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Portrait of author Clodagh Quigg
Clodagh Quigg

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Solving automotive supply chain challenges with AI

By Clodagh Quigg on May 19, 2022

What makes artificial intelligence the most valuable tool for any automotive supply chain?

In the past few years, we’ve seen artificial intelligence (AI) play an increasingly prevalent role within the automotive industry. From self-driving cars and in-vehicle entertainment, to safety, mobility and weather interconnectivity, it’s safe to say that AI is no stranger to the world of automotive.

While these AI examples are all extremely exciting advancements for the experience of drivers, what about the manufacturers behind the scenes – how can these organizations reap the benefits of AI?

The key to any automotive manufacturer’s success lies in the utilization of AI across the supply chain process. Within this sector, AI can be leveraged in numerous areas, from assisting with vehicle assembly to the handling and automation of repetitive tasks during production and post-production. AI is also a very powerful tool when it comes to identifying any defects or potentially-serious issues.

Given that automotive organizations have been manually running their processes for years and years, you may question why we now need AI – so here’s an example. 

An average passenger car consists of approximately 30,000 parts. These parts are usually sourced and ordered from hundreds of suppliers, from different parts of the world. With undependable market availability, an intricate manufacturing process and limited access to data-focused talent, the sheer number of moving parts in automotive manufacturing make it increasingly challenging for humans to make vast, complex decisions at scale. However, being able to distill value from masses and masses of data is an ideal challenge for AI.

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AI can take automotive manufacturing performance to the next level by eliminating the guesswork and reliance on gut feel – in short, leaving no room for chance! There’s an incredible volume of data created by modern manufacturing equipment. When you also factor in external and environmental data, it becomes even more complex.

By gathering this data and utilizing AI and machine learning models to gain a holistic view of it, and by applying its insights to the way we make decisions, we ultimately unlock a whole new generation of AI-driven data strategy.

At Peak, our AI platform provides a perfect example of the kind of results that AI and ML can achieve for businesses in manufacturing. Our suite of problem-solving AI applications assess patterns, warnings and insights constantly, leveraging all of an organization’s data to provide game-changing recommendations, delivered in real time.

Peak's applications assess patterns, warnings and insights, leveraging all of an organization’s data to provide game-changing recommendations in real-time.

From here, supply chain decision makers can use these AI-fuelled recommendations to automatically make any necessary adjustments to their supply chain. 

Some of the core areas AI can add value to the supply chain are within optimizing inventory management, part and product buying (especially during industry-wide shortages) and pricing optimization. On top of this, it can also power data-driven outcomes in areas like improving logistics that directly contribute to a company’s sustainability targets.

However, the benefits of AI stretch beyond the supply chain, with applications across demand planning and marketing – for example, understanding customer behavior, minimizing churn risk and ensuring the best possible return on advertising spend (ROAS).

As the role of AI becomes more commonplace in businesses, it’s evident that this technology is already having a transformative impact on the workplace. Now, we can have the right decisions always available at our fingertips – eliminating silos from our business and making concerns over bottlenecks a thing of the past. 

These are the changes that AI is driving; an easier, more productive working life with more time to spend on other tasks rather than getting bogged down in the data. It’s all about working smarter, not harder; and when you look at it like that, utilizing AI in any organization seems like a no brainer!

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