Discover how top researchers at the University at Buffalo’s School of Management use Kirchner’s U.S. maritime import records to analyze global trade flows, supply chains, and economic trends. In this interview, Dean & Professor Ananth Iyer and Professor Aditya Vedantam discuss why they chose Kirchner’s U.S. import data (Bill of Lading records), how it supports academic research, and what unique insights it provides.
Professor Ananth Iyer is the Dean and a renowned expert in supply chain management, logistics, and operations strategy at the University at Buffalo. He has extensive experience working with large-scale trade datasets and has published extensively on inventory management, supply chain risk, and global trade dynamics. Previously at Purdue University, Iyer has also collaborated with industries and government agencies to improve supply chain resilience and efficiency.
Professor Aditya Vedantam is an Associate Professor specializing in sustainable operations, supply chain management, and decision sciences at the University at Buffalo. His research focuses on how data-driven insights can optimize trade and supply chain networks, with particular attention to sustainability and economics. Vedantam uses big data and analytics to drive research in green supply chains, circular economy, and operational efficiency.
Transcript of the Interview with Professor Ananth Iyer and Professor Aditya Vedantam.
[Kirchner Opening Music] From sea to trade, across the land, Kirchner’s legacy, we understand. Global data, insights wide, For research strong, with Kirchner by your side.
Niklas Vesely @ Kirchner: Welcome and thank you for joining us. Today, I’m honored to speak with two distinguished professors from the University at Buffalo’s School of Management—Dean and Professor Ananth Iyer and Associate Professor Aditya Vedantam.
I first worked with Professor Iyer during his time at Purdue University, where he played a key role in advancing supply chain and trade research. Now at Buffalo, he continues to focus on logistics, operations, and global trade, making him a great example of a researcher with long- term experience working with large trade datasets like Kirchner’s.
Professor Vedantam brings deep expertise in supply chain management, operations, and sustainability. His work explores how data-driven insights can improve trade and economic decision-making, making him an ideal person to discuss how researchers and students at Buffalo are using Kirchner’s data for academic research.
Together, they offer valuable perspectives on how large-scale trade data can support economic research, policy analysis, and industry applications. Today, we’ll discuss why the University at Buffalo chose Kirchner’s U.S. import data set, how it’s being used in their research, and what insights it has provided.
I would like to start by introducing Dean and Professor Ananth Iyer.
Professor Ananth Iyer is Dean at the University at Buffalo’s School of Management. He holds the H. William Lichtenberger Chair in Management and he is a renowned researcher in supply chain management, logistics, and operations strategy. Previously at Purdue University, Professor Iyer played a key role in supply chain and manufacturing research initiatives, and also published extensively in top academic journals on inventory management, supply chain risk, and global trade dynamics. Professor Ananth Iyer has worked closely with industries and government agencies to enhance supply chain resilience and efficiency, making him a strong advocate for data-driven decision-making in trade and operations research.
The university at Buffalo has purchased Kirchner’s US Maritime Import Records from January 2022 to December 2024. Can you describe Kirchner’s trade data set to someone who has not worked with it before?
Professor Ananth Iyer @ University at Buffalo: So for our interest in the data – it is that it is focused at the level of the individual container. And that it tracks the end to end flow of the container; all the way from a supplier – to a port – to the ship – to the destination port – all the way through to the final customer.
The second portion of the data which is particularly interesting is the focus on the HS code, a classification of the items. One of the things that is really important, is that it shows at a local level, or even at an individual company level, their link to the World At Large.
The U.S. Import Records are open records unlike the import and export data of other countries. Therefore you can get an idea of how each region gets linked to the World At Large. This is vitally important for students and it’s also important to understand how trade links change as a function of government policy changes.
For U.S. ’22 to ’24 – in fact ’25 will be particularly interesting, so we look forward to to getting ’25 data too as soon as it becomes available – the main reason is that very often people don’t understand how decisions made at government levels impact the local level. And it’s this Global-Local-Connection that’s vitally important for researchers and students. That’s one of the reasons we’re excited to be working with Kirchner’s U.S. Imports Data Set.
Niklas Vesely @ Kirchner: How deep do you dive into the data? You’ve been mentioning that you can see the impact of policy changes down to the individual container – do you really check individual containers? Or do you look at the big picture?
Professor Ananth Iyer @ University at Buffalo: So there is some work to be done to clean up the data, don’t get me wrong. And the reason is that individual shippers don’t have their manifests being completed for them. There are inconsistencies etc.
But once that is fixed (these days using a lot of generative AI tools you can fix it) we go down to the individual customer who’s receiving the shipment. This way we can go to a zip code, like for example our local zip code, look at all the companies in that area, and ask “how are they connected to the world?”, “what do they buy and where do they buy it from?”.
This is important for us because one of the questions that we ask is if there is a local company that’s producing a certain product which is being imported by other companies. Then we tell that company who in their same region is buying the same products (with that same HS code). It’s a simple thing for local manufacturers to go to those companies importing the same product and say “I can provide you these services and I can be your backup supplier”. So there is a trade impact which is far more local. And we think that this has a big impact in companies thinking about expanding, adding capacity, automating, etc!
There is sort of a value proposition we can make. So we are eager to do that. I do want to caution that in order to do it with a high degree of precision some work needs to be done to clean up the data – but that is all completely reasonable, and this is really something for our students to understand: that manifest data is not clean, because shippers choose! Therefore some judgment is required. And there’s actually learning in that judgment! So we’re really down to the individual customer. We are also thinking about somebody who is buying a particular component: It’s useful for them to see where other companies (importing that same HS code) are buying from and who they are buying from. So there is both a a selling opportunity but also an opportunity to streamline the procurement by looking at where everybody else is procuring from.
Niklas Vesely @ Kirchner: Very interesting! The data set is huge, but at the same time you can look into the individual container. How does this data compare to other data sets you’ve worked with in the past for other research?
Dean and Professor Ananth Iyer @ University at Buffalo: There are two separate questions and I’ll let Aditya talk about some details. But there are two things: one is just in the area of trade. If you look at Imports there are many people who provide summaries of imports but they don’t give you access to all of the data. I think your data set – the Kirchner U.S. Import Data Set – is one of the unique ones that gives you all of the data in one place. So that is pretty significant, that is very important, and it’s very useful for researchers. Because our interest is not a commercial interest. We don’t have a commercial interest. We have an analytic interest. And we are a university, so most of what we do is to just assist. And in many cases it’s small businesses, not large businesses – they don’t need anything from us.
If you had asked me a few years ago this would have been a tremendous data set in terms of size. But these days when you’re trying to compare it with the kind of large language model data sets.. I think in the logistic space this is a pretty comprehensive data set and a very large data set to use in the classroom.
And I think that’s where I would emphasize: in the classroom. Students get to realize the detail and they get to jump in and do an analysis. For example you can give an assignment saying: analyze shifts in Imports of apparel. And suddenly people will see that imports from Vietnam have grown so much that (I think as of last year) the total imports from Vietnam in terms of containers into the United States just crossed the imports from Germany.
Nobody would have expected that this much volume changed. But you see it in the data. Right now the composition is different. Obviously imports from Germany have a slightly greater value added than imports from Vietnam.
It’s just useful for students to understand that kind of a shift, so that they start seeing how things have changed in terms of flows. It’s Unique. In that sense I’ll hand it over to Aditya.
Niklas Vesely @ Kirchner: I have two more questions I would like to ask you. You mentioned that you’re looking forward to the data of 2025: How new does the data have to be? Is it by month? is it by year? Or do you compare even bigger sections of time?
Dean and Professor Ananth Iyer @ University at Buffalo: So by month would be great and the reason is because there’s a monthly report that is put out by various publications, as well as other individual reports. The port of Los Angeles, Oakland, New York, Etc, put out reports regarding flows. And that’s because there’s a lot of focus on Imports, Exports, Etc. And that is something which many people think of as an early report of how the economy is doing. And there are so many reasons associated with the growth in Imports.
It might be people are importing before tariffs go up, it might be all kinds of reasons, so certainly monthly is important. The date as such is a little tricky because it’s a function of when the ship actually pulls into the port, but monthly is really good – at least from my perspective.
What’s also useful to see is where the goods are coming in: There was this period when the prices in the Panama Canal went up, or there were issues with respect to the suez canal and you had to go through the Cape of Good Hope, there were a lot of shifts in regards to where the Imports were coming in. When potential strikes were being announced by longshoremen… so it’s useful for us to see the U.S. Import Data at the monthly level.
A lot of the events are happening more at a smaller granularity. Where an end product is ending up, in different states – that’s also very useful information. So month-granularity at the state level, and the ports at which the flows are coming in, and the countries from which they’re coming to these ports. Those are some important pieces of information.
Niklas Vesely @ Kirchner: Beyond your current projects, in what other areas of research do you see Kirchner’s U.S. maritime import records potentially also being useful?
Dean and Professor Ananth Iyer @ University at Buffalo: As I mentioned before, Economic Development. Giving people, giving local companies an idea of the opportunities available. That I think is important.
It really goes into the area of resilience of these small companies. Because the small companies, the smallest shock hits them very hard. They’re selling a particular product, there’s a problem in that product, then the question is: „what do I do?“. The greater the range of options they have the better – and I think one of the things that we’re looking at – is we’re trying to tell the companies: „Take a look at your capabilities. You use your capabilities to produce this product today. But you’re capable of producing a number of different products. In your back pocket, if you knew all these other products, and you started working on other things too, you would create a natural hedge.“
That is something we’re trying to assist small businesses with. Because small businesses cannot afford to hire consultants. They work with students and others just to give them an idea of what to do. We’re just providing a service to them. So it’s more for students to learn, they get an opportunity to talk to businesses which is particularly important. Otherwise it’s the shifts, the link between the macro and the micro as I mentioned before, the fact that it’s so tangible, it let’s people realize how quickly things
change, how quickly Vietnam becomes a much bigger player, all of those things are important. Because that suggests how things are changing across the world. And I think that makes the students in the classroom more interested in the World At Large, rather than just focussing on „what do I have to do“. It’s important for us in the classroom to get our students to understand that the world matters in the way in which it affects the local environment.
Niklas Vesely @ Kirchner: I have one more question to close it off. think a lot of other researchers will be watching this from other universities. In which case would you suggest them to work with Kirchner’s U.S. Import Trade Data, or where do you say it would not be that suitable?
Dean and Professor Ananth Iyer @ University at Buffalo: First, if you’re going to use the data at a very granular level, then I would say spend the time to clean up the data so that it works for the particular question you’re asking. And this is just because shipping manifests are not clean. And the shipper don’t take the time to clean things up. So you’ve got to check the zip code, the address, the state, etc. Because the shipper is not being clean in their data. So that’s something I would say: spend the time to clean up the data. By the same token if you’re not going to spend the time cleaning up the data, don’t use the data. Because then there is a big disconnect between the shipping volumes you will get at the state level and the actual volumes. They will not reconcile.
At the aggregate level everything is great, don’t worry about it. Everything is clean in terms of total flows. At the micro level, spend the time to clean up the data. The same is true with respect to the content. When you look at the value of the Imports rather than the quantity of the Imports, be a little careful because not all the imports valuations of the individual container give you a clear picture. You may need to do some work to look at the value part. These are all notes of caution, just because this is the way shippers ship and code goods. The worry I have in talking to industries who import a lot.. I asked them: „hey, you know I’m trying to look at your company’s Imports and there is a part I’m not sure about“. Certain companies ship to a distributor or a third party logistics center before it goes to the customer. So a lot of the shipments to Walmart you may not be able to reconcile. Because there are intermediaries in between, or they basically request that their data be cleaned out of the shipping manifest. These are little details. I would say „don’t use the data blindly, be aware of all of these things, and correct for them before using the data“. Otherwise this is a great way to see the world at work. And how that can be used within research is an open question. But that’s what I would say. For the detail spend the time to clean it up, and understand that the data is this way.
But for the aggregate, it’s a great way to understand what is going on. Also, check the conclusions that are being drawn. This is very important at the national level here in the U.S. Every month there’s a report that comes out, and there’s a lot that’s read into this report. It’s important to see: can we see whether the numbers we have in the data track the numbers that are there? You get to see where the gaps are. Finally, during Covid when there was this hold up in the shipping industry, or when the ship was stuck in the suez Canal, or even when there was a drought… It’s really about taking those events and seeing how they affect the trade flows. This is a great way for people to understand the impact of those shifts.
Niklas Vesely @ Kirchner: Ananth, thank you for those insights! Let’s dive deeper into how the data is being applied in research. Professor Vedantam, I’d love to hear about how your team and also the students are engaging with Kirchner’s US import data. But first I would like to introduce you to our listeners.
Professor Aditya Vedantam is associate Professor in the Department of Operations Management and Strategy at the University at Buffalo’s School of Management. Professor Vedantam specializes in sustainable operations, supply chain management, and decision sciences, with a research focus on the intersection of sustainability, economics, and operations strategy. He has published extensively on topics like green supply chains, circular economy models, and carbon footprint reduction, and has deep expertise in using big data and analytics to optimize global trade and supply chain networks. Professor Vedantam is also highly engaged with both academia and industry to explore data-driven approaches to supply chain sustainability and operational efficiency.
Professor Vedantam, can you walk us through the types of research or projects your team has conducted using Kirchner’s U.S. maritime import records?
Professor Aditya Vedantam @ University at Buffalo: Thank you Niklas, so there are two specific projects that we have been working on involving Kirchner’s import data.
The first project is around understanding the near-term sourcing potential for electric vehicle components. New York state where the University of Buffalo is situated has very ambitious targets to get to 100% electric vehicles sold in in the next couple of decades. This requires a a strong manufacturing base of electric vehicle components to encourage more electric vehicle manufacturing in the state.
We have been using Kirchner’s data to come up with a list of E.V.-components or electric vehicle parts that are being imported from from different countries around the world into New York State. What we have done is has come up with a list of electric vehicle components and their associated HS codes. Then we are looking at Kirchners data to identify the country of origin for that HS code, the volume of the EV component that’s being shipped, it’s value, and which of these components are ultimately destined for a location or a consignee address within New York State. So this project is aimed to help us understand the potential for near sourcing, which leads to the economic benefit that Dean Iyer mentioned, where you would have companies in New York state get potential tax benefits from manufacturing those components here rather than importing them, as well as create a more resilient and less risky supply chain – particularly for clean energy.
The second project we’re working on, also involving Kirchner’s import data, is related to a third party. A freight forwarder that is in Buffalo. This company essentially purchases capacity from many shipping companies / many carriers and sells this capacity to various customers. so this particular company was interested in understanding (or building a model to predict-) prices for these carriers. And typically these prices are revised every month or so.
The company currently has no other sources of data. We are attempting to merge Kirchner’s import data for three categories of products the company is interested in. Which is: Cashews, Apparel and Footwear. And for these product descriptions we are pulling out all the relevant maritime shipping records from different countries destined to any port in the US. This helps us understand the volume of product that’s being shipped, over time, at a monthly level. Then we merge that data with the pricing data that we are getting with the company in order to help build a prediction model for prices. This project hopefully helps the company understand more about what drives pricing fluctuations in sea cargo logistics and helps them make better decisions in anticipation of that.
Niklas Vesely @ Kirchner: This is a a great example of how you can take the broad data and go really niche to help a specific company. When working with Kirchner’s U.S. Import Data, did you get any insights- or uncover something that you did not expect? Something that surprised you?
Professor Aditya Vedantam @ University at Buffalo: So both of these projects are still ongoing. While we are working through them, they’re still in the preliminary stages. But we have uncovered a few interesting insights, particularly in the EV – electric vehicle component imports area. As I mentioned, we are trying to understand the near-shoring potential for electric vehicle components.
We are also trying to understand the risk for sourcing from multiple origins and countries. So one potential insight that came up with our preliminary analysis was that there were a high number of E.V.-components for which there is significant near shoring potential. We identified five HS codes corresponding to components like plastics, ironware, brackets, tires used in EV vehicles, and panels. And for these five HS codes, as we pulled the data from Kirchner, we found significant potential for manufacturing within New York State – also by value.
And other interesting aspects we’re trying to uncover is regarding potential for dual-sourcing or multi-sourcing. Many of these HS codes, or components, EV-components, are sourced from just a few countries. We are looking at possibilities where we could make policy recommendations in order to increase domestic manufacturing. To create more suppliers rather than solely relying on sourcing. So this was particularly interesting for us given the interest within New York State to create domestic manufacturing for EV components.
Niklas Vesely @ Kirchner: What does it look like from the perspective of a student? I think for an experienced researcher it’s quite tough to navigate this data set, but what is it like for a student?
Professor Aditya Vedantam @ University at Buffalo: We have a number of students who have been working on this data set for the last couple of months. And as Ananth said, these are shipping manifests. So there’s a lot of missing data and sometimes inconsistencies that students have to work with. Data pre-processing and cleaning is a very essential first step before conducting any research using the data set. The data set itself is semi-structured. It’s very large, with a lot of rows, and millions of transactions have been captured in there. One of the issues the students have been facing was the number of columns. For example in the 2024 data set, the number of columns is different from the data sets of the years 2022 and 2023. So having to reconcile that is important. Secondly, in all of these years, there are some columns where there are missing values. There are also columns where the value is n/a as well as zeros, and there are also blanks as I mentioned. For these, the students are trying to add the missing values based on some cross validation from other columns. For example, sometimes, let’s say the shipper’s name is present but the shipper’s address – or other shipper information – is missing (but this is only in a few rows), in which case the students are trying to see if we can gather that data from other rows where the data is present and sort of impute the data in some sense and validate the data accordingly. So these are some of the issues that our students are facing when working with Kirchner’s U.S. Import Records. But I think for the most part the data set is incredibly useful and gives a very comprehensive picture for our research.
Niklas Vesely @ Kirchner: I have a follow up question regarding how you work with the data. It’s a huge data set. You’ve got tens of millions of records, each with multiple columns. How do you navigate such a big data set?
Professor Aditya Vedantam @ University at Buffalo: The students and I, we have been working with different software programs to help us navigate this data set. We have been using Python and shell scripts for smaller workloads. For larger workloads we have been using Google Cloud. We upload the data and then use Python and shell scripts in order to pass the data. Sometimes we have to use some statistical methods in order to impute the missing values. In addition to that, we’re trying to use Google’s big query tool for pre-processing, cleaning, and analyzing the data. In addition, for the future, we hope to use a cloud address validation API. This API essentially helps with validating the address columns directly. As well as Pi-spark. This is also a Google cloud data processing API for complex data processing methods. In summary, a lot of software tools are being used in order to process the data. Especially since it’s so large.
Niklas Vesely @ Kirchner: My last question: how is the statistical significance of this data set? is it strong and reliable as the foundation for a research paper?
Professor Aditya Vedantam @ University at Buffalo: There are number of nice things about the data set. It’s at a very granular level. And we can pinpoint at the container level, the origin, destination, volumes… it’s because of its granularity that it helps in validating and providing statistically significant results.
But as mentioned earlier, we have to make sure that the data is clean before we do that. In some instances wherever the master bill of lading is missing, since that’s the primary key, we cannot extract the data from other sources. But wherever other data points are missing, for example the shipper name, the container type, the consigning name, and all of those information – if they’re missing, we can cross reference it from repeating instances and fill that up. Another column is the CIF value. That has also a lot of zero entries. So instead we now use TEUs to measure volumetric impact of imports. So from a research perspective, as long as the data is cleaned up and processed, then it is very incredibly valuable. And because of the granularity we are able to show statistical significance of our results.
Dean and Professor Ananth Iyer @ University at Buffalo: I just want to add, there have been a few papers published in our field in the top journals using data, not using Kirchner’s data but other data sets. And an interesting question is this question regarding supply chain resilience: When constraints get added, like tariffs, frictions or others, the system readjusts. And when it readjusts, you too look at the second order effect not just a first order effect. Just because you increase the tariffs doesn’t mean you’re going to collect that much tax. Because when you increase the tariffs everything readjusts.
How things adjust is really important to understand. That’s an interesting research question. Just if you look at trading partners for the US. When there was friction between the US and China, Mexico became a very large trading partner. And you said „what happened in Mexico?“ – Well, you can just set up a factory in Mexico and ship from Mexico. You just cross the Mexican border. So this is logical, and logical things happen fairly quickly. I think that is the part that is really interesting, which means that the world is a lot more fluid and it’s not stuck one way or another.
Things adjust very quickly. How quickly is really a metric that is worth understanding. So yeah, I agree with Aditya that as you clean it up, it’s a foundation for a lot of analysis. In our own school we’re setting up something called a data theatre where we would have screens. So my view is that we would take this data and feed it to this data theatre to have students see all of the flows coming from everywhere as well as how things change. So that when a faculty member teaches a class it becomes very visual for the students. Then they can be on their keyboards analysing the data, etc. We’re hoping to actually have it as a building block in the classroom. That’s our focus. It’s really the education part of it.
And I think that as we develop tools to allow this to happen, we would love it if you took these things and told the world about it. So they get the data from you. And we, we’re a university we have no commercial interest, we would love to tell people how they can use it in the classroom. That would be wonderful.
Niklas Vesely @ Kirchner: Thank you so much! This has been an incredibly insightful conversation. Professor Iyer and Professor Vedantam, thank you both for sharing your expertise and for giving us a deeper understanding of how large-scale trade data is shaping academic research. Hearing how Kirchner’s U.S. import records are being used to analyze supply chains, trade flows, and economic trends has been fascinating, and I hope it inspires more researchers to explore the potential of working with granular trade data.
For those watching, if you’re interested in learning more or accessing this dataset for your own research, feel free to reach out. Thank you again, Professor Iyer and Professor Vedantam, for your time and valuable insights!
Dean and Professor Ananth Iyer @ University at Buffalo: Thank you Niklas, it’s been wonderful working with you and we look forward to continued collaboration for the 2025 U.S. import data and beyond.