the engineer and those vast databases. It would be hugely valuable for OEMs if AI could quickly find the “right“ lessons learned and provide them to the engineer. However, this is a prime example of a task where keeping a “human in the loop” is essential, to make sure the right questions are being asked and that the results coming back are addressing the specific quality issue. 

4. Maintenance: how can automation reduce stock levels and speed up repairs?

Generative AI can compare the specifications of parts against existing in-stock parts or search for alternative spares, to prevent downtime in out-of-stock situations or find cheaper solutions. In addition, AI could be used to quickly analyze maintenance and past damage reports to identify the most frequently ordered spares that need to be in stock at all times.

When it comes to repairs, we see a number of ways that such an AI tool could be useful. Its language processing capability could be used to filter through past damage reports, manuals, and the history of a part to quickly find the right solution for replacing and maintaining it on the shop floor, as well as in the factory.

One of the main potential uses for ChatGPT specifically is as a chatbot, and it could be used for troubleshooting during a repair, comparing the machine manual to damage reports and documented repair procedures, to identify promising solutions and minimize downtime. In the near future, AI chatbots might also be capable of providing first and second level support for customer and service personnel during a repair.

5. OEE: how can generative AI increase factory productivity and profitability?

AI could be used to analyze shift handbooks, maintenance logs and quality reports and link the information to certain events, such as shut downs, quality problems and setup-times. By working in combination with a data analytics solution that identifies patterns in process parameters, a generative AI tool could identify and suggest optimal set-ups and schedule preventive maintenance to prevent potential equipment failures.

Armed with this information, production managers can make more informed decisions and take corrective actions to improve OEE, and increase factory productivity and profitability as a result.

6. Supply chain: where can AI cut the burden of paperwork?

Generative AI could be used in supply chain operations to analyze and interpret the complex legal texts linked to deliveries, including tariff laws and regulations from different countries. These are subject to frequent changes that can be challenging for supply chain managers to keep up with. By processing vast amounts of legal data quickly, generative AI can identify the relevant new changes in legislation and provide actionable insights.

The AI could also be trained to identify opportunities for companies to leverage differences in tariff laws and regulations from country to country, in order to reduce tax costs and speed up the operation of their supply chain. Both examples will help supply chain managers to design more efficient and cost-effective networks.

7. On the road: how can generative AI lead to significantly higher customer satisfaction?

Despite all the automotive industry’s quality management processes, there are still occasionally problems with cars once they are on the road. We have written in the past about the potential for OEMs to make better use of field data to catch and address such issues early, and with the advances in AI, potential solutions to help them do so have moved closer. Today, a customer with a problem while driving phones the OEM’s call center. But with a chatbot powered by generative AI, instead of working through the possible reasons for failure together on the phone, the customer support worker could ask the chatbot what the most likely problem was, feeding in the customer’s report of the fault and the car’s over-the-air-failure codes.

One of the next steps in connected vehicle development is likely to be to integrate AI functionality into the car itself so the driver can ask for help directly via voice commands. For example: Driver: “Why is the rear camera not working?” Car: “The camera is not working because the trunk is open.“