I returned back from a great trip to the 2023 German-America Frontiers of Engineering symposium held in Jülich, Germany. This event brought together early-career engineers from Germany and the US to talk about engineering in relationship to four disparate topics. I learned a lot from the fellow speakers and participants on supply chain resiliency, the hydrogen economy, neuromorphic computing, and sustainable production and the circular economy.
One of the fun things was making connections between these different topics. One big connection was that a central challenge to the successful design and operations of these critical systems is variable. Variability is the villain, yes for supply chain stories, but also in our energy systems, our production systems, our computational systems, etc. Thus, I would like to introduce you to the symposium’s mascot: Mr. Variability.
I was honored to be one of the speakers in the supply chain resiliency session. The abstracts from our session provide a critical, but also hopeful view of the future of supply chains. If you are interested in my take on why supply chain design is challenging and how we can make future supply chains more resilient, check out this video of my presentation, read my abstract, or reach out to me for further discussions.
Jennifer Pazour 
Fascinating and thought-provoking, so thank you Jen! An observation: your conclusion is that we need better decision-support methods and tools, and that the physical world needs to adapt. I would offer that there are two fundamental challenges to making this happen. One challenge is the very different “mind maps” in these two domains or communities. In the decision-support modeling and analysis community, the “mind map” is mathematical and almost always methodology specific, i.e., optimization, simulation, stochastics, statistics, ML, etc. In the physical world, or in practice generally, the “mind map” is specific to the domain–the supply chain people speak their language, the warehousing people speak theirs, the factory people speak theirs, … in each domain the decision makers have domain-specific concepts, terms, strategies, etc. We can formally model each of these different “mind maps”. What has been missing and what is becoming so critical as the supply chains grow in scale, scope and complexity is a way to map formally and effectively between the math/methodology languages and the application domain languages. The second challenge is operational control–the vast network of operational decision-makers that collectively determine how the SC performs. We have, at best, very rudimentary models in the decision-support community and in fact, very ad hoc decision making throughout the physical supply chain. Until some order is brought to this mess, we will not be able to fully realize your (very ambitious and very worthy) goals.