Futureproofing Pharma Supply Chains

In the fourth part of his video interview with Pharma Commerce Editor Nicholas Saraceno, Brad Stewart, BDO’s national life sciences co-leader, addresses how AI can help support pharma leaders with managing their supply chains and addressing potential material shortages.

In a video interview with Pharma Commerce, Brad Stewart, BDO’s national life sciences co-leader, describes the challenges that tariffs may pose to reshoring manufacturing services in the life sciences industry. He highlights the complexity of supply chains in this sector, which are large, long, highly-regulated, and slow to change. Given that future administrations may alter tariff policies, reshoring decisions need to account for the potential reversal of these changes. While it can be more expensive, prioritizing U.S.-based manufacturing capacity can help de-risk supply chains, particularly as tariffs and uncertainty continue to affect global operations.

Stewart emphasizes that the life sciences industry’s supply chain is not limited to finished products but also includes raw materials and components sourced from different countries. Drug substance production often occurs in one country, with fill-finish processes in another. The unpredictable nature of tariffs on goods at various stages of production further complicates the situation, especially when combined with issues like transfer pricing and taxes. These complexities require companies to develop strategies to better manage these risks.

To address these challenges, Stewart suggests that life sciences companies focus on minimizing risks by moving more of their manufacturing capacity back to the U.S. While this may come at a higher cost, it offers the benefit of stability in the face of changing tariffs and taxes. Stewart notes that building new manufacturing capacity in the U.S. is expensive but advisable for companies planning long-term investments, as it allows them to better manage risks and focus on their core mission of producing life-saving products.

Stewart also comments on the internal constraints that nearly a quarter of life sciences CFOs are reporting as their biggest manufacturing obstacle; how artificial intelligence can help support pharma leaders in managing their supply chains; and much more.

A transcript of his conversation with PC can be found below.

PC: How can artificial intelligence help support pharma leaders with managing their supply chains and addressing potential material shortages?

Stewart: That's the question for the last two years is: how does AI really change what we're doing here? Specifically, in the supply chain, i'ts one of those areas where people are starting to use AI, which is helpful, and as I said earlier in a question, you really want to make sure that you understand your supply chain. You have backup suppliers, but you're also managing your inventories, and sometimes, when things are uncertain, you hold larger inventories just to minimize your risks, and I think that's really where people have the opportunity to use AI right now—trying to understand where weaknesses are in their supply chain, where critical parts are that they don't necessarily think about.

Let's just say there's tubing or certain bags you use, or a buffer you use all the time, and on the surface, it seems to not be a problem, because you always have it in stock. There're no complaints, but what you don't realize is you have a six-month lead time for that buffer because it's something that you have custom made, or it's shipped from somewhere else. You just have a really great person in your supply chain group who always knows to order it nine months ahead of time, and keeps a close eye on inventory to make sure you have it because they've been there long enough to understand it's a constraint in your supply chain.

I think that's really a huge opportunity. We sometimes have humans who understand what they're doing so well that it seems obvious. It seems like everything's working well, but what you don't realize is if that person leaves or retires or something else, that suddenly breaks down. I think that's a chance with AI to look for those non-obvious weaknesses in your supply chain. It's certainly an opportunity too for financial people to try and model what would happen. What’s my cost of goods going to end up being if next month, there's a 20% tariff from all my raw materials coming from this country, or a 50% tariff, whatever it may happen to end up being from all my raw materials there.

They can then start thinking through those different potential scenarios and saying, well, maybe the least risky thing for me to do is to import it for my company that the tariffs are 20% now, but supply is adequate. I know I can get it whenever I need it, I have a good relationship with that supplier, and it may not be worth saving a little bit of money with someone that I'm uncertain that I'm going to get supply from.