Artificial intelligence isn't a one-time implementation – it’s an ongoing journey. As supply chain organizations continue to evolve their planning capabilities, new AI use cases can be introduced over time, delivering increasing value and enabling teams to make faster, smarter decisions.
During our webinar, Agentic AI and the Road to Empower the Augmented Planner, we explored how businesses can build this journey through different use cases. In this clip from the webinar, Ruud Verstegen of John Galt Solutions, highlights some of the most impactful AI applications available today and explains how companies can prioritize them based on implementation effort, planning horizon and potential business value.
Rather than viewing AI as a single project, Ruud demonstrates how different capabilities work together to create an increasingly intelligent planning environment. He discusses how teams can combine multiple AI use cases over time to support the evolution towards agentic AI, augmenting the capabilities of planners.
Ruud talks about key examples, including AI-powered demand sensing, supply constraint optimization, and multi-echelon inventory optimization. AI enables teams to optimize across far more than cost alone, taking into account factors such as carbon emissions, minimum and economic order quantities, changing capacity constraints at ports and warehouses, and a wide range of operational KPIs.
The Atlas Planning Platform from John Galt Solutions empowers organizations across industries to leverage AI across the end-to-end supply chain, to elevate demand planning strategies, optimize inventory across an entire supply chain network, improve service levels while balancing investment, and much more.
Watch the video to discover how adopting AI can help you build a more resilient, responsive and intelligent supply chain planning process, unlocking greater value with every stage of your AI journey.
- Full Transcript
You will see that there's different AI use cases in supply chain and in supply chain planning, and what we've tried to depict here or try to explain here is that all of them will need a certain or a different amount of effort to implement.
They will have an impact on also different planning horizons and what is their potential value or the value potential of this individual use case, which you can, of course, later on also combine in your journey deploying agentic AI and having this augmented planner.
One example I touched upon already earlier is the demand sensing AI capabilities. Another one I would like to focus on is optimizing supply constraints. What you saw in the past is that maybe a lot of the optimization was done based on costs, but these days you need to think about a lot more constraints and also a lot more KPIs to focus on.
It's not only costs – it's maybe also CO2, it's minimum order quantities, it's economic order quantities, there's maybe shifting constraints and capacities at either ports or warehouses, and you need to optimize them across that whole supply chain, and that's where AI can help with in terms of level of effort, medium or high amount of effort needed to implement it. It will focus and it will support you on the medium to long term planning horizons, but we also see that the value potential is really high.
Another example is if you also want to optimize your inventory across your full supply chain or across your network, across multiple warehouses, factories, ports, even maybe customer locations.
