Global experts – intelligent insights. A focus on AI and the supply chain.

In this series, global experts share insights on the impact of AI on global supply chains and data centers, covering everything from product design and manufacturing, logistics and distribution to retail. These discussions highlight the importance of data, human interaction, efficiency and sustainability as AI reshapes industries worldwide.

In this episode we hear from Matthias Winkenbach, principal investigator at the MIT Center for Transportation & Logistics.

/"I think some of the key application areas of machine learning and AI in the supply chain industry in the future will involve demand forecasting, inventory planning, network design and any kind of complex optimization problem that is becoming increasingly difficult to solve for the industry.

I am Matthias Winkenbach and principal investigator at the MIT Center for Transportation and Logistics.

The key ingredients to successfully implementing an AI strategy in your supply chain organization are having a strategy around the AI computing architecture you will use. So whether you can build the infrastructure in-house, or essentially rely on third-party cloud services. Having a solid data strategy so that you have a good basis on which to actually train models. But I think the most critical part, however, is having the right people who can translate your real business problems into the requirements for an AI model.

So you need someone who understands your business well enough to say, okay, this is the problem we're trying to address first when we implement AI. And then you obviously need talent that can convert that translation into an actual model implementation. So we are not yet at a point where we just say that AI can solve everything in a better, faster and cheaper way. And that's kind of where we're currently pushing the boundaries of research to still make sure that the domain experts who will hopefully eventually use these models and methods trust them. Developing AI methods or models that can beat existing state-of-the-art methods in terms of solution speed, solution quality, but most importantly solution costs.

In terms of AI adoption across the supply chain logistics industry, we are still in the very early stages, and that's because this is really cutting-edge technology right now that requires resources that not many companies in the industry currently possess.

But hopefully, as these methods mature and more applications are published, we hope that this technology will quickly spread throughout the industry, with smaller companies becoming more likely to adopt it as well. However, it also requires a few words of caution in principle. Because this technology is very powerful and powerful in a way that most of us don't fully understand what it is actually capable of. So be careful when it comes to sharing confidential information. Be careful when it comes to investing in the resources and talent you actually need to get AI models off the ground within your company. And also be aware that it will take time to build those capabilities. Yes, AI and machine learning are probably the next big thing in the supply chain logistics industry, but it's not the only thing you should be thinking about. Try to find the right balance between investing some of your research and development budget into exploring this space, but not the entire space. Much research still needs to be done./"

Watch the full series here: https://www.goodman.com/about-goodman…
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