Suppliers and the need for change

The automotive industry is experiencing a once-in-an-era transformation. Above all, the industry needs to electrify – but it must also respond to customer demand for new Vehicle-as-a-Service business models, set within a true circular economy. These forces of change demand new operational models at every level in the auto value chain.

For some companies, the threat is existential. Auto suppliers that fail to embed innovation in their business models – and to demonstrate this to capital markets and wider stakeholders in the automotive ecosystem – will run out of customers and run out of money long before the automobility revolution is complete.

Many automotive companies have been surprised not only at the depth of change brought about by the pivot to connected, autonomous, shared and electrified mobility, but also by its speed. In addition to the widely forecast increase in demand for sustainable mobility, government requirements – and in some cases possible outright sales bans – are forcing automakers to adopt electric-vehicle technology much faster than they were anticipating only a few years ago.

Consider the fact that in 2022, sales of electric vehicles rose 60% year-on-year. The majority of those sales were in China, the world’s largest EV market, but sales in Europe and the US are set to grow fast. We forecast that sales in the US will rise tenfold between 2021 and 2030 to 6.3 million units a year, and the US will be the fastest-growing EV market in the world, with 34% growth to 2030, compared with 33% in Europe and 22% in China.

It is unlikely that the EU’s proposed exemption of e-fueled vehicles from the 2035 phaseout of combustion-engine cars will significantly impact investment in electric vehicle components; most auto suppliers are already committed to transitioning their product portfolios to electric technologies. This became more than clear at this year’s AUTO Shanghai recently held in China.

However, the growth in electromobility masks the fact that in the shorter term both OEMs and supplier companies remain heavily dependent on conventional internal-combustion technology for revenues. In 2022, just over 81 million road vehicles were manufactured, of which 8.7 million were pure EVs and 13.08 million were hybrids.

This leaves small and medium-sized supplier companies with an acute challenge. Unlike large OEMs or large suppliers, they are typically highly specialized, and lack a broad span of technology competence. They cannot afford to make multiple technology bets as they prepare for a future in which many of the critical components of today’s vehicles will no longer be required. An expected fall in global sales volumes in the short term – as the chart below shows, vehicle production forecasts for 2023 have recently dropped by around 6.6 million units – only make it harder still for small and medium suppliers to plan and finance their transition.

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.“

Clinging to California is not enough: Do the easy part first

California is the market model: More EVs sold than in any other state, 34% of all 2022 US EV sales^1,17, second highest charging point density of 128 points per 100,000 licensed drivers and the first state to introduce clean vehicle legislation in 1990. California’s success in the EV game can be attributed to early and continuous adoption of EV-forward policies and regulations¹⁸, public and private purchase incentives, and a vast charging infrastructure. To scale EV, manufacturers need to identify regions that follow the Californian model in optimizing the subsidy, infrastructure, and policy triangle. 

OEMs should focus on areas where the triangle is complete, which is rarely found at the state level so OEMs ought to go deeper to a regional level. When applying the triangle principle, OEMs should assess population density as it skews the charge point per population metrics. For example, the state of Washington has a small number of public chargers per licensed driver and limited state subsidies, but a high EV penetration rate, primarily due to a high density of chargers around Seattle and limited infrastructure throughout the rest of the state. The state of Colorado has higher number of chargers per licensed driver and more subsidies than Washington, but significantly lower EV market share, due to a smaller number of residents throughout the state and more dispersed pockets of EV infrastructure. In addition, city-level incentives like charging infrastructure subsidies and access to parking discounts (e.g., Sacramento)¹⁹ play a role at the municipality level. OEMs should also weigh the consumer demographic and psychographic differences within regions. For example, Chicagoland (Cook County plus surrounding counties) residents live in more densely populated areas and, on average, have a higher income than downstate Illinois²⁰. OEM’s segmentation of target markets needs to go deeper than state level to reflect local particularities in population make-up and public support. 

What is in it for me? Mass EV adoption will accelerate with cheaper EVs and customer education

Today, even EV considerers see a gap between their willingness to pay and current EV prices. 36% of US customers said vehicle sticker price is a top three purchase factor, and 48% of customers say they are not willing to pay a premium for an EV². Yet the December 2022 average transaction price of an EV was $12,000⁵ more than an ICE vehicle⁶. While Tesla and Ford (among others) have recently cut MSRPs on the Model Y (up to 20%)⁷ and the Mach-e (up to 9%)⁸ respectively, the ‘price-willingness to pay delta’ coupled with slim profit margins will require OEMs to move beyond the tactical (pricing) level. A sustainable EV business model will benefit from significant advances in OEMs’ supply chain and production (e.g., EV vs. ICE price competitiveness is expected when battery cost falls below $100/ kWh⁹) and smart exploitation of purchase and production incentives (see the second part of our series for further details).

Considering the high share of vehicles financed or leased in the US (above 80% over the last three years)¹⁰, monthly payments may play an even stronger role than list prices. Narrowing the current delta between gasoline and EV leases ($337) and loans ($210)⁷ requires OEMs to increase residual values of vehicles – e.g., through advancements in battery durability, parts quality and vehicle certification. In addition, creative expansion of their businesses along the vehicle’s life will further contribute to attractive BEV economics.