Darcy MacClaren, chief revenue officer, SAP Digital Supply Chain

Supply chains, ever more complex worldwide, are nonetheless on course to excel at handling whatever disruption comes their way.

With innovative use of AI applied to lessons learned from the global market disruptions of recent years, global and domestic supply chains are entering a new era of resiliency.

AI-enabled supply chain systems — boosted by the growth of such technology as generative AI, machine learning and predictive analytics — will give business executives the information and tools they need like digital twins to quickly respond to disruptions, improve lead-time predictions, analyze production errors more quickly, monitor asset wear and tear, optimize their operations and address customers’ needs.

To explore the promise and reality of how 2025 supply chain technology and strategies will lead to a higher level of performance and resiliency, Digital Commerce 360 spoke with Darcy MacClaren, chief revenue officer, SAP Digital Supply Chain.

DC360: What is AI’s promise for supply chains?

MacClaren: AI and machine learning are adding predictability to what’s happened in the past in supply chains — to set the model for the future and then getting the inputs from the current situation and, in real time, monitoring what the current situation is so you can react.

DC360: How do companies get started with AI for supply chain operations?

MacClaren: Senior leaders and supply chain leaders recognize the need to embrace AI. They know they have to do it — the question is how.

Start with a very defined, impactful and measurable project. What is the area that is most broken and that can have the biggest impact the fastest? And then it’s also a part of the change management to the organization and readying your people, which includes internal governance on training people on AI and setting it up. Right now is a good time to do that because a whole new crew of early talent is embracing this technology.

A lot of people start with integrated business planning because it has high value and put AI into that. You’ve got to validate your data and your rules, enable the team and measure results. And you have to validate early results for people to gain trust in AI. Do that and the early results can be pretty staggering. Then figure out how you roll that out.

DC360: A digital twin is a digital model of an intended or actual real-world physical product, system, or process. How do digital twins and AI fit into production and supply chain strategies?

MacClaren: The concept of a digital twin is to mirror your organization — so we understand the flow of material from beginning to end. You know how your products are designed, then go to manufacturing, and we know the equipment involved in manufacturing, and the logistics.

Where AI comes into play is in the many places we can use a digital twin. If it’s a plant facility, we’re gathering information on what’s really happening in the plant. We’re using machine learning to gather that information and adjust the realities and say, OK, we thought we should run this way. But to fine-tune the operation, the digital twin allows you to run simulations: What if we slow the machine down? When will it break?

The AI comes into play in making recommendations. And the more accurate it gets, the more powerful it can be to change things. It can reveal the actual throughput of a machine at this speed, but if the barometric pressure is this level, slow the machine down. It’s a way of running your organization simulating different scenarios.

DC360: And how does that increase supply chain resiliency?

MacClaren: It’s very, very powerful to see what’s going on in your organization and across your supply chain, as folks are getting more sophisticated and connected when information comes in.
You know every [supplier’s or customer’s] shipment travel. And the digital twin is learning to say, OK, this is what happened last Thanksgiving.

When tornadoes become reality, you can use these scenarios to say, OK, this is how our equipment maintenance plan should be set.

This is how a digital twin works — it’s a powerful tool to learn about your organization to do simulations and put a plan in place.

DC360: So then a company shares that information with their networks of trading partners, possibly generating or modifying orders and shipments of replacement parts or equipment.

MacClaren: That’s right. We have the concept of networks that have different areas of expertise including supplier networks, design collaboration networks, logistics networks, contingent worker networks. When you have connections to these networks in your ecosystem, you don’t have to go point-to-point to get information from individual providers.

If we’re going to lose our trucking from San Francisco to Colorado, we’ll go rail, go air, whatever. The networks help you with the visibility and your ability to react faster and better.

It’s really very powerful in getting the most out of your assets — especially when you’re talking about sustainability to get the most throughput of equipment, it also affects the carbon footprint. Asset information-sharing combined with artificial intelligence allows you to say why equipment is running better in one plan than another.

DC360: What’s an example of a B2B company with an effective AI supply chain strategy?

MacClaren: A large paper products manufacturer for commercial and retail business customers started with a demand-planning project. The starting level was a pretty immature organization, more spreadsheet oriented. They started by making sure everyone involved understood the basics of the organization’s consensus planning — how the company was collaborating with multiple internal and external stakeholders to manage demand forecasting and supply planning. They said, ‘This is our process. This is how we’re running it.”

Then they started their journey to introduce AI and got everyone on board by picking the right area they were struggling with — which was a small subset of their product line. Compiling and analyzing information from various points of their supply chain and using AI to identify areas to improve, they then measured the accuracy of AI, the results of AI and the power of AI.

What’s really cool is the concept of using generative AI to understand how AI produced useful recommendations. For example, companies using SAP’s Joule AI copilot can ask how it generated a recommended solution to a supply chain issue. It will come back and explain totally how it came up with an answer.

That helps everyone in understanding what’s going on with AI, resulting in trust and understanding. Otherwise, it’s a black box.

DC360: What key things should companies focus on to be ready for the next supply chain disruption?

MacClaren: If you want to have a resilient, agile, intelligent supply chain that allows you to be what we call anti-fragile, there are three key components:

⦁ The first one: Your supply chain processes have to be connected, from product design and manufacturing to delivery operations.
⦁ The second key component: Everyone in your supply chain ecosystem — including suppliers, logistics, contingent workers, assets — have to be connected. You have to get information from all those partners in, and you have to get your own information out to them.
⦁ The third piece: The data has to be contextualized for how it needs to be used. All of these tens of millions of data points that come into your organization: Who needs it? What do you do with it? You see there’s a delay in shipment. Maybe it doesn’t matter. Maybe it matters big time — who needs to know?

Those three components are very important. And if they’re in an ecosystem from an enterprise resource planning system standpoint with embedded AI to put these tools in place, you are much further ahead. You have the integrated networks — and you don’t have a lot of disparate data that you have to clean up.

DC360: How far along are most companies with AI-enabled supply chain resiliency strategies?

MacClaren: The majority the product-centric companies we work with — the ones that actually make things — are in the early phase where they’re just optimizing their supply chain. They’re using the algorithms we’ve had for years to identify at least the cost of whatever their objective function is. Then you have about 30% of organizations that are moving up pretty quickly to the adaptive phase, where they’re bringing in a lot of analytics.

Some smaller and newer companies and industries, including electric vehicle manufacturers, are moving along faster than others to more AI-enabled supply chains.

The long-term goal for many companies is to get to a more autonomous, AI-enabled supply chain to enhance operational efficiency and responsiveness. They’re making significant strides, but it’s a constant journey.

Paul Demery is a Digital Commerce 360 contributing editor covering B2B digital commerce technology and strategy. [email protected].

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