Driving Growth: AI’s role in Building the Resilient, Fast & Efficient Automotive Supply Chain

With automotive Time to Market becoming a key area of competitive differentiation, how are organisations leveraging digitisation, including AI (Artificial Intelligence), to safeguard supply chains in an increasingly volatile and unpredictable political and economic landscape? Bodo Philipp explains…

 

New Supply Chain Pressures

For decades, the efficiency of the automotive supply chain has been heralded as the gold standard to which other industries must aspire. Detailed and precise delivery schedules, and quality, have enabled intricate and effective just in time manufacturing models. Yet over the past few years, as technology innovation has transformed every aspect of car design and manufacture, automotive companies have been compelled to add a raft of new vendors to the supply chain, from ECU suppliers to sensor providers, and software developers to battery manufacturers.

And while the addition of new vendors is a tried and tested process, the new function-led automotive design and manufacture model requires a completely new approach. The traditional, linear and siloed production processes no longer apply in a function-led world that demands continuous integration between software and hardware. If the new production processes are to be efficient, automotive companies must also overhaul supply chain thinking.

At the same time, of course, global supply chains have endured escalating volatility in recent years. In addition to port closures on the East Coast of the US during 2024 and continued crises in the Middle East, weather events have caused significant problems. Notable incidents include the drought that affected the Panama Canal,  the earthquake in Japan and floods in Slovenia, both of which had a huge impact on the automotive industry. Plus, of course, countries around the world are adding new regulations and sanctions, including planned revision of the EU Dual-Use Regulation, that need to be considered during supply chain partner assessments, as well as increasingly complex environmental reporting requirements.

 

Mitigating Risk with Agility and Responsiveness

To safeguard new function-led build processes, automotive companies need to build in additional levels of agility. Reserve suppliers must be on standby to avoid catastrophic disruption to manufacturing processes that are already contributing to unacceptably long time to market. The depth of data now available is key to the effectiveness of this process, enabling automotive companies to leverage concepts such as digital twins to simulate potential supply chains and the implications of possible events.

Digital twin modelling allows companies to better understand the likely impact of weather, political change, even strikes on providers throughout the supply chain, supporting better planning. The use of digital twins can also enhance capacity analysis. It provides a platform to enable companies to assess the status and resilience of both tier 1 and tier 2 suppliers, supporting a far more agile and effective response to events that can be several steps away from the core supplier network.

In addition, AI-supported software is providing automotive companies with the ability to quickly surface supply chain risks – for example highlighting suppliers that exhibit risky behaviours or may have unacceptable links to prohibited countries or business partners. It can also improve the management of Environmental, Social and Governance (ESG) data, helping companies to manage Scope 3 upstream emissions.

 

Transforming Operational Efficiency

Indeed, in an increasingly complex and regulated supply chain environment, AI-supported technology is set to play a key role in streamlining and enforcing processes, releasing automotive supply chain experts to focus on business-critical issues, from on-boarding to compliance.  With automatic contract management using AI tools to scan and analyse contracts, companies can identify savings opportunities. Purchase automation uses AI to automatically check and approve orders, making the purchasing process more efficient.

Leveraging AI to manage mundane business operations allows supply chain experts to dive deeply into the data to explore new opportunities. For example, they can gain deeper insight into market trends and shifts in consumer behaviour to predict demand more accurately and continuously improve the supply chain model.

 

Conclusion

As the traditional automotive industry strives to turn around the decline in fortunes, a fast, efficient and resilient supply chain will be a vital component in reducing Time to Market while supporting essential design innovation. With economic and political instability causing problems globally, it is now vital to leverage powerful tools, data sets and innovative thinking to embed the next level of resilience and agility into every part of the supply chain.