What’s the true value of AI in the automotive supply chain?
Capgemini’s Head of Automotive Laurence Noël describes the exciting results of a recent collaboration between Capgemini and supply chain risk management specialist Prewave.
Supply chain challenges for the global automotive industry seem to keep arriving in a never-ending stream, one of the latest examples being the threatened (but averted) strikes by port workers on the US East Coast. Market volatility is currently further increased by geopolitical tensions and by evolving EV incentives. In such an environment, it’s hard for automotive manufacturers with a global supplier base to achieve the level of supply chain resilience they want or to confidently manage their supply chain risk.
Auto companies face an additional, and related, challenge: their risk management doesn’t yet extend far enough along the supply chain. The lack of visibility of more remote tiers affects not only their ability to minimize the impact of supply chain disruption but also their compliance with increasingly stringent regulations about ESG accountability, such as the German Supply Chain Act.
AI looks promising – but how can the promise be realized?
Most automotive companies suspect that – as we argued in a recent blog article – new advances in AI could help them overcome these challenges. Possible benefits of AI include getting ahead of crises through improved supply chain resilience, better identification and management of risk (including supplier-centred risk), and visibility of more tiers of the supply chain, which could help companies meet sustainability goals.
AI looks promising in part because of its ability to instantly process large volumes of data, create data models, and use learned patterns to predict the future. Given the rapid progress in this field, the possibilities are expanding almost by the day.
However, until now, making that happen has looked like an offputtingly large and complex task. Automakers are often too busy, and in too much of a hurry, to spend a lot of time experimenting with “bleeding edge” technology – or even to seek out the innovative startups who are known to be ahead in AI. They also want AI to be integrated into a total supply chain solution rather than implemented as a point solution, with all the issues that can cause.
In addition, the application of AI to supply chain problems holds significant inherent complexity. For example, to build a rounded picture of the current and future supply chain situation – and to build and evaluate remediation scenarios – it’s necessary to pull together data from multiple sources. Internal data might relate to projects, products, sustainability KPIs, and risk assessments. External data might include publicly available company certifications, news, and annual reports, as well as specialist third-party data about everything from financial scores to carbon footprints. This data can be in more than 150 languages and any number of formats, with much of it unstructured.
Demonstrating the power – and accessibility – of AI-enabled supply chain intelligence
Capgemini knew that these complexities could be overcome and has been working to do so on behalf of our automotive clients. In a recent collaboration with supply chain risk management specialist Prewave, we’ve built an AI- and web-based solution to tackle current supply chain issues. We were thrilled to demonstrate the result at the recent Paris Motor Show and gained great feedback from our clients and stakeholders.
The results of this collaboration show that an AI-powered solution can ingest all that multilingual, multi-source, unstructured data and use it to inform supply chain risk management. As a result, we’re confident that we can efficiently embed Prewave-powered solutions within client systems, providing features such as:
Supplier mapping – giving the automaker visibility of most tiers of their supply chain
Risk and sustainability scoring – informing supplier selection decisions and enabling share-of-business refinements
Real-time risk monitoring – telling the automaker exactly what’s going on in their supply chain at a given moment
“Especially in the automotive industry, which is very dependent on supply chains, we help to identify risks early and take action. The goal is not to offboard suppliers, but to improve them. Prewave leverages 13 years of AI research to offer an end-to-end approach to supply chain risk management, integrating risk, sustainability, and compliance into a single platform. Our AI maps 1.3 million global suppliers, analyzing 150+ risk categories and 400+ languages, delivering real-time insights and empowering businesses to take direct, preventative actions, proposed and evaluated by the solution.”
Harald Nitschinger, Co-founder and CEO, Prewave
As a result, we know AI can be used to create flexible, easy-to-use risk intelligence that supports the company’s strategic vision—including its supply chain sustainability goals—as well as its day-to-day management of supply chain issues. Entities such as transportation hubs, raw material markets, and suppliers can be monitored to maximize supply chain visibility and enable comprehensive supply chain management.
We’ve also shown that AI can help predict the automotive supply chain’s future, including when disruption will occur and what impact it will have on both upstream and downstream activities. In addition, AI-powered predictive methods can be used to assist with supply chain mapping, although complete penetration can never be guaranteed. These methods can also help to evaluate and rank alternative scenarios as an aid to decision-making.
Could AI-powered supply chain intelligence help your organization?
Would you like to see our solution in action, or find out how AI-enabled intelligence can tackle supply chain challenges in the automotive industry so that you achieve both day-to-day and strategic supply chain goals? Please contact our supply chain team today via the form on our automotive supply chain page.
