LogiPharma Europe: Quickfire Questions With Nico Vandaele

Disruption in pharmaceutical supply chains is no longer episodic, it’s structural. Geopolitical instability, energy constraints, and climate-related events increasingly intersect with highly regulated, temperature-sensitive networks. As a result, traditional buffers such as safety stock and excess capacity are becoming less effective long-term risk strategies.

Following his keynote address at LogiPharma Europe 2026, Nico Vandaele, professor of Operations Research & Operations Management at KU Leuven, sat down with Pharmaceutical Commerce to discuss the shift from static planning to dynamic scenario simulation, highlighting how digital twins and simulation tools allow companies to test disruption scenarios in advance. These models combine system knowledge with real-time data to simulate outcomes ranging from demand shocks to geopolitical disruptions affecting transport capacity. Instead of only asking what could happen, companies must also evaluate the operational impact of different responses. Simulation provides a consistent way to assess trade-offs in cost, resilience, and service levels.

For Vandaele, predictive accuracy depends on more than data volume; it requires integrating high-quality data with validated domain knowledge about how supply chains actually behave.

PC: What are the most common disruption scenarios pharma companies have to plan for today?

Vandaele: If you know what's going on in the world, there's a lot of small but big disruptions, and that has in the past always been covered up by, say, safety stocks and so on and safety capacity. But nowadays, I think these are over, and I think being prepared and being very flexible on, let's say the ways you can counter these disruptions, is key. These disruptions have increased a lot in size and also in diversity in let's say the last two years.

How can advanced modeling and simulation change the way pharma companies approach scenario planning?

By modeling, you embed the knowledge and of course the data, but also the knowledge how a system performs, how a system works in a digital twin, in a model which you can run on your PC, and that means that you can play with it. The scenarios are, of course, a kind of a base case, which is typically the most obvious way the future would evolve. And you can base that on past experience, if that is available. But of course, sometimes, for instance, with Covid, it was not available. So there, we have to look in the most possible or a probable way the world would go. And then you make scenarios also, again, with experts like, “hey, what if it goes softly, whether it's goes roughly, whether it goes something in between?” And that's a combination - very important of what will happen. So the events, especially if it's a disease, it is an event, but for many systems, it could also be a decision from somebody outside, like now, what’s happening with the oil and so on and ocean traffic. That's, of course, an externality for many companies.

And then you combine that with your own decisions, right? It is not because something is happening this way, of course, you can also have a variety of decisions you make and these are then all a combination of scenarios, which we also run through the same model, and then you see the difference in performance. And also, of course, the difference in effort.

It's always two sides. In every scenario, you have efforts, things you do, and doing nothing is also an effort, right? So if we don't do anything and we just let it go, or you have efforts and then you see the outcomes of your efforts, whether they make an impact or not. And that is, I think, the very high usability of modeling in general, but specifically for the topic we are discussing here.

Speaking about those models you mentioned, what data inputs are essential to build accurate models that are able to look forward and avoid disruptions?

The core of the models is knowledge. So how such a manufacturing or supply chain is built up, and that's, of course, expert knowledge. And so that's the way a specific company or industry or you know, person is doing it, and that's what you embed. And then you have next to that the data. And the data are something which are, of course, evolving through time. For instance, demand data could be one, capacity data could be one, technological data could be one, and that combination is what makes your model very strong. But the knowledge, the embedded knowledge of how the system behaves and works, that's really the consistency you build in, because if you compare it to, for instance, frequently meeting and discussing these topics with the management team and so on, you're not sure about your consistency, because you can make different logical deductions in different meetings just because you're a human being, and at that time you're more risk averse or risk seeking. But with a model, the model is consistent, and that gives you a bit of a kind of a fixed ground to make your decisions on. Again, it is important not to forget the model doesn't make the decision, it's still the manager or the decision maker, the policy maker, who is doing it. But of course, the consistency, the knowledge and of course, the correct data, actual data, timely data, can of course make this support very useful.

Are there any emerging disruption scenarios that pharma supply chain Leaders should be looking out for and preparing for within the next five years?

The geopolitical situation these days definitely points to energy and all kinds of things on that. So air freight and sea freight will be impacted, trucks, road freight, that's one - but also for biopharma and pharma, cooling for many pharmaceutical products is very important. And that is a tricky one, because electricity may become, even without, let's say, the geopolitical disruptions we have on wars and so on, even then, electricity will be a very scarce resource in the future.

So now you see also that in many product development projects, that companies try to figure out whether their product should not be cooled and be kept in ambient circumstances, or could be lyophilized so that they are more in powder form, things like that. So that will also be very helpful to counteract those things. But these will be the major disruptions I think - electricity, energy, and then, of course, everything which may come as a deduction from climate. So storms, water, floods, dry, all these things will impact heavily our, let's say, distribution and our manufacturing and also the development of products.

Electricity and energy will be a big impactful disruption, irrespective. And my final quote on that is, of course, demand made disruptions. If humans decide to close waterways, to close airports, to close things for whatever reason, that is, of course, not beneficial for these types of pharma and biopharma supply systems.