May 19, 2020
Dr. Jessica Heier Stamm, Kennedy Cornerstone Teaching Scholar in the Department of Industrial and Manufacturing Systems Engineering at Kansas State University, explains the applications of supply chain engineering in the humanitarian response of the current pandemic. Dr. Heier Stamm develops quantitative models and algorithms to designs and improve humanitarian relief and public health systems. Her work has modeled the impacts of facility location decisions on cholera response in Haiti and earthquake response in Nepal.
Demand for Humanitarian Response – How to Apply Industrial Engineering Toolbox to Solve Problems Related to the Humanitarian Response, with Dr. Jessica Heier Stamm, Associate Professor in Industrial and Manufacturing Systems Engineering
The transdisciplinary perspective and having multiple kinds of expertise at the table is critical to making the model the right model, whether that's in the public health and humanitarian sphere, rather in the animal environmental health sphere.
Something to Chew On is a podcast devoted to the exploration and discussion of Global Food Systems produced by the Office of Research Development at Kansas State University. I'm Maureen Olewnik, coordinator of Global Food Systems.
I’m Scott Tanona. I'm a Philosopher of Science.
And I'm Jon Faubion. I'm a Food Scientist.
Hello everybody and welcome back to the K State Global Food Systems podcast Something to Chew On. COVID-19 has affected all areas of our everyday life. One of the things that we rely on the most but rarely consider is the supply chain. Do you buy locally produced products. Even if an effort is made to do that many of the things needed for everyday life come to us through a complicated and many times an international supply chain in a time of crisis, a functioning supply chain is critical in getting essential materials to where they are needed. Today's guest is Dr. Jessica Heier Stamm Associate Professor in industrial manufacturing systems engineering and the Gisela and Warren Kennedy Cornerstone teaching scholar. Dr. Heier Stamm uses operations research and game theory tools to analyze supply chain systems in which decisions about systems control are made in a decentralized way. This work is addressing two central research questions. What is the impact on systems effectiveness as a result of decentralization? And how can systems be designed to mitigate the adverse effects of decentralization? Answers to these questions can help us gain a better understanding of the supply chain and will have direct implications for participants in that supply chain in designing and managing those process systems. In the last few podcasts, we've been discussing viruses and research dealing with the physical nature of the organism, and computer aided models that help us to understand how viruses spread. Modeling can also be used to establish methods of getting help to those that need it the most. Again, from our socially distance homes and offices, we would like to welcome Dr. Jessica Heier Stamm to the podcast. The logistics systems are critical in the movement of people and supplies. Logistics modeling can apply to medical situations like we're seeing today with COVID 19 outbreak, or Congestus will be used in the critical movement of food and supplies. I'm excited to learn more about this area of study. Jessica, before we get started on research that you're currently involved with, can you give us a little background on yourself on who you are and how you become interested in this area of work?
Certainly, first, let me say thank you for the invitation to be on the podcast. I think it's never been more important to examine Global Food Systems challenges from transdisciplinary perspectives. And I agree with what you said Maureen that the logistics are a big part of that. So I'm a native Kansan. I grew up in Quinter, on a small farm there. And early on, I enjoyed using math to solve problems. But I also had this deep desire to make a difference in people's lives. So that through a somewhat circuitous path led me to earning an industrial engineering degree at K State. And at the time that I was entering college, I didn't meet an engineer until I was a senior in high school, let alone know what most engineers did or what industrial engineers did. But I came to learn that industrial engineers design, improve and manage systems that produce goods and services. And I was really drawn to the opportunities to use math and statistics and business skills to make things better. But the examples that I saw in the standard curriculum about the systems that traditional industrial engineers were working with, were not the examples that I wanted to see myself working in through my career. I was really excited about using the same tools in that industrial engineering toolbox. To solve problems related to humanitarian response to getting the goods and services to people in need people that have been affected by disasters or long term public health challenges. And so at the conclusion of my undergraduate studies, I decided to go on to graduate school and really focus on becoming an expert in supply chain engineering, but with the intent to apply those skills in the humanitarian domain. So I earned my PhD at Georgia Tech. One other fact that I didn't mention is that I knew that I wanted to be a teacher, before I knew that I wanted to be an engineer. And so a faculty role really helped me mash up those two interests that I had, I had an opportunity to come back to K State, and I just couldn't turn it down. So I joined the faculty about nine years ago. And about three years ago, I became a graduate faculty member in the K State, interdisciplinary master's in public health program. So now I have what I tell my students is the best job in the world, I get to work with them on important challenges that the world is facing. And I get to interact with stakeholders who are making decisions about logistics systems and help them find ways to solve those problems.
And how would you over the nine years that you've been here? How have the problems or circumstances or situations that you're bringing your expertise to have they changed? Have they shifted? I sense there's going to be sort of a tidal shift coming up. But has there been a change over the last few years any otherwise?
The predominant area where I'm focusing my work continues to be in disaster preparedness and response for human public health systems. At the same time, since I've been at K State, I have worked on a number of projects that touch animal health that touch environmental health, water, decisions around water and land use. And I really see those things holistically, right, I think about myself as a person who studies supply chain systems that support human animal and environmental health. And at K State, we really prioritize and recognize this notion of One Health, how all of these components are linked together. And in studying them together from a multidisciplinary perspective, we can have a greater impact on those problems. So I do think that my focus has broadened to think about how that tool set is applicable to a wide variety of domains.
In reading through the work you've done in the recent past, your focus has been on decentralized systems. What are the pluses and minuses, the differences between looking at this type of activity from a centralized system in a decentralized as the decentralized? Better? Is it just more common? What is the focus for that perspective?
Certainly, I would say that the majority of systems if we think about these complex supply chain systems that support human animal environmental health, they really are comprised of multiple stakeholders, all the way from the private sector, to the government, to the nonprofit organizations. And so these, these systems in practice are more often decentralized than not. On the other hand, most of the tools in the toolbox that we bring to bear to model these systems have been built from a centralized perspective, the perspective that we can optimize a single function to achieve the goals of the system, that one person can, or one entity can determine what the constraints and boundaries of the system are, and make the decisions to control actions within that system to achieve the goals. And that's fine. But those kinds of models really end up optimizing components, isolated components within this broader system. To get a bigger picture and a better fidelity to the real world. We need to account for that decentralization for the different levels of decision making for the different objectives and information that stakeholders have, and for the ways in which decisions are actually made. If we think about it from that system's perspective, we may have been optimizing sub components, but the result is not a system optimal solution. And so it's very important to think about how do we adapt our traditional modeling approaches to account for that real decentralization? And then what insights can these new models give us to better understand to better design to better manage those systems, Maureen, you also asked a broader question about whether decentralization or centralization is better. I think, you know, from a philosophical perspective, the answer is it depends a lot on on the nature of the system
That makes total sense is we know, the food system, the medical system, whichever you want to look at is made up of a lot of parts. And it would not, there would not be a centralized focus on how to do that. And it's interesting to understand that you've got a lot of centralized focus is put together and that is the decentralization.
Could you give an example of a centralized system and how some of the techniques that are traditionally used would address optimization there, and then, you know, example of just how different it is once things are not centralized?
Sure. So we think about a single, let's say, transportation for optimizing the deliveries, that it needs to execute in a given timeframe, let's say a day, that single transportation firm owns the assets that it's using or contracts with its drivers in their assets, and can make decisions, how to deploy those resources to meet the demands that it's facing from its customers. A traditional optimization model works well at that scale, to be able to deploy those resources to maximize on time delivery, or maximize profit or minimize cost, whatever the objective function might be for that firm. But if we think about the ecosystem that that firm works in, even within other segments of that same company, they are serving different markets, they are serving different consumer segments, maybe one branch of that company is operating, truckload delivery, and another branch of that company is operating, express air freight with last mile, local truck delivery. In a lot of our major transportation companies, those two branches of the same company operate as independent entities. And the resources, the drivers, the pilots, the planes, the trucks that belong to one of those entities are not necessarily shared or even visible to the other part of the same company. Could there be synergy? By pooling those resources and allocating them to the pooled demand across those two different segments of the same company? Absolutely. Are there costs associated with collaborating across those two divisions of the same company? Yes. And so one thing that we focus on in in the research that my group does is how do we think about allocating the costs and benefits of collaboration of making this decentralized system function a little bit more closely to the centralized one, in a way that makes those two separate entities more likely to want to collaborate, so that they both come out ahead. And that's it, that's a hard problem to figure out how to allocate costs and benefits across entities to to move towards achieving the system wide objectives that you might achieve with a centralized approach. But recognizing the realities that those two segments of the company are still going to make their own decisions according to their own profit objectives or market objectives.
So that is the main issue there that there is not centralized decision making between the two? Or is it that there are different sets of interests, that you're not looking to optimize for a single thing, but these different subunits are trying to accomplish different things?
Both and. Right, so there's not centralized decision making. And the reason that centralized decision making may not be realistic to achieve is that there are different objectives. Even if the bottom line objective for the overarching company is profitability, the way in which each organization sees itself contributing to that, and the metrics may be different for each. I can take this a bit more specific to challenges that we've seen in public health emergency response. So we think about one project that my students and I worked on was looking at the response to the cholera outbreak in Haiti that followed the 2010 earthquake there. And so about 9-10 months after the earthquake occurred in January of 2010. In the October timeframe, cholera was discovered in Haiti. For those who may not know cholera is a serious bacterial disease that can be easily treated with routine methods. But if those treatments are not readily available, people with significant illness can die within hours. So that makes easy access to treatment facilities a high priority and combating the disease and the consequences of that disease for the population. Many international and and local non governmental organizations, health organizations, United Nations agencies were involved in the response to cholera in Haiti, the publicly available data that we accessed through the World Health Organization and the Pan American Health Organization identified more than 100 unique entities that were operating cholera treatment facilities in Haiti, at the peak of the operation, there was some degree of communication between many of those entities that operating facilities via the United Nations cluster system that tries to bring together independent organizations who are operating in the same space, to share information to share objectives, and so forth. But there was not a centralized agency who had the authority to direct these NGOs and other responders about where color treatment facilities needed to be located. And so the actual system was quite decentralized, individual organizations made decisions about where to set up their color treatment facilities. And what we saw was that there was a great concentration and services and facilities available in the capital city. But there were many rural areas where there was no cholera treatment facility available within any reasonable transportation distance for the population, or based on the conditions. And so the result of that decentralization was redundancy in some areas, so duplication of service, and disparity so complete in availability of service in other areas. We don't believe that we could achieve complete centralization, there is no entity with the authority in many of these international disasters to dictate what Non Governmental Organizations will do and where they will operate. But what we were able to do with our models was to demonstrate the potential benefit of reallocating the same resources in different locations, and how that would impact the accessibility of treatment of cholera for the population across all of Haiti. And we see those results as a starting point for saying, you know, what are some mechanisms that could encourage this still decentralized system to behave more like one that we could achieve if we did have this hypothetical centralized control, things like providing additional resources to organizations that are willing to operate in remote communities, because obviously, that operation has additional costs. Those organizations are not as visible to their funders and to donors if they're operating in rural areas. And so what are some mechanisms, even information sharing about where demand is not being met, and how organizations might be able to better use their resources, recognizing that organizations will still make those decisions independently. But providing information or providing financial support to help them make those decisions more readily could be a mechanism to moving towards this more centralized outcome?
It is very interesting. I'm kind of curious about how, how to think about modeling, this independent decision making. In some sense, everybody, all the organizations that are down there have some set of common goals. Right. You know, they're trying to address the humanitarian crisis, and they have different focuses, probably for sure. Right. But they've got some common set of goals, but you're just talking about costs and such as cost of money, but costs too, while funding right sort of through their donors. Could you say a little something else about like, what, what the incentives and what the goals of like different individual organizations are in a crisis like this and how you think about them and how you think about what's driving their decision making?
Certainly. So a number of scholars have looked at what some of those objectives are, we have not specifically been investigating the components of those objectives but have benefited from the work that others have done. There are the missions of the individual organizations, right? What is their priority? What is their organizational purpose. And to the extent that that can be captured in a model, you know, that that goes into their decision making, there is also the need to secure continued funding. And for many of these organizations, that does mean demonstrating to their donors that they're being financially responsible, that the funds that the donors are giving, are going directly to the cause to the mission. And so anything around management or logistics, or investing in systems that might make some of these practices more efficient, goes into that overhead category that is not counted as direct investment in mission. And so it can be an opportunity to educate donors about the metrics that are used to evaluate the operation of nongovernmental organizations. So there's that the need to continue to receive donations, and then to demonstrate financial stewardship of those donations. So how many people are they serving per dollar that they invest or? And so there, it gets to some very nuanced mathematics, right? If you choose the wrong metric for any optimization model, or a decision maker is not solving a mathematical model, they're using a heuristic decision process in their heads to make that decision. So if you back out what those criteria are, we can get a model to give lots of different answers depending on which metric we put in that objective function. And so thinking carefully about whether we're measuring efficiency, right outputs over inputs, or effectiveness, did we accomplish the goal that we set out to do which was to minimize the number of cases and minimize morbidity and mortality? Or even equity? Did we serve the people across the country at the same level? Or were we prioritizing people that lived in urban areas over rural areas, so how you choose those metrics, and then how you combine or balance those metrics can have a big impact on what the outcome is.
Since the stroke of the COVID pandemic, we are starting to hear more and more examples of large organizations, both private and public, that are making huge changes in what they do, what they make, what they, you know, essentially, who they are, and going from, you know, forwards going from making radiator pumps to ventilators or whatever. And are, are these the sort of models that those folks could apply to optimize this change, in course, this change in process that they're putting themselves through.
Certainly, there is a role for models to help with that. If you think about changing from producing automobiles to producing ventilators. That's potentially a subset of the suppliers, the raw material suppliers or the parts and equipment suppliers could be similar, but a variety of them are going to be different. And so identifying procurement strategy for the new parts and equipment that are necessary identifying an efficient and effective production process or how are you going to need to retool, reconfigure the factory. What does that physical space look like? And then what is the the distribution chain look like once the ventilators have been produced? They're not going to be going out through the the regular distribution chain that Ford has. So who are the other partners that need to be at the table to think about what is the end target for these new products? Yes, the kinds of supply chain models that we use to improve the traditional automobile supply chain can be reconfigured and re adapted to design manage and improve this adapted supply chain for ventilators.
I see that you use game theory tools to do some of the research that you're working on and analyzing these systems. Can you tell me what game theory is and how that works in the analysis that you're doing?
Yes, game theory is a formal way to represent decisions of multiple stakeholders when those decisions have strategic interaction. So if I'm making a decision, and it doesn't impact you, and it's not impacted by any decision that you make, there's no strategic interaction between those decisions. But the minute that something that you decide impacts, the options available to me are the outcomes for me based on my decisions, then there's a potential to model that as a game. The name comes from formalizing the strategies, the actions and the outcomes in table games, board games, and so forth. But it's been applied to economics, supply chains, political science across a variety of different disciplines. The purpose is to model and then better understand the behavior of decision makers in these strategic environments. The way that it comes into play in the research that I do on supply chains, is to overcome the limitations of the traditional optimization models that have taken this centralized perspective where there's a single decision maker. So if we use game theory, we can bring in the perspective and objectives of multiple decision makers and identify the impacts and the outcomes of decisions by each of those decision makers and then predict what what they would do in practice, and what actions would be beneficial or detrimental to the overall system based on on those decentralized actions.
So one of the main games of game theory that maybe people have heard about is the prisoner's dilemma, right? Where two people are making decisions on their own right in cooperating, they're gonna do better off but there's incentives for each of them to, to cheat or to not cooperate, right? And then that drives them to a situation where things are not good for any of them overall, right? So a lot of what I have heard about game theory is the ways in which the equilibrium states that people end up being when they're individually making these decisions on their own interests are often not in their collective best interests. Right. Tell me what kinds of things you see when you apply game theory to these human humanitarian decision making?
Certainly, so you're right, that much of the study of game theory has identified that the outcomes in equilibrium are not those that would be mutually beneficial to the players. And what we see when we apply this in supply chains, particularly with respect to public health, I'll make a couple of points. The first is we used game theory ideas to model decisions of individual patients in seeking where to obtain a vaccine for the h1n1 pandemic, right. So, vaccines were available at a number of different clinics, information was available to the public about how many vaccines were available at each location, people make decisions about where they're going to seek health care, based on their own objectives, we might imagine that those objectives include the distance or the travel time to get to the facility, and the relative waiting time or congestion that they're going to experience at that facility, which is a function of how many other people are there, and how many vaccines are available at that place. And so the traditional optimization approach, if we're thinking about a vaccine distribution problem, is to say, we're going to send out the vaccines and we can tell people where they must go if they want to receive a vaccine. That doesn't work, when people are independently rationally making their own choices about about their health care. And so a more realistic approach is to say, here's where the vaccines are, what decisions are people likely to make? And that gives us a metric for what is likely to happen in the system. And what we saw when we applied game theory in that way, modeling the individual patients decisions was that some facilities had more vaccine than they had people willing to visit the facility to receive the vaccine. Other facilities were overwhelmed and highly congested. And so what that speaks to in turn have, you know backing out some policy recommendations is the opportunity to design that distribution system a priori, having accounted for people's likely decisions. So we would make different decisions about where to send the vaccines. If we assumed up front, that people are going to make their own choices about where to visit, then we would make if we assume that we could tell them where to go. Since we know that the situation is really that people make their own decisions, let's design the system so that it operates effectively under that scenario. So that's one, that's one perspective, that's one thing that we see when we incorporate game theory into those models. And to your original point that the decisions of individual decision makers are not necessarily in their mutual best interest. The idea of game theory is that people are going to make their decisions. And in an equilibrium solution, there's no way that one patient could switch to another facility and be better off in terms of the distance she had to travel, or the waiting time that she experienced, there would have to be collective movement of groups of people who were coordinating. And so that moves to another branch of game theory, where we explicitly model the opportunity for decision makers to collaborate with one another in groups, smaller, large groups. And what we see there is that if the system the incentives, the mechanisms are designed, well, we can actually achieve group decisions that approximate what would happen if we could tell everyone exactly what to do, even when we don't have to tell everyone exactly what to do. What do we mean by that? designing the system and the mechanisms means figuring out a way to allocate costs and benefits. So if we collaborate, that presumes that we have a way of sharing information presumes that we have some binding agreement that we're going to do what we say we're going to do, these kinds of models apply more readily to organizations, necessarily, then to individuals. And so the context where we're exploring this is with healthcare and public health organizations, thinking about their supply chain decisions. So if we work together to procure the supplies that we need, there are supply chain synergies, because we have a greater total demand, we can collaborate on transportation, we can share inventory management costs. But in order for us to collaborate, we have to have information systems that talk to one another, we have to decide when we pay for transportation, do I pay for 50%, and you pay for 50%? Or you pay for 60? I pay for 40? Was that decision based on. And so if those cost and benefit allocation mechanisms are designed in appropriate way, we can achieve those supply chain synergies that we would not be able to achieve otherwise, if individuals were acting just on their own according to their own objective functions. So we see both ends of the spectrum. When we model things with game theory, we see. Yes, the reality that decentralized decision makers can end up in an equilibrium state where none of them is as well off as they would be if they collaborated. But we also see what system design parameters are necessary to achieve that collaboration and move the system to that better equilibrium.
I was just gonna ask how specific those kinds of recommendations are and well, and how general are they right sort of other general things that you can say that about supply chains where you can induce better collaboration, or you can put into place the right kinds of mechanisms that would allow collaboration to work, where you can generalize these policy recommendations to a wide variety of situations like maybe food supply chains, as well as vaccines, supply chains, as well as, you know, humanitarian situations or there's really very specific deep situation.
Great question. We are working toward generalizable insights based on general models that would be translatable across industries. We are not specifically there yet, with respect to the cooperative game models that I described. For a number of reasons we're trying to incorporate the multiple objectives and the decisions over time to be able to make those models generalizable. What we have been able to see with respect to the public health systems that we've studied, is that certain nuances of those decisions depend very heavily on the context. It's a what, what is the form of the objective function for individual decision makers? What are the demographics, the geographic configuration of supply and demand points, and so forth. And so from that perspective, the models can be really useful in identifying areas where we need additional health care providers, or we need to recruit existing health care providers to be able to distribute vaccine, for instance, where we need to account for different demographics in terms of just the underlying healthcare infrastructure in a particular location. So at this stage, we have primarily focused on what specific recommendations can we make for the dataset that we are exploring? Then we're working on backing that out to generalizable models?
At the beginning, you mentioned I think, I think I remember mentioning, interdisciplinary or transdisciplinary work, can you tease out a little more information on the need for interdisciplinary activities and understand how those different facets work into the kind of modeling that you're doing?
Certainly, and to highlight that, I think I'll go to three models that are more closely related to food systems, some work that we have done. And I'll start by saying that the transdisciplinary perspective and having multiple kinds of expertise at the table is critical to making the model the right model, whether that's in the public health and humanitarian sphere, we're in the animal environmental health sphere. I see my role in these teams as somebody that can help the team visualize what the system is as a whole and how the different components of the system linked together and then find the mathematical linkages that we can use to model those connections. But I cannot know what the right for instance, disease transmission model looks like. For that I need an epidemiologist I cannot know for human decisions, how to represent values and beliefs and norms and policy choices. For that I need a sociologist and an economist. And so I see my role as bringing those pieces together and helping them talk to one another, not the people, right, which is another challenge of interdisciplinary work is, is helping the people learn to talk with one another and understand each other. But to make the pieces of the model talk to one another in a way that links everything together. So some specific examples. I've worked on a project to understand some of the interdependencies between the beef cattle industry in the transportation industry. The case study region that we use for that project was southwest Kansas, obviously, a major center for beef cattle operations for the country. And this collaboration involved partners in electrical and computer engineering, in computer science at the Beef Cattle Institute, and in psychology, in addition to myself, to try to understand these interdependencies to try to model the different components of that system and then try to understand some strategies for managing those interdependencies. So when we have infrastructure systems, like the food system and the transportation system that depend on one another mutually they're vulnerable to shocks to cascading shocks and cascading failures in any of those systems. And so one important priority for this project was to identify mitigation strategies. For these disruptions, and understand the potential impact of secure information sharing, let's say, via blockchain technology or something similar, the impact of that secure information sharing on the system outcomes, if there is a disruption in one of these infrastructure systems. And so being able to represent each of those granular pieces in a mathematical model requires those multiple expertise at the table. So the bottom line, that connection to where my work comes in, is really to understand how to allocate these costs and benefits of information sharing across the stakeholders, the cow calf operators, the stalkers, the feeders, the Packers, how do you allocate the costs and benefits of sharing information, let's say via a blockchain technology in a way that can encourage everybody to participate, and can help achieve system resilience if there was a shock in the system, because there's a blizzard, and the transportation is not available, if there's a shock in the system, because there's a suspected foot and mouth disease outbreak. You know, how does that cascade through the system? And what strategies can we put in place to mitigate those impacts?
Interesting, the blockchain technology is something that I had seen introduced to the food system. Oh, probably, in the last five years. There was a gentleman with Walmart that's now with the FDA that really pushed it hard and had a big voice in all of this. Do you see that as being and continuing to grow as a major connector for these systems over time?
I think that it has great potential. I am not personally an expert in blockchain and its specific strengths and weaknesses and opportunities. I defer to my electrical engineering colleagues for some of those specifics. But I do think that systems like it have the potential to overcome some of the challenges that we see with adoption of information sharing and traceability to the extent that it's able to ensure the security and the privacy that stakeholders have been concerned with. And I think it has some of that potential. Again, from my perspective, it comes down to how do you allocate the costs and benefits of that technology in a way that makes it attractive? If there's an upfront investment in deploying the technology across an operation, you know, a herd or a feedlot, or a packing facility? Who bears that upfront cost? And who reaps the benefits? Is there a benefit in routine operations in terms of a premium price for the product? Or is the benefit solely in these low probability high consequence disaster events, and changes the calculation in how you allocate the costs and benefits across stakeholders?
So with the idea of interdisciplinary and partnerships, you did a great job of explaining the criticality of that, is this something that carries forward with you in teaching is this notion of inner interdisciplinary, brought into the classroom?
Yes, and I'm working to increase that as well. So one class that I have really enjoyed developing and teaching is one around quantitative models in health. And so over the the series of times that I've offered this course, we've had students from the College of Veterinary Medicine from the College of Agriculture from palliative engineering, more importantly, had speakers from across different disciplines in on campus and from industries outside to try to, to come around this common topic of using quantitative models to advance human animal and environmental health. And I think it's a really beneficial experience for the students to see how, you know, the statistics or the optimization or the epidemiology or whatever it may be that they're coming to, from their own disciplinary perspective, also has linkages with things that other people are working on. And by bringing those pieces together collaboratively, they're able to tackle problems of growth. Human importance. And I think, you know, I've found even in my undergraduate courses, where the curriculum is much more specific and detailed, and there's certain amount of information about operations research that you must cover in this semester. The more examples that I can bring from multiple industries, from multiple perspectives about ways that these models are relevant, the more excited the students get. They want to know that what they're learning can make a difference in systems that are important to them. And when I was a student, I really was really excited about the tools in this industrial engineering toolbox. I was less excited about the examples that I saw, where those tools were being applied. And so my goal as an instructor is to demonstrate the breadth of systems where these tools really do have an application and have the potential to have an impact in the hopes that that conveys the message to the students that whatever their passion is, there's a way to use these quantitative tools to make a difference in that sphere.
That's great to hear that that's that that's the approach that you're taking. Because I think it's critically important to the students, that they start seeing that broad perspective of how these things interact with one another.
Well, it's the world that we live in, right. So regardless of whether a student is going to leave with a bachelor's degree and go out into industry and begin a traditional career in industrial engineering, where they're going to pursue their own passion and path or they're going to go to graduate school work on research. No matter which of those paths and any that I didn't name they choose, they're going to be operating across multiple disciplines, multiple cultures, and need to understand the role that they play and humbly accept the things that they don't know. And the ways that they need to rely on experts in other domains.
So absolutely critical. So very, very well said. One last bit of discussion here, I wouldn't when we were just starting. Before actually, we started to record here. We mentioned the fact that our most recent podcast was with Dr. Caterina Scoglio,. And you said that you had had the opportunity to do some work with her. Do you want to speak just a bit about some of that interaction? And what kind of research you you carried out with Dr. Scoglio?
Certainly, actually, the example that I gave with the beef cattle and transportation infrastructure systems is a collaboration with Dr. Scoglio. And so far, a very, very productive one drawing on her expertise in network systems. And the expertise of folks at the Beef Cattle Institute and colleague in psychology, Gary Brase, thinking about how people make decisions in these kinds of environments. And so it's been a very fruitful and interesting opportunity to think about the way that each of us brings those perspectives to a common problem. One where I think K State is really uniquely positioned to think about the challenges in that space.
Good, I find myself highly educated, or at least much more comfortable discussing these, these topics than I was before.
It's been interesting. Thank you.
So. Jessica, do you have any questions for us or any closing comments?
You know, as I think about the role of supply chains and Global Food Systems, especially in light of the challenges that we all are facing right now, in the midst of the COVID 19 pandemic. We see supply chains on display, right? If I had said six months ago that I work on supply chains for human animal and environmental health, I get some nods. Right. But now, the supply chain is at the forefront of our minds. We don't necessarily notice the essential service that supply chains provide in delivering our most basic needs, whether it's food and medicine, or the tools that we need to do our work like a N95 respirators or spare parts for the tractor, right? The supply chain does all of those things. And we don't notice a supply chain until it stops working the way that we expect it to work. And so I think we have a really unique opportunity right now, when supply chain logistics is at the forefront of our minds to take advantage of that, I hope that one outcome of this very trying time is that we think about ways to design supply chains, so that they're more resilient. And I hope secondarily, but equally importantly, that another outcome is that students get really excited about the real impact that they can have, by studying and working on supply chains on things that impact people's lives every day.
Well, that's a great, great way to end the discussion. And I want to tell you, I really very much appreciate your time. And as Jon said, I learned a lot here. I've worked in areas that dealt with a supply chain for years, but you brought a new twist to understanding how some of these things work. So I want to thank you for that.
Thanks for the opportunity. Thank you so much. I really enjoyed talking with all of you and I hope that you stay safe and well.
Likewise, you as well. Have a great day.
Bye bye everyone.
If you have any questions or comments you would like to share check out our website at https://www.k-state.edu/research/global-food/ and drop us an email.
Our music was adapted from Dr. Wayne Goins’s album Chronicles of Carmela. Special thanks to him for providing that to us. Something to Chew On is produced by the Office of Research Development at Kansas State University.