 Welcome to MIT Supply Chain Frontiers from the MIT Center for Transportation and Logistics. Each episode features center researchers and staff or experts from the field for in-depth conversations about business, education, and beyond. Thank you for joining today's MIT CTL Supply Chain Frontiers podcast. So happy to have Mike Bucci and Milena Janjevic. Milena is a research scientist here at MIT CTL, and Mike, can you tell us a little bit about yourself and your role? Yeah, sure thing. Mike Bucci, I worked for Coupa, formerly LamaSoft, as a few years back, been largely in the professional services group during that time, mostly working with companies around the world in all industries, kind of helped them understand how to leverage the Coupa software to solve their supply chain design problems. So I've been fortunate to work with many companies around the globe and look forward to sharing some of that experiences here today. Excellent. And Milena, can you tell us a little bit about your work and how it relates to supply chain design and the work that Mike is doing? Yes. Thanks for having me here. So I am leading the supply chain design initiative, which is basically looking at the ways in which we can improve the decision making process around supply chain design. So here at MIT CTL, we do a variety of industry-sponsored research and educational programs with the aim of helping companies learn how to better address their supply chain design problems. Great, great, great. So for today, we're going to be talking a little bit about sort of old world or past use supply chain design and what some of your research and some what new practices are that you're doing in the supply chain design space. So you recently co-published a white paper called the New Competitive Edge Analytics Driven Supply Chain Design. And in the white paper, you mentioned that most companies are still using standard supply chain design practices that have been in place for around 30 years. What is the traditional supply chain design approach? From the Coupa side, I mean really what we've seen over the years, and there has been a transition, but yeah, the traditional approach and practice here for supply chain design has really been around more event-based or episodic kind of processes that occur at some schedule, but usually less frequently, every year to two to three years. And the idea behind that is really to reassess a significant supply chain problem, strategic supply chain problem. It could be a DC network design problem, it could be production capacity planning problem, but it's more of that event-based project or process. And there's really a very significant heavy lift to go find the data, collect the data, cleanse the data, get organizational buy-in and process, provide recommendations and then go implement the solution. And so that's kind of a process that reoccurs, but it's kind of again this more of an event-based problem. And the focus there is typically on minimizing cost. It includes obviously balancing service and inventory, but it's just that general problem, but again it's more of that event-based activity. Does the periodicity or the time in between updates affect the difficulty of you to get the data in a situation like that or to get accurate information? So I'd say that when you are redesigning your supply chains every five years or 10 years, the level of granularity that you will be able to incorporate in those supply chain studies, it's typically very low. So typically companies will aggregate data at a very higher level and that will lead to all kinds of approximations that will ultimately result in a big gap between the expected performance of your supply chain and then the realized performance wants to implement a new design. And some of that has resulted because of simply the computational power that was limited in the past. But now we see that the data availability has significantly increased, but the supply chain design practices have not necessarily caught up with that. Right. Anything else to add on that point, like as far as the length of time between? Yeah, I think as Molina said, I mean historically we've been somewhat constrained by computational power and other things. Certainly looking forward at what we see in the future or what we see coming into play now is kind of that more frequent analysis study, which brings up a whole bunch of pros and cons related to that, but we can talk through that. Yeah. Well, I mean, let's talk about that. So before we get to the pros, let's imagine that it's 1995 and we're doing every five years, we're doing a supply chain design. The cons to that that I heard are that you're just going to have highest level data, you're not going to have any granularity. Are there any other cons to that length in between? So one big industry trend that we see is basically a much higher pace of change and that's going to be both on the demand side and on the supply side. Companies are facing increasing customer demands and need to re-adapt the supply chains in much more frequent matter to basically cater to those increasing demands and that requires to have a much higher speed of adaptation of their supply chain designs. Yeah. I mean, thinking back, as Molina said, if we go back decades here, the computational complexity or the challenges we had for computational horsepower as well as data collection, because systems were dispersed, uncentralized, unstructured in many ways to try to consolidate the data and effectively leverage it for these kind of studies, that was largely the challenges. So today we obviously have a significant difference where, as Molina said, supply chains are much more diverse, both from a supply and demand perspective, there's more continual change and the frequency of adapting a supply chain is required to be much more frequent than in the past and all that requires, of course, then the infrastructure around that. So I think the newer landscape is that we not only have a computational capability, we also have the system infrastructures to be able to pull the data more frequently and more efficiently to kind of do these studies more frequently and therefore leverage those results, obviously, and react to them much more quickly than we could in the past. Great. And this is going to lead to the conversation about supply chain design. Just before that, you mentioned consumer demand. So people want things now. They want things in the format. They want them on their front door. They want them in a box somewhere or maybe they want them delivered to their summer home or whatever it is. Is there a B2B demand that's changing? I think is one question that I have. And then what are some of the other trends that are pushing towards this more tight timeframe and advanced supply chain design models? So I would say that customer sensitivity has become a key element and a key source of competitive advantage and both in the B2C and B2B realm. And that's, as Mike mentioned, that's not something that is typically covered by the traditional supply chain design methods, which are really focused on this cost minimization. And so whether it's in the B2C or B2B space, we see that delivery lead time and delivery reliability are becoming key order winners and cannot be ignored from supply chain design studies. And from the KUPA site, are you seeing people changing their sourcing models or is there anything happening with globalization? Or does that also influence this process? Yeah, I would say absolutely. I mean, we all know that supply chains have been diversified and global now in nature. And as we've seen, obviously in the pandemic and other things along the last several years, one disruption along that supply chain can have a significant impact on your entire system. And so all of these questions now that were maybe in the past more long-term questions that we could answer once and not have to revisit them frequently. Now there are questions that we need to revisit often because of all the things Melina described, customer behavior as well as on the supply chain, all the changes that are occurring on the supply side. I would also say that the need to reassess these strategic decisions more frequently leads kind of a shrinkage between the strategic and tactical level decisions that are occurring in companies. And so now you're making strategic decisions more frequently, maybe quarterly, and you're making tactical decisions maybe monthly or quarterly. And now there's kind of a connection there that we were trying to capture. And that's one thing we see a lot with the companies that we're working with is that there's a sniff and overlap between those kinds of questions and those time horizons where typically were separate kind of questions that were asked. I think we're understanding the challenge now. We know where the market is sitting. We know where business players are sitting and what they're up against. And so in the white paper you mentioned that there are four opportunities, primary opportunities that companies and managers can take to reimagine the design of their supply chains. And they are extending the scope, incorporating the tactical, accounting for risk and planning for resilience, and adopting new technologies and business models. So I'd love to get a couple minutes in on each of these to find out more about what they mean and maybe what they mean on the ground and what you've been seeing with people that you've been working with. So let's start with extending the scope. What do you mean by extending the scope? Yeah, so the first and most immediate opportunity that we see here is extending the objectives that we are considering in our supply chain design. So we've already mentioned that traditional methods focused on cost minimization on physical structure of a supply chain. And that's a very limiting way of considering supply chains and supply chain design in the contemporary markets. And so supply chain design should have a much broader scope, which basically focuses on long-term value creation and basically consider the interactions that happen between the choices we make in the supply chain design and our ability to generate value, increase market share and generate revenue. So for example, one key question in supply chain design that we have been answering for the past 30 years is, where should I look at my facilities? How many of those facilities should I have? And if you have purely a cost-based perspective, the answer is probably going to be to centralize as much as you can and to gain some economies of scale, have centralized inventory with lower cost. But if you consider this idea of customer centricity and the fact that the market share that you can capture will depend on your ability to actually serve your clients in a reliable and fast fashion, then the answer is going to be completely different. I do not see that a lot of companies are currently using approaches that fully account for that aspect of value creation. Yeah, I think Molina covered a lot of the items I would mention. I would also say that as far as extending the scope, historically, as Molina outlined, if we're looking at a supply chain problem, for example, looking at a DC distribution network study, we'll put in the distribution network. Some of the customers may be a little bit on the sourcing, but now we have the ability to go further back. Maybe we can pick up some more supplier detail. We may even include options of supplier direct shipments, other ways that we can extend the breadth of the scope of the supply chain problem. That's one thing. Then, of course, adding in other components or costs or considerations to the model as well. Molina obviously discussed the idea of being closer to the customer, the impact on how that can have on demand. We, of course, can incorporate things like taxes and duties and other new government regulations as well, as far as understanding how our supply chain design should be. Sustainability is another component as well. Companies are more concerned with their CO2 or total emissions and other components. We can incorporate that as well into the problem and evaluate that in conjunction with cost, et cetera. That's a little bit about extending the scope and broadening what you traditionally would consider as a supply chain design problem. Tell us a little bit more about what you mean by incorporating the tactical. I'll start here. I think incorporating the tactical really is, again, this combined or shrinking of the difference between the strategic and tactical decisions. In all the work that we do with our clients and customers that we work with, we see when we try to address both the strategic question and the tactical questions, we estimate that 75% of the data requirements are the same. Why not take advantage and get a multiplier of that effort to solve both kinds of problems and have that same data foundation where there's not discrepancies between the tactical decision-making process and the strategic process? We see a lot of convergence there of incorporating tactical-level detail into our strategic models or overlapping the two decision-making processes. In examples of that would be things like an SNOP process, short-term, rough-cut capacity planning, replenishment inventory planning policies. How can we address those more in our supply chain design problem optimizations? One thing that I would add to that is that from the modeling perspective and data collection perspective, the incorporation of these tactical decisions will often require a certain effort. The idea is not that every company should incorporate each type of tactical decision with the same level of granularity, but really identify those areas that are key for their value creation. If I am competing in the last mile space, I probably need to have a much more granular and precise integration of my routing decisions and my inventory decisions in the last mile than if I am a manufacturer that is mainly going to be looking at production planning decisions as a key driver of their advantage. This idea that we can have a single model and a single approach that would fit all different industry context and companies is now outdated. Excellent. I like that. I like that. Not only is it tactical, but the tacticalness is going to depend on where you are in the supply chain, what your business is, that sort of thing. The next of the four opportunities that you recognize in the white paper is to account for risk and plan for resilience. I think this is on everybody's mind after the last few years. Can you tell us more about accounting for risk and planning for resilience? I think the first step here is for companies to basically map out the different sources of risk, the different sources of vulnerability, and to characterize those. There we need to recognize that when you talk about risk, there are different categories of risk that will be accounted for in a very different manner in our supply chain design. If I'm talking about natural demand, the elasticity that just comes from some variations in the market, that's very different than a risk linked to a major natural disaster or pandemic. The way I will account for that in my supply chain design and in my tools should be very different. I think that at this point, companies are very much aware of the requirement to incorporate the risk, but they don't necessarily have the right tools to employ and address each type of risk today in my face. Yeah, to add to what Malina said, I mean, what we're seeing is historically, companies will run scenarios for their supply chain design or tactical problems, and they'll do a few sensitivities of, well, what if transportation costs go up or down 5%, but we're seeing obviously those sensitivities increase significantly, a 2-3x increase in number of scenarios companies are running today, partially due to this risk-related problem. So the first thing we see is just running more scenarios to look at the resiliency of your network to different changes. They could be maybe their cost changes or they could be changes to constraints. I have a certain port volume that I'm expecting to move through. What if that port volume were to decrease by 50%? How would my supply chain react and what is the impact of that on my network? A second theory to that as well is we're now able to pull into some of these models risk scores and risk metrics from external sources. And so by pulling that in, in some ways, we can kind of put that into the objective function where we're trying to minimize certain amount of risk in our supply chain, or at least report that out as far as certain nodes. Maybe we're flowing 95% of our volume through a certain node. We wanted to highlight that as a potential risk because we were kind of single sourced or one single node kind of constrained if there was a risk event related to that node. Also, again, based on those external risk metrics, we can provide some risk score for our supply chain design based upon a supplier's risk, based on different transportation risks, et cetera. And so we're seeing that more and more in kind of the models that we're running and the requirements our customers are having regarding risk and resilience. I think what's fascinating about this then is it kind of leads us directly into the fourth opportunity because I'm sensing that people are maybe collaborating more or maybe they're getting data from resources that didn't exist before that are public or that are able to be purchased. And maybe there are new ways of analyzing and new technology. So can you tell us about the fourth opportunity to adopt new technologies and business models? Yeah, I'll start here. So for what we're seeing and you're right on there, Arthur, I mean, as far as what we're seeing is again, risk is one good example. We're able to maybe incorporate external data more, consider that or at least define that in our solutions. I would say a couple of things here in play. Of course, as you mentioned, it's just big data, right? There's a lot more data we can capture and bring into our analysis. The cloud infrastructure is certainly helping us there as well. With more information around the cloud, we can do some interconnectivity. It's easier to get data than it was in the past from different sources and different systems within a company and externally. AI and machine learning play into that as well. Now we have models where we're doing the optimization of the simulation, but maybe on the front end and the back end of that, we're doing machine learning and AI to kind of tease out other things in the solution that better provide results to the customer, which some of that can be related to that risk and resiliency as well. I even think further, we're seeing situations where that AI and machine learning can actually look for things in our supply chain that maybe, I'll say cognitive blind spots maybe to the modeler, where we don't really see that issue or we don't see that problem, but machine learning or AI can kind of pick up on potential issues with our supply chain. It could be risk related or it could be kind of trends and costs and other things that we're seeing over time in their networks. So those are some examples where I think the new technologies are kind of morphing into this kind of supply chain design problem. I would add one thing to that is that the digital transformation that Mike has just mentioned also enables a new types of organizational relationships between different companies and relationships that now include actors that were previously not part of our supply chain ecosystem. One example of that is the use of crowdsourced resources, be it in transportation or warehousing and basically everything that is targeted towards an on demand use of the resources rather than investment in assets, which is a really interesting mitigation strategy that can build higher resilience into your supply chain. When we talked about the traditional approaches to the supply chain design, they would typically assume a very classical supplier-buyer relationship that did not reflect these new complex organizational relationships, did not for example incorporate the ways in which we would share revenue, share costs and share risks among those different. So that's another opportunity that we see. It's extending basically the way that we represent the organizational structure of the supply chains to take advantage of these new business models that have arised in the last few years. So we've seen the four opportunities are to extend the scope of the supply chain design, to incorporate tactical, more granular information and data, accounting for risk, and adopting new technologies. Do you have any examples of any of these that you want to share from your recent work? One would be a mining company that we've worked with for many years, and they're doing a lot of strategic analyses, even some tactical studies on their capacity planning. But recently they wanted to add CO2 to their analysis. So we were able to bring in CO2 into both their production facilities. They use a lot of rail as well as truck, incorporate CO2 into the network and be able to provide to them as a almost free byproduct of the model, that CO2 analysis. And they could run scenarios as well as they were looking at different sites, at different sourcing strategies, different customer assignments, what the impact would be on CO2. So that was an example where extending the scope to other factors was one example there. I'll give another example where a medical drug company is evaluating their production locations around the world. And in this example, there's obviously some tariffs and duties, but also there is some country rules around local production and how the government provides tenders and the win rates that you will achieve. So at the Malina's point about demand, this was very much a situation where, depending on where the production location was, the demand could shift and they change. And so we helped evaluate not only the network design from a typical traditional sense, but also how would that location decision affect demand, which was significant in their example. And if the time cycle is speeding up as we work on supply chain design, are these iterative processes then, do they become almost a constant in the background? Or how would you describe like, these sound like some pretty massive projects. They multi-year, multi-month, do you plan on a certain periodic rate? Yeah, so I think really from what we're seeing, Cooper's perspective and what we're seeing in the environment is not only are the decisions happening more frequently, again, we've extended now into that tactical scope. And we see a repeatability requirement that I would say 95% of our customers are requiring, and we certainly agree with that. Instead of doing that episodic event-based kind of process, it's now, how do we make this repeatable so that the data process can go from, in many examples, it's significant 30 to 60, 90 days down to hours to refresh a model. And that maybe seemed like a snick of improvement. It is, but it's really not that hard if we really just work through it one time. And then we get a compounding benefit because now we can answer those strategic questions more frequently. We can answer the tactical questions. In many cases now, we answer a lot of additional questions that we couldn't answer before. So one example is cost-to-serve. We often get answered, okay, now I just need to know cost-to-serve. Can you help me with that? Well, if we build this repeatable process, we already have the models in place, cost-to-serve is a natural byproduct we kind of get for free again, right? Can you answer now the more tactical production planning problem? Yes, because we can refresh the data quickly and we can provide those results. And what we see in addition to that is not only are we able to solve these strategic and tactical problems frequently, because we have a set of data that's validated and kind of refreshed and clean, we see, to your comments earlier, a lot of other parts in the organization want to go and access that data and leverage it just from a BI perspective and data analytics perspective, which we love because now we're all working on the same page. And from an organizational buy-in perspective, now there's more incentive for all the departments and functions to provide good data to the system because they want to see the results out of the systems, if you will. So we see this very much compounding benefit if we can get that repeatability put in place. So again, that's a, I would almost say a must-have in this day and age. So, Melinda, do you also have some examples from the work that you're doing? Yes. So one example is a pharmaceutical company that we are currently working with and that is directly related to this idea of extending the scope of supply chain design and having a more customer-centric approach. So in the pharma sector, we have a few industry trends that are really redefining the way that these companies are thinking about their supply chain and these are portfolio shifts towards different types of drugs that are targeting much smaller audiences but have potential much higher revenue, increased market competition with some generic competition that could directly impact the market share for their products. And basically what they are starting to realize is that in order to increase the revenue and market share, the drug is only one of the components of the patient experience. And so the way that you will actually fulfill that drug and bring it to your patient is a key element that they want to consider going forward. So the first project that we worked with them actually looked at different last mile strategies to deliver a certain drug to the patients and they were exploring various supply chain configurations but also basically the response of the consumers to things like home delivery versus getting your drug at a traditional channel, which would be a hospital or pharmacy. And we built a model that was basically incorporated at customer response with our overarching supply chain decisions. So that was an interesting exercise. And in the second iteration of that, what we realized is that in this specific space, there's actually a much broader healthcare ecosystem that we need to think about. And so rather than just focusing on a company and a patient, we are now incorporating additional stakeholders like healthcare authorities, insurance companies, etc., etc., which also have a say in this space. And so we're basically moving towards a kind of a multi actor model that captures those relationships that exist between those problems. And as you see, it's highly customized and highly specific to the operations of this company. And it's also going to be derived into a series of different models according to the country where you're operating, because of course the regulations around drug distribution and such will be very different. The second example that I have is linked to this idea of incorporating the tactical and the strategic. And so there we are working with a company that is a global shipping company. We started with them by looking at their overall distribution network. So, you know, spanning from Asia mini to the US. And as the time went by, we actually realized that in the current context, we needed to incorporate into that strategic model, much more granular description of the specific events that could arise, such as strikes at ports or different weather conditions that can delay shipments in certain areas. And so now we're moving towards a model that basically is incorporating the tactical scheduling and planning and route optimization, as well as more strategic decisions on that. I'm also curious, what are you personally excited about? Like, what gets you up in the morning related to these two questions? Like, you come to work and you say, you know what? I'm going to help this company overcome this obstacle. What is that obstacle? And why does it excite you? Yeah. So, as far as, you know, what keeps us excited here at Coupa and me personally, I mean, I've been doing this now for 10 plus years at Coupa Lamosoft. And to your question, the reason I'm still here is because I get more and I'm excited because I think like we can make a difference and we can help companies and they can really find value. We have the tools, the algorithms, you know, those algorithms are evolving, but there's solid algorithms that we know how to use. And it's really kind of taken advantage of the data that's not available, the computational capabilities we have, and then all that infrastructure to kind of make that a seamless process so that we can answer questions quickly for customers where before that took a long time to do. Additionally, with cloud infrastructure, you know, we now have the ability to roll out apps and other things that have kind of a user interface that's much more customized to that business function or that user. But under the hood, it's all the all the math that we love and know, right? All the algorithms and stuff are running underneath the hood that we developed for them, but now we're presenting it to more users in that organization to take advantage of that capability, run their own scenarios within some guardrails typically, but run their own scenarios, get their own results and allow the modelers to not be still excited and being part of that, but allow more people to take advantage of these capabilities in the future. How do you incorporate designing for uncertainty and anticipating the way the discipline will evolve? So that's a really good question because we are at a phase where, as mentioned before, we do not have this one model or one framework fits everything. And so we need to constantly basically reinvent the way that we are thinking about supply chain design. And the first way that we would do that would be not so much about replacing the types of tools that we are using, but really focusing on the organizational processes, the decision-making processes around supply chain design. And here, I think the key is really to enable organizational learning in a way that we can always adapt both our designs and our processes. And when we talk about organizational learning, there are several levels at which we can characterize it. So the first and most immediate one is to say, well, we implement a design, we observe a performance of that design. For example, I know demand was higher, capacity was exceeded, something like that. And then we tweak our design to adapt to this new condition. The second level is, I would say, is more complex. It's basically reframing the problem. So we've mentioned some of that, extending the scope, etc. And so it's basically saying, I'm no longer focusing only on cost, but incorporating risk, incorporating value creation, etc. And then the third level, I would say, is even more challenging. And it's about basically reflecting on our decision-making process in the organization and monitoring how that process happens and trying to improve that design process itself. And that means who is involved when we are defining our objectives? Are we including the right people in the organization, the right people outside of the organization? Who's responsible for monitoring the results? How are we enabling these feedback loops? And I say that most companies currently maybe at the level one or level two and are not still at that level where they are actually reflecting on their process itself. And that's one of the things that I find actually the most rewarding. It's when we get people from functions in the organization in the same room and we have them come up with new problems, new ways of solving those problems and understanding the value of actually changing the way that they are currently performing their supply chain design. So what stops people from knowing that they need to do that? I mean, I believe the white paper probably is groundbreaking in that respect. People have never stood back and asked themselves like, hey, I need to know. Oh, I've got to talk to people across my domains or oh, I have access to this other data or oh, I need to do this more periodically than I have been. What are the biggest obstacles? Because we don't know what we don't know, right? If we're running an organization and we've been doing it this way and... Yeah. So I think from what we see, I would say there's probably three things that get in the way of customers moving forward. The first relates to what I'll call organizational buy-in, which from the leadership perspective, we talked about earlier cross-functional involvement, commitment, IT, as well as the analytical resources. We can say as COE, a center of excellence related to supply chain design, all of those pieces need to be in place and agreed upon such that from an organizational perspective, we're going to sustain this process over time. And just from a COE perspective, we know today, obviously with some of the challenges in the job market is even that COE, you have to have a process of training the people, giving them more opportunities to learn, recruitment, retention, all of those things are critical to keep the COE progressing and lively and growing. The second thing is, I would say, is that companies do not have a clear roadmap of the types of problems they want to solve and how they're going to grow that over time. I think the Malina's point is they're solving a problem, but they really don't know what problem two or problem three and how those are connected and what the sequence should be and all of those things. In addition to that project roadmap, we talked about earlier the idea that there needs to be an automated process to do the data collection and the data foundation of this whole process. They lack that infrastructure to do the data collection and data automation, and so therefore every project is a challenge. People come to them and say, I need this question answered and the response is great, I'll have it for you in six months. Well, we know that's no longer even acceptable. If you don't have that common data infrastructure in place, then you're not going to be able to answer the questions timely and therefore the value proposition to the company is less. The third thing is really that we have a sound understanding in that organizational buy-in of the metrics and the deliverables that we're going to be providing to the business. We know that there's an investment in resources and effort to do all this. We need to be able to measure and show that to the business that we are delivering the results. Historically, I think we've been very good at the analytics side. Maybe most of the modeling team and those data analytic people are not good salesmen. We need to help support them to make sure that, hey, you are delivering value to the business and we actually prove that ROI to the business. Those are the things that I would say we see as some of the common obstacles to really getting moving forward. What steps should companies take then to get the ball rolling? There are many steps and I think Mike has already hinted to a few of them such as increasing data availability, making sure we have automated data processing and sharing structures in order to really get that end-to-end view and transparency. However, and here I'm seconding Mike in what he previously said is that I really do think that the most important step is at the level of organization. We really need to have a clear ownership of the supply chain analytics and design. For example, establish a dedicated team or a center of excellence who's going to have that long-term vision around supply chain design and also empowering that team by the top levels of leadership. We want to avoid to have that be a separate cell that has to battle with each individual structure, function in the organization, and we want to have a top level sponsoring of that supply chain design center of excellence or at a dedicated team. We see things evolving very quickly in many domains, including supply chain management. Where do you think we're heading? What we're seeing as far as where we're heading in the next several years is really several things we touched upon some of those throughout the discussion. First, of course, is we continue to see that scale data, big data, the scale of data, the granularity of the data that we need, as well as the cloud-based connected solutions will continue to increase. We already talked about the idea that the strategic tactical slash planning solutions are kind of continuing to merge as far as what those problems we're trying to solve. I think, as Malina noted, though, that doesn't mean that there's one size fits all, but there's a library of solutions that are kind of interconnected as far as their data, but there may be slightly different variations to solve different problems. Kind of related to all that is the persona of continuous process-driven design, that this is a continuous process. We talked about the idea of that requires then this repeatable kind of foundation to it. We talked a little bit earlier about this multi-enterprise kind of solution that's kind of kind of growth. We do think that multi-enterprise kind of solutions will continue to be play a part in the future here. Risk, of course, we don't think that's going to go away. We think risk will continue to be play a prominent role in kind of these network designs and how to be able to react and respond quickly to those as the problem is proactively planned for those things. Sustainability, of course, those kind of pieces to the puzzle will continue to, I think, be important. Again, we talked a little bit earlier as well about the AI and machine learning. We see, again, extending the solutions to include more AI and machine learning components to these problems, whether it's kind of on data analysis and trends or other components, as well as prescriptive kind of findings and solutions that they might provide. So I think Mike covered quite a broad range of things that we are expecting to happen over the next few years. One thing that I would want to add is relevant to what we are observing now. I would say that the last few years really triggered a change in a way that companies are thinking about supply chain design. And I think a lot of companies are starting to be aware of the different problems, starting to realize the necessity to be more customer centric, to incorporate risk. And we are at a stage where the problems are there and well defined, but we don't necessarily have the solution. And I would say that supply chains are in a very specific place. They are really now at the center of the corporate strategy and decision making. There's a lot of excitement going on and I see a lot of experimentation happening, particularly with new products and services. How do we differentiate from our competitors? How do we use supply chain design to do that? I do, however, see that this experimentation is not necessarily always supported by this data driven approach and that the level of decision making maturity is often limited. And so I'm expecting that in the next few years we'll kind of see more clearly what are some of the winning strategies among those range of different things that people are trying out and basically identify the winners and losers of this process. And there I will think that having an intentional well structured and analytics driven approach is going to be key to being in the winning side of the equation. Excellent. So how do people get the ball rolling? If I'm an operator in a company, how do I get the ball rolling? Yeah, so obviously we work with a lot of companies that are just getting started and so what we typically want to tell them to do or how we coach them through that process is pick something that's small but important to the business. So you can kind of work on something that clearly is going to demonstrate value to the business and kind of show some value as far as the effort that we do. We certainly prefer something that has some repeatability, like we want to get answers more frequently and we want to then build on top of that kind of a repeatable process. So let's get in place something that not only answers questions but answers it in a repeatable way so customers can begin to see kind of how that the value of that repeatability and how it can speed a lot of the speed to answer a lot of the questions. Certainly there's many organizations out there, you know, CTL, Coupa, other companies that can provide some support to those companies to help them guide them along the way. We certainly have experience with customers that struggle maybe a little bit more than they should. An external kind of partner can help kind of speed that, you know, time into delivery. I think overall being more efficient for everyone and then of course, you know, leverage the existing solutions out there. There's plenty of solutions out there that already can kind of solve these problems, take advantage of what's out there, make sure you fit it to what you need but, you know, find something that's the right fit for your use case and get going. So, Mike, Melena, thank you very much for being here today. Thank you. Thank you. My pleasure. Glad to participate. We really appreciate your time and super valuable to us and hopefully it'll be valuable to everyone. So yeah, thank you. Thank you. Yeah. Thank you so much. All right, everyone. Thank you for listening. I hope you've enjoyed this edition of MIT Supply Chain Frontiers. My name is Arthur Grau, Communications Officer for the Center and I invite you to visit us anytime at ctl.mit.edu or search for MIT Supply Chain Frontiers on your favorite listening platform. Until next time.