Automating Warehouse Inventory with Drones: An MIT SCM Capstone Project

Speaker A: Welcome to another episode of Supply Chain Frontiers, the MIT CTL podcast where we explore the trends, technologies and innovations shaping the future of supply chain management. I'm um, your host, Mackenzie Berry. In today's episode we're venturing into a cornerstone of the MIT Supply Chain Management Master's program which is the capstone project that students complete with the company every year. Here to join us are Tommaso Portalori, Camilo Mora and Elisa Ruiz.

Speaker B: Hi everyone. My name is Camilo Mora. I'm a uh, postdoctoral associate at ctl and uh, well I was invited to participate in this project a year ago. I joined uh, Dr. Jose Velazquez in um, co advising um, very talented students to develop this project aimed at better understanding the potential improvements in terms of reductions, uh, of scope two emissions by replacing for lifts with drones for you know, warehousing activities.

Speaker C: Hi, I'm Elisa Ruiz. I was part of the Supply uh Chain Master program last year, class of 2025 and when I was there I chose this project. This was my first choice as uh, a capstone project and it was given to me fortunately. So I had the opportunity to work with Tomaso Camilo, um, Dr. Josue and Dr. Miguel. And I was interested because I come from a background in warehouse operation and automations but I have never looked at it from a sustainability lens. So I thought it was very interesting to have that opportunity to know like the impacts it might or might not have ah, in sustainability.

Speaker D: Hello everybody. My name is Tommaso, uh, and I lead the sustainability efforts at Verity. Verity is a Zurich based uh, company with global operations that has developed a system of uh, AI powered drones that automate inventory in uh, warehouses. What was very interesting for us was the fantastic work that the Sustainable Supply Chain Lab has been doing in the last years within the MIT center for Transportation Logistics. This capstone was the first opportunity we had to actually try to put some hard numbers on the sustainability benefits that our solution enables in one of our clients warehouses.

Speaker A: Glad to have you on Tommaso. You spoke to it a bit, but I wonder if you could speak a bit more to um, what inspired Verity to work on this particular capstone project and what you all were hoping to learn from it.

Speaker D: Absolutely. We do have quite a strong operational story of uh, what are the benefits uh, that our solution brings in terms of um, accuracy, labor saving, uh, error scanning and so on. Things that are relatively easy to measure. But we're really lacking a rigorous and independent quantification on the environmental benefits across all the uh, three scope of green gas House agreement, greenhouse gas emission. And so what we're really looking for in this uh, project is to work with academic rigor and to partner up with our clients and with MIT and um, try to understand uh, better what are the uh, benefits that we are enabling for our clients.

Speaker A: And what drew you to focus on the environmental impact of warehouse automation as opposed to just focusing on operational efficiency over anything else?

Speaker D: I would say there are two aspects. The first one is the operational efficiency was already well proven so there was not much to discover there. And the second one that despite these two being often presented as a trade off, what we believe in is that whenever you're going in the direction of sustainability, you're also improving operational efficiency. Uh, quite interesting. Um, last year the Sustainable Supply Chain Lab also compiled a report that I think was presented in a previous episode. And uh, operational efficiency was named there as the number one near term decarbonization driver. And so for us it was really important to look quite an effective environmental impact, but also look at how they interact with the operational efficiency and try to eliminate at least this false meat of the trade off between these two components.

Speaker A: And Elisa, you spoke to how you were working on this as a capstone. You spoke about what drew you in terms of the environmental angle, but wonder if you could speak a bit more to what you were interested in about the project and maybe even um, how you were looking at it for your career at that point.

Speaker C: Yes. So as I mentioned before, I was working in with warehouse automation, um, before going to mit and it was always very focused on increasing capacity, taking more advantage of automation to improve efficiency throughput and very operational focused. But I have never ever was asked the question of what, like every effort we were doing at the time, how was it impacting the environment? Right. So when this project was presented to us, I thought that was very interesting also from the inventory management perspective, because inventory management, like when you think about warehouses and operations and the manufacturing operations, no one really cares about inventory management. So. So like most of the time it's very overlooked. And warehouse sustainability is another area that is also very much overlooked. So I thought about, okay, this is a good chance to kind of like bring the spotlight to those two areas and see how they together can create something interesting for the environment. So after working on this project I was not thinking about technology or the automation alone, but I was thinking about how information quality affects sustainability. So it's not only about how we are going to automate all the processes and now use all of these machines, but how those machines can transform the process in a way that it's affecting everything around them, including the environment.

Speaker A: And Camilo, can you speak to from a research perspective, why is it so important to quantify the sustainability impact of

Speaker B: drone based inventory systems from a research perspective? Not just about this project, but others. You know, it's very important to really assess with you know, uh, objective plans whether they are in this case greener or not. Right. Whether they are more, you know, efficient or not. And uh, with like, you know, again from an unbiased standpoint, provide you know, uh, evidence to determine whether this is true or not. Right. So now in terms of warehouse automation, it's uh, often evaluated mainly through um, an operational lens. Right. And uh, then the question is like, does this technology improve productivity or reduce labor requirements or I don't know, increase inventory accuracy? So again those are like important questions, but in this case the do not fully capture um, the sustainability implications of the technology. So if companies are going to claim in this case that um, autonomous drones can contribute to decarbonization or environmental performance, we need to move beyond intuition and quantify those um, effects rigorously. Then having a partner, in this case Verity, sponsoring this project to understand greenhouse gas emissions across scope, 1, 2 and 3, and then making more deeply focused on Scop2 emissions with the second project and build again something that was uh, not just evidence based, but I would say with scalable recommendations for other companies

Speaker A: in the future before we dive into the findings of the Capstone project. For those who may not be familiar with Verity's technology, Tommaso, uh, can you explain what autonomous drone based inventory automation actually does in a warehouse?

Speaker D: So Verity developed a system of a swarm of autonomous drones. So nobody um, is piloting these drones. They navigate autonomously the warehouse also High Bay Islands, uh, even in total darkness. And what they do, they go to the different locations and they scan the barcodes or they count the boxes and the items that are there and then they produce a report, uh, highlighting discrepancies, misplaced palettes, lost pallets and so on. And they're able to do this uh, with very high frequency. Much more that would be possible uh, without automation, just with a human based scanning. There is no physical infrastructure chained to the building. And this continuous update of the app to really allow to have a digital twin of the warehouse where you have uh, a very high visibility on what you have in stock or what you don't have. And what are the problems that they materialize, uh, before they materialize, at the worst, more impossible. That is usually when uh, you need the, the goods to be shipped, uh, to some customers.

Speaker A: And how does this differ from other forms of warehouse automation that we've seen? M and why is the focus on inventory counting specifically important?

Speaker D: So if you think of many automation solutions that we see, uh, nowadays, logistics fairs or around conveyor certification, ISRs or forklifts, it's mostly about moving goods. Uh, for us, what we really wanted to focus uh, on was the data visibility layer or uh, as uh, ELISA said before, is the information quality. And so we don't lift anything, but we generate evidence. We provide visibility to our customers where their goods are and how they can intervene, uh, to reduce inaccuracies. And once you operate at this level, the ripple effect of increasing, uh, by 1%, by 2% or by 5%, your uh, inventory accuracy really has uh, a multiplying effect then on your uh, picking activities, on your shipping activities and so on. I think this is why it was so important with this project, uh, to really look at the impact on the entire value chain and across all emissions groups.

Speaker A: And Elisa, you were speaking to this earlier. Why do you think inventory, uh, management is under represented or under prioritized in the space?

Speaker C: I think because most of the times it's only treated as a metric, as a KPI, you do your cycle counts, you say, oh, we were supposed to have 100 items, I only found 99. Let's write off one item and that's it. Right. But no one ever really gives attention to all the consequences that one lost item can have downstream and upstream as well. So it's not often seen as a process that adds value when we're talking about a, um, whole manufacturing process. So it's often not prioritized.

Speaker A: Pivoting now into the findings from the Capstone project, Yara's research found a 49.5% reduction in total emissions from drone implementation, which is quite significant. Can you all walk us through where those emissions reductions actually come from and what surprised you most?

Speaker C: Yes. So this is actually a funny story because when we came into the project, we thought it was going to be something very straightforward, very easy. You are changing a process that used to be done by people, now it's going to be done by drones. And you think, okay, energy is going to change, the process is going to change, we're no longer going to have people and that's going to be our emission savings. Easy, right? Uh, but then we found out that not only the equipment itself, okay, drones, if you compare a drone with a forklift, it's Tiny and also the energy it consumes or the energy source differs, it comes from a battery, whereas forklifts can use fuel or also batteries and energy as well. So the change in terms of, in regards to equipment, it was not very significant. Like the Trump footprint, footprint was tiny, but also the um, forklifts as uh, a whole is not very polluting either. And that was surprising. We thought that a forklift because of all the components and manufacturing process and its size was going to be more significant. But not only that, when we were digging more into how the operations were done in the warehouses, uh, we talked to people, we interviewed people, uh, the actual operators that were doing um, the job. We realized that the process didn't fully shift from still needing manual operations. So it was not like I uh, used to have people doing this and now I got rid of everyone and I'm only using drones. People were still needed and they were still performing things like looking for items that were lost or accounting for discrepancies, for example. So then when we noticed that it was not okay, I got rid of four forklifts and instead I'm using three drones. And then I can say those were my savings. We had to dig deeper because we didn't have results. Right. So it's like, okay, now where are these savings actually coming from? What the drone change. Right. Um, so by this time our whole capstone, um, kind of like took a torn because then the technology was no longer the story as we thought in the beginning. But we realized that the operational changes, uh, enabled by the technology were actually the story. Uh, so in the end what we found, like this 49.5% did come from energy, um, forklift energy. There was a lot of savings there because drones, they require less energy and they're more efficient. Uh, it also came from labor, commuting and waste. And that was our more most, I think shocking finding that waste was a big contributor because of what I mentioned earlier, of all of these efforts being done into like finding an item that was lost and then when the item is not found, it has to be considered waste because of all of the upstream and downstream emissions that it comes with losing an item.

Speaker A: Absolutely. So really looking at the picture, the operations as a whole, as opposed to just isolating each part. And one of your key findings is that inventory write offs or scope three ways account for about 40% of post implementation emissions. Um, you spoke to it, um, but why, why is waste reduction such a powerful lever when you're looking at warehouse sustainability?

Speaker C: Yes. So if you think about Your everyday products, where are they coming from? They needed raw materials to make it a manufacturing process and a ah, shipping process, transportation to get to where they need to be. Right. So the product is already done, is sitting at a warehouse and then it gets lost. So that product is now unaccounted for and those embedded emissions are now wasted. So it's not only the disposal, if it's later found that's generating waste and emissions, but it's all of those things that were done beforehand to get the product to where it was that now it's wasted as well. Also often the company still needs that product. So you're trying to find your product either to ship it somewhere else or to use it for another manufacturing process and it's not there and it needs to get replaced because you still need that product. So another cycle begins, you need to produce that product again and there comes more emissions. So waste, when you lose a product is not only disposal, but is replacement and all of the emissions that came with the product for it to even exist. And that's what makes waste a big, big player into all of these emissions contribution.

Speaker A: And you mentioned employee commuting, uh, and that emerges as the largest emissions contributor post implementation at 37%. Most people think about energy use first as you mentioned. What does that suggest about warehouse sustainability strategies overall when you're zooming out, looking big picture?

Speaker C: Yeah, so that could be a little misleading if it is not in interpreted correctly. It became in largest after implementation doesn't mean that it got worse, it only means that everything else got better. So because the system improved and commute is or people, it plateaus. Right. You reach a point where you can no longer not use people in your process because you still need to do all of these manual things to go find some items that were lost. For example analyzing data, improving operations, you need people for that. So as the system continues to improve, inventory accuracy improves, data improves, then you still have people and that becomes now the biggest contributor post implementation. So uh, the lesson is not that commuting gets worse, for example, but it's that sustainability priorities shift as the operations improve and also the use for the workforce. Right. So before you had people doing all this manual work, riding the forklifts, counting manually item by item, and now you have them doing other tasks that probably could add more value.

Speaker A: So that's a good problem to have that everything else was improved so much that it appears larger than it is. And you all use the GHG protocol framework to measure scope one, two and three emissions. Um, we've talked about that in previous episodes of the podcast. Uh, but for you all, why was it important to look across all three scopes? And what did taking that broader view reveal that maybe a narrower focus might have missed?

Speaker D: Sure.

Speaker B: The first study looked broadly at uh, greenhouse gas emissions across as you said scope one, two and three and um, showed that drone based inventory automation can reduce emissions right through several channels including uh, as previously described, uh, including lower forklift use and reduce inventory write offs amongst others. And then in the second study then we focused more on the uh, scope two emissions and uh, yeah we built simulation framework to compare drone based um, operations and forklift based scanning, you know, across different warehouses, uh, size regions, you know, lagging assumptions and electricity grade factors. So I would say that to summarize what we did was like of course use um, in this case the protocol. You know, that's something that is standardized, validated across industry and also for research first to look at something more broadly, you know, about what was going on to better understand, you know, the conditions under which uh, these uh, drones work. And then once we identified something more like an opportunity then we deep dived into the scope 2 emissions, you know, and in particular for you know, these uh, inventory scanning activities.

Speaker A: Today's episode is brought to you by the MIT Supply Chain Management SEM Master's program. Through capstone projects, companies can work with experienced MIT SEM students and faculty to tackle high impact supply chain challenges. In fewer than 10 months, your team gains practical solutions, strategic insights and direct access to top talent, all through a project tailored to your business needs. Each student brings multiple years of real world supply chain experience with backgrounds spanning procurement, logistics, operations and more. Learn more at SCM MIT Edu or reach out at SCM capstoneit. Can you all talk about one methodological challenge you face taking us behind the scenes in the research process?

Speaker B: Sure. For any model it's crucial that you uh, give it good inputs. Right. So just to you know, briefly again describe the kind of inputs for these uh, simulation we consider different uh, warehouse characteristics. So imagine small, medium, large warehouses, you know, the amount of the percentage of the area that was like allocated for like pallet storage, number of shifts as well hours and uh, well for drone inputs, you know, the number of drones, um, you know, depending on the type of facility and the different uh, activities. We were very granular. I mean we're capturing you know, the speed of the drone, uh, the time that it to charge also uh, when they will, you know, whether with a power scan or more like in a Determined area, the flight duration, among others. So I would say that for this and any other simulation or modeling, the inputs are crucial. And uh, with our partners, in this case with Verity, they did this job very, very well, you know, in providing ah, the information timely, um, and precise such that we could like of course believe in the results, trusting them and taking it.

Speaker A: Now from looking at the simulated results and what the project revealed to real life implementation. In the capstone, you modeled scenarios where drone coverage increased from 64% to 90% to 100%. Can you talk about in practice, what are the actual barriers to reaching that 90% goal? What's holding warehouses back? Why are we not looking at 100% drone implementation right across?

Speaker D: I think there are different aspects that speaks um, to this and different adoption barriers. So first of all, if you think of a warehouse of third party logistic players, you might have different uh, clients, uh, of our client that warehouse. And so maybe they have different operations or they have different willingness to automate. And so this must be considered that uh, the solution maybe is not applied to the entire warehouse. Another uh, topic is also the technological limitation. So the drones can uh, scan what they see. So our ideal use case scenario is uh, a ah, full pallet location. So you have one pallet per locations. Because if you happen, you have, if you happen to have deep pallets, of course the drones cannot see through. So I think it's a combination of technological limitations, business process change, cultural change, and also where the clients that are trained at warehouse and what their desires are.

Speaker A: You found that about 19% of warehouse locations can never be reached by drones due uh, to layout constraints. How does that shape implementation strategy as well?

Speaker D: Yeah, so layout and hardware, they really set the ceiling of what uh, you can do in the warehouse. And of course our technologies, we always work to develop uh, and make uh, our technology better. And so this a way in which we try to extend uh, this uh, capability to different type of warehouses. Because sometimes we use the word warehouse, but uh, when you enter into a single warehouse and you go to another one, you really realize how important it is to have size specific uh, calibration in this respect.

Speaker A: The model that you all use shows that drone implementation actually reduces the number of personnel needed for inventory counting. But the overall workforce doesn't necessarily shrink, but it shifts. We're seeing that a lot with the impacts that AI is having across the board for different roles, uh, what kinds of roles are emerging and how are warehouses managing the transition for sure align with AI.

Speaker D: This is a physical AI solution. In the end, so it goes in the same direction. And one number that I want to mention is that in the US last year the turnover of warehouse workers was 43%. And so automation solution for uh, companies, uh, working in warehousing, it's a way to address labor shortage rather than reducing a laying off personnel. And also another point to consider is that I've been around quite a few warehouses and I've never met uh, a person who was uh, passionate about uh, checking inventory. Uh, checking inventory. It's a dull task, it's a repetitive task, it's a dangerous task because sometimes you do that at height and ideally nobody should be doing that. What the technology really helps you to do is that you can shift the work of people that uh, do a task that really is a high risk of automation and they learn digital skills. For instance, on how you ask a system of drones to the inventory for you, and now you check the output of these drones and you free up hours to do other tasks or by checking other parts of the warehouse that the drones cannot reach or doing different tasks on the control side. And so this is really the type of change that uh, uh, we try to build together with our customers.

Speaker A: In the capstone, you ran several what if scenarios, including one where inventory accuracy did not improve with drones. Is there a point where drone implementation stops making environmental sense?

Speaker C: There could be. All of these were hypothetical scenarios. As you said, what if, uh, so after we got our final 49.5 results, uh, we were challenged to test the assumption that automation always helps. So we were asking what if companies deploy drones but expected improvements never materialize? What would happen then? So you reach a point where you have the equipment, um, doing all the operations for you, you already have the energy savings and all of the other savings you could have. But if your inventory accuracy doesn't improve and if your material losses the other way, if they start increasing, there comes to a point where that lost, uh, and waste that we were talking before offsets every other benefit. So that is a hypothetical scenario, but it could happen. And it's only to highlight the importance of, as we also mentioned before, the quality of the information. So a system is only as good as the outcome it produces. So you can have all the technology, all the process, but if it's not making um, your operations better, more efficient and improving the actual outcome, then you may not have all of these sustainability gains that you could expect.

Speaker A: Practically looking ahead based on the project, if a warehouse operator came to you and asked, should we invest in a drone based Inventory automation. What questions might you ask them before giving your answer?

Speaker D: I think it first um, of all depends on how much of their footprint is drone reachable. So the fit with the technology that uh, was mentioning before, it also really try to understand in their operations where uh, the pain points are. So is it about the shrinkage at the write off which is uh, probably a bigger problem from manufacturers that it is for third party logistics because they produce the goods that they lose so they even bear higher economic consequences. Or if it's with uh, the partial deliveries or when they need to deliver the product, they cannot find it. And then I think it's very important to ask them if they are measuring this data so which KPIs they want to change. And of course uh, drone solution is something that uh, works very well when you have warehouse with very high ceilings. And so again dealing idea that not all warehouses are the same. And so it's really very important to look at all these elements together.

Speaker A: And you recommend treating drone coverage as a master lever for emissions optimization. Can you talk about what that means operationally and how leaders should think about phasing implementation?

Speaker C: So usually and more nowadays companies and leadership might look at automation, AI and think the more the better. So I can say if I am fully automated, that means that I'm going to get all the possible benefits. And that is not always true. So what we were trying to say is that more coverage isn't just about how much you have automated, but also that it influences multiple process simultaneously. So we saw that it's kind of like a chain reaction, right? Drones, um, they have better issue rate or the issue rate got better when drones were counting the inventory rather than humans because they're more precise, um, they do the counts more frequently than humans could possibly do. So they are better at catching errors. Right? So because they were able to cover more locations than the accuracy got better, the issue rate decreased, uh, there were fewer issues, fewer write offs, therefore lower emissions. Uh, from an implementation perspective this means that leaders shouldn't view coverage as a simple technology metric, but more they should think about it as a strategic variable that is going to shape the overall performance of a warehouse. So the more the drone covers that means the better results you're gonna get.

Speaker A: Beyond drones, what other practical recommendations would you make to companies looking to reduce scope 3 emissions?

Speaker B: I would say that the most important part of this kind of projects is to allow managers and people to look at other types of technologies to be open to try odd things, right, that could in this case cut carbon Emissions. So uh, essentially when you talk to some practitioners, they have certain ideas on how to do things, how to mitigate carbon emissions, how to follow what others are doing across your sectors or industries. But in this case I would say that one of the most uh, important parts and one of the most uh, things that I like the most about this project is innovation. Right? So imagine this is very futuristic, right? Like man. Now instead of forklifts using drones to scan inventory. And guess what? After doing this and the simulation, these yields these significant reductions. That's amazing. Maybe this is an opportunity to think outside box and try other things. And instead of an A B testing, well I invite, if you are listening to this and you uh, want to try this, well, uh, reach out to us and maybe we can conduct research together to really understand whether these um, solutions are good or not. So that's, I uh, would say um, in terms of solutions the way that I will recommend uh, people to um, try in their industries.

Speaker A: And Tommaso, from Verity's perspective, how are customers actually using the sustainability data? Is it driving purchasing decisions or is it considered a nice to have?

Speaker D: So the honest answer to that is that sustainability is uh, increasingly part of the conversation, but it's not yet the sole driver. Of course there are some um, nuances, there are some differences between uh, Europe and the U.S. uh, within Europe we see that this is particularly important for some clients in the Nordics. Um, for instance, uh, one of the clients we partnered with for the second study was COP2 emission. They run a sort of uh, internal carbon pricing where they charge their own countries based on their uh, emissions. And so for them when they see a reduction in CO2 emission enabled by automation, solution is much easier to attach an economic value to that than it might be for other clients. In some cases it's easier, in some cases less easy. But uh, there are two other things I want to mention and I think um, we are really supporting uh, company in the right direction. And the MIT report from last year on sustainability, supply chain sustainability, they noticed that 70% of firms, they send the lack of supplier data and especially Scope three reporting as a major limitation. And most of them still rely on uh, spreadsheet or some various karma accounting tools. And so the additional uh, benefit uh, of solution like ours on top of the reduction is that you have that evidence and visibility layer once again where you get audit rule and timestamped activity data on your inventory, what you lost and so on. And this is also sort of a data equalizer, um, opportunity because even a smaller uh, company can then report and trace, uh, their operations and their stock at the same level of the global corporate. And so this is also the contribution that we're trying to make.

Speaker A: Elisa, you conducted interviews with warehouse teams to validate assumptions during the capstone project. For you, what was the most important insight that you gained from talking to people actually doing the work that the data alone may not have told you?

Speaker C: Yeah, that was a very big part because as Camilo said before, um, one of the biggest challenges is representing reality correctly. Like we can make a thousand assumptions. And then you go and talk to the people who's actually, they're using the technology and doing the work and they paint a completely different picture. So, um, it was very interesting learning, um, how the actual operation worked, how they had to go and find every single missing item. Every time they find a discrepancy. They had like this whole process of like a team of people going location by location, trying to tracking the system, where was it last, where could it be next? And then it's a whole chain reaction of manual efforts that require equipment, people, ah, energy and time. It's very time consuming. So that was very, um, not surprising. But that really helped us shape how we were going to do this methodology and how we were going to represent that effort into numbers and eventually into emissions. Right. Um, another important thing was that it was very good to know how the operations and processes, they all depend on the warehouse, the consumer, the materials, the regulations. Like we interviewed two different warehouses in two different locations and they operated in a different way, not completely different way, but they had different customers and that brought different requirements. Some of them had contract that required the materials to be in a specific aisle that could not be accessed by drones. The other one didn't have that. Also some were, um, storing their items in cases, other in pallets, other in units. So you need to know all the specifics to be able to try to quantify this kind of thing. Um, so talking to the people, that's always very, very important.

Speaker A: Looking to the future of the work. So Tommaso, this capstone was published last year and Verity decided to expand and do another capstone this year. Why did you all decide to do a second project and what did you change this time or what more are you looking to find?

Speaker D: In this first project we were looking, uh, at emissions across all three scopes. So it was a bit larger in terms of the, uh, type of emissions that we were considered. Whereas the second project focused uh, mostly on scope two emissions. But at the same time the first project was really looking at a single warehouse with a snapshot of a real case study. Whereas in the second project we moved from a single warehouse to a portfolio portfolio overhauls. And what we did was more a simulation model that could drive decision making for implementation. And so I think um, you know, they probably answered two different questions in one case. Can we quantify in a real case where we implemented the drones footwear, the benefits. And second, uh, was more. Okay, we want to implement the drone in a, ah, portfolio warehouses. Which ones should we prioritize? Which counters need this first? Where can we maximize the impact on the portfolio of clients? Uh, by prioritizing certain warehouse type or whereas certain countries with different carbon emissions. So we were answering different questions, different needs of the clients that were involved in this project.

Speaker A: Could you all speak to the benefits for all the stakeholders involved in a capstone project and perhaps distinguish how it's distinct from say a consultant firm?

Speaker B: So I think that there are uh, various stakeholders that benefit from this kind of project. Of course students, you know, because they gain hands on experience by working on you know, these real world problems with industry stakeholders and as a consequence they develop technical analytical, communication, project management skills. Right. While you know, um, also helping like these students to translate the analysis into actionable recommendations. In addition, of course companies sponsored companies because they obtain rigorous analysis on their uh, problems. Right. And uh, this usually results in uh, developing practical tools, models, dashboards, frameworks, so on and so forth. Um, and uh, certainly for, for us as researchers, well it's an opportunity to bridge the gap between theory and practice well, you know, while producing insights that become publishable, you know, with papers and of course practically relevant that we can disseminate to you know, improve the conditions of society. We are doing this because we want a better world. Right. So in terms of sustainability, well this is a, this is the case of this company that is interested in doing this and that and by doing greater research with um, the most talented students in the world now we can show the results create impact beyond a single company.

Speaker A: As Verity is doing a second capstone project this year, could you all speak to some of the people involved in working on the project?

Speaker B: For sure. I would say that uh, the work that uh, our two talent students conducted was um, amazing. You know, I'm referring to Liam Fan and Srivets Agarwal. They did an amazing job, extremely committed to the project. And uh, yeah, again kudos to this uh, work. I would say that uh, in this case the sponsors, you know, uh, were very happy with the deliverables and uh, they built that, I don't know, a communication, um, strategy and environment such that they were, you know, our sponsor, willing also to share many things that, as Elisa also mentioned before, you know, it's important to model things that are real and that are practical, that they are useful. Right. So from the very beginning of the project, they were, I um, would say committed to that. And yeah, so to them and of course to um, Dr. Jose Velazquez, who I would say was the mastermind, you know, behind all of these two projects, you know, connecting also with Tomaso, positioning the. The different, uh, studies and again to create impact.

Speaker D: So yeah, I would say this speaks also to the benefits. Right, because it's uh, a sponsor of the capstone. What we bring in is, uh, our time and our commitment. But also we bring in, uh, uh, operational data from uh, actual warehouses, uh, that we make available, that our clients make available that would be, uh, otherwise difficult to access. Uh, but we also gain access, uh, to incredible talent. We were extremely lucky last year with Elisa and Philip for the case study on single Warehouse, and this year with Liam and Shimas for the simulation that we did in collaboration with uh, dsv. Um, and then of course it's very different from uh, commission, uh, consultancy, uh, setting because of course there is a greater risk here. I mean we were very lucky that we very much liked the results of both the capstone. But that's not granted because uh, the additional benefit that you get is that you get a stamp on these results from uh, an academic institution that doesn't compromise on anything. And so this guarantee for us, a guarantee for our clients that the numbers that uh, we're getting, of course always with assumption, with all the limits that uh, uh, comes with any data analysis that are acknowledged, but really indicates a strong direction that uh, you cannot getting from uh, any other source.

Speaker C: I would say I can add something from the student perspective. Um, I think it is very valuable because of all of the reasons Camilo and Tommaso mentioned. Uh, but also there is a big partnership between the researchers, the sponsor company and the students. Working this together, you can really feel how everyone is really invested. Uh, they all are expecting and working towards the same goal and really the best outcome possible. Um, in this case the project was mostly aimed towards sustainability. But for example, because we were also working with automation solutions, we partnered with Dr. Miguel, who specializes in automation. And I think that helped us kind of like have a team that is really an expert in sustainability efforts, but also in automation. And you have the company that brings all the, uh, technology knowledge, solution knowledge, and you have the students that are going to help put their own perspective with all the things they're learning, like modeling methods and AI coding. So it's really a great team. For all the capstones, not only my experience, but for all I seen with my, um, the rest of the cohort, everyone had a great experience working with the researchers and their sponsored company.

Speaker A: That wraps up this episode of Supply Chain Frontiers. A big thank you to Tommaso Portalori, Camilo Mora and Elisa Ruiz for joining us. I want to credit Philip Cook for his work with Alisa ruiz on the 2025 Capstone project as well, which you can read in full under our publications and@CTO MIT.edu. supply Chain Frontiers is recorded on the MIT campus in Cambridge, Massachusetts. Our sound editors are Dave Lashansky and Danielle Simpson at David Benjamin Sound, and our audio engineer is Kurt Schneider of MIT Audio Visual Services. Our producer is myself, Mackenzie Berry. Be sure to check out previous episodes of Supply Chain Frontiers at CTE Podcast or search for us on your preferred podcast platform. I'm, um, Mackenzie Berry. Thanks for listening and we'll catch you next time on Supply Chain Frontiers.