Written by Alexa Cheater. This guest post first appeared on Kinaxis’ industry blog, the 21st Century Supply Chain. 

There’s no getting around it. Artificial intelligence (AI) is here. From self-driving cars to intelligent digital assistants (one of whom I share a name with) to advanced robots working on the shop floor. But conspicuously absent in all this fervor around what’s new and next in AI are details and examples of how to implement these emerging technologies in supply chain planning. Everyone it seems is focusing on supply chain execution.

Don’t get caught in the spin cycle

While examples of AI and machine learning in supply chain planning are few and far between, that doesn’t mean folks aren’t making progress in this area. The trick it seems is not getting caught up in all the hype – and there’s certainly a lot of it. Nearly every company under the sun is touting their software as having advanced AI capabilities. Well it’s time for a little truth – most of those claims are just creative marketing spin (it’s ok, I work in marketing so I can say that!). There isn’t a supply chain management tool on the market today that can catapult your supply chain planning into a realm where humans are strictly hands-off.

That’s in part because AI in supply chain planning is still in its relevant infancy, but mostly because that isn’t the direction we’re heading. The robot apocalypse isn’t here. And there’s a good chance it’s never going to come. The likelihood of robots and smart machines putting everyone in the unemployment line is miniscule at best.

Hang your misconceptions out to dry

Instead of focusing on how machines are going to take over your supply chain, look instead at how you can leverage AI to improve the speed, accuracy and efficiency of your existing supply chain planning processes. To start with, you’re going to need the right foundation. You won’t see value by just slapping AI capabilities onto a platform not equipped to make use of them. It isn’t just about new technology.

The right foundation is one that lets you plan concurrently. Concurrent planning drives supply chain planning value all on its own by letting you know whether an exception or event is important or not – a critical piece to the planning puzzle. It gives you the ability to plan, monitor and respond across business functions in a harmonized way, breaking down silos, enhancing end-to-end visibility and ultimately leading you to make better decisions, faster.

When adding AI capabilities to supply chain planning processes, it isn’t enough just to collect data. You have to see the patterns behind it. AI-enabled supply chains need to be able to answer fundamental questions like:

• Is this important?
• What’s the impact?
• How can I correct the situation?

By bridging the gap between data, process and people with concurrent planning, you’ll be able to manage sales and operations planning (S&OP) and supply chain planning more effectively with stronger integration between critical business functions like finance, IT, marketing and sales. Concurrent planning lets you look at your supply chain as a whole, not just a set of individual links in a broken chain.

Iron out your expectations

The benefits AI technologies bring to supply chain planning are the ability to provide speed and accuracy beyond human capabilities. It is possible to have a supply chain that’s smarter, faster and self-healing, meaning it continuously observes and measures data and automatically adjusts or repairs itself as it finds exceptions. You supply chain will be able to detect, predict and suggest, letting you answer those fundamental supply chain questions outlined above.

Let’s explore a case related to lead times. Current supply chain planning systems typically look at one item from one source to one destination. A self-healing supply chain would observe and monitor lead times not just for that one product, but any others that share similar patterns based on historical data and slope. It would then be able to predict if other products are likely to experience similar lead-time delays based on factors like shared suppliers, distribution routes and even commodities. It would also be able to suggest possible solutions, like running a promotion on an alternate product to shift demand, or finding an alternate supplier or route.

AI in supply chain planning helps you understand these patterns behind your data, instead of just feeding you disparate streams of data that don’t really provide any meaningful answers. It’s about intelligent question answering, not just big data.

The wave of new AI-enabled technology is only growing, and coming with it is a surge of opportunity related to supply chain planning. Companies that can see past the hype and put a solid supply chain planning foundation in place now will be better equipped to utilize AI in the future to drive real value that matters.