 Welcome, everyone. Thanks for joining. I'm Kellan Betts. I'm a course lead here in the MicroMasters MITx MicroMasters Program in supply chain management at MIT Center for Transportation and Logistics. I'm very excited to be here today and co-hosting with my colleague, Laura Aleya, also a course lead in the MicroMasters program. And today we're very fortunate and super excited to have Annalise Cabrera, a managing director and partner at Accenture. Welcome, Annalise. Thank you. So if you've joined us before for these webinars, we'd like to kick things off with a poll. And so if we could launch that first poll here, I'd like to know a little bit more about our audience. So question, why are you here today? Some of the options I want to learn about distribution networks. I'm interested in knowing more about use of data as an enabler for network design. So if you could please just take a quick moment and thought that poll, we'd love to learn why you're here today. And while we do that, my colleague, Laura, will go through the agenda for today. Thank you, Kellan, and welcome, Annalise. I'm very happy to have you here today. So during the next 15 minutes, Annalise will discuss real-life considerations when designing and operating a distribution network. Of course, this will be in the high-tech industry, but it will be applied to all. She will cover from the layers of complexity in modeling that network to the importance of selecting the right partners to make it work and the role of technology and data management in this process. Kellan and I will then ask some questions we have prepared for Annalise. And we will save time for your questions at the end, of course, as we usually do. So start thinking on those. And please use the webinar Q&A feature to ask your questions. Be sure to be logged in with a name because we will not read any anonymous questions. So thank you for that. And be prepared to participate. And before moving on, I will check on the poll results. So if we can end the poll and show the results, that would be great. And let's see what our audience is here for today, Annalise. So it seems like most people want to learn about distribution networks. So that's great. We are going to address those. They are also interesting of the use of data on the distribution networks and generally expanding supply chain knowledge. So I think we have a little bit for everyone here. And that's great. So with that in mind, I think we're ready to kick it off, Annalise, right? Yeah, I think we're ready. Hi, everyone. Thank you so much for having me here. It's a pleasure to be here. As Laura said, well, actually I can't share this. I'm a managing director within Accenture and part of the supply chain practice based out of Seattle. And currently I mostly work with high-tech clients. So I will be mostly focusing on that industry today. But again, I work with distribution networks with other industries. It's just today I happen to focus on high-tech. Okay, so the topic is how do we turn network models into action? I would like to start saying that every single object that we have around us has a distribution network story, even art. So if you look at the picture that I have in this screen, it's actually a picture I took in an exhibition. It's an exhibition of around 2,000 little rods. One of the things to think about like, how do they actually move this? Like they move all these little rods all the way from California to Zurich. So it made me realize like really like, someone had to take the decision on how are we gonna package these rods because art is very like fragile, right? We need to keep certain temperatures so these rods don't melt. And really like all of these decisions take a play when we start deciding distribution networks depending on the product. In a specific for high-tech, I wanna talk about the fact that a lot of the companies are already finding their distribution networks with one thing in mind. And that thing is building the resiliency that they need. The reason why this is happening and this might not be news to you is I think there are three forces happening in the market. One of them being after the pandemic, there was a lot of semiconductor constraints and a lot of these industries rely on it. Right now there's actually, at some point there was a surplus and then there is already a constraint again. So it's very volatile. But the reality is that the demand for these semiconductors is exponentially growing. So when you think about like for example, the cloud infrastructure that it's needed to run a lot about a lot of the chat GPT promise that Microsoft made, these semiconductors will be needed to deploy all that compute that they will need for chat GPT. Cars need the same semiconductors. The computers need the same semiconductors. So it's really something that is in high demand and that a lot of industries are relying on. So really companies need to be resilient in phase to a lot of the political dynamic that are also happening, which is the second force. It's probably not news to anyone that there is certain dynamics between China and the different countries. And a lot of the supply of those semiconductors come from there. So a lot of policies are changing, right? Like the U.S. making a very large push to have now cheap manufacturing in the U.S. to reduce the reliability that we have in other countries. So that's a second force. But the third one is, and this one is very interesting. I was reading that the center of gravity of exports of goods hasn't changed in the last decade but now it's forecasted to change in the next five years. Actually, Africa is taking a larger role or it's forecasted to play a larger role. And if you see China, it's forecasted to reduce its presence in those exports. All of this matters because again, as we think on where things are gonna be moving from, we need to think where are we gonna be positioning the different warehouses? How are we gonna be moving all of these different woods around the world knowing that the center of gravity is also changing? There is also implication of, for example, cultural implications, right? Like you will have to learn to do business in other countries. So it's really like a perfect term, if you will, that as supply chain practitioners can start thinking about when we start setting up these distribution networks. One thing that makes it very interesting and I think when I started my career, this was not the mindset at all, is that there are new forces that need to be taken into account when designing distribution networks. So there is a mindset shift of our decade that we all here in this room will have to face. One of them is the commitment to sustainability. And when I talk about sustainability, I don't necessarily only refer to emissions. I also refer to ethically sourcing the raw materials that we have. And even like making it economically sustainable for the communities that we work with. I was actually recently last week in a symposium and one of the founders of this chocolate factory was talking to me about how he's working with suppliers or with farmers in Madagascar. And he basically set up, like he risked a lot of these farmers to work in the manufacturing facility and he's literally working with the whole community to make it sustainably economically for them to have that facility with them and using their cacao and all of that. So it's really thinking beyond the normal parameters that we as supply chain practitioners used to think which was cost and service and being fast. I think now we do have more responsibility in the decisions we made and more commitment to the planet. So that's something that we will need to take into account as we think about these distribution networks. As I mentioned, the second one is the source of diversification and that is really driven by the fact that that center of gravity is changing. So at the end of the day, if the center of gravity is changing, we also wanna diversify the number of supplies we play with. We don't wanna have all our eggs in one basket. We really wanna have resiliency and redundancy across the supply chain. That comes with a lot of implications where are you gonna be setting up your different distribution nodes? And also the whole concept of economies of scale kind of breaks because before you will think I have to consolidate because that helps you fight a lot of the constraints in the market. Now with all of these implications you actually need to start diversifying intelligently. The third one, which is a new parameter that we have to play with is the trade-off between the service and cost but also the emissions. I'm sure that Jose in the last session talk a little bit about the sustainability piece of it and really like the measurement of CO2 but emissions will play a big part specifically the transportation networks. And I know there is like a whole transition into electric vehicles and really having this transition to an infrastructure but we need to think that even with that infrastructure batteries of those vehicles don't last that much, right? So you will need to consider that the vehicle will be downtime for a period of time while it charges again. A lot of that infrastructure is just being built. We don't know where those nodes are gonna be. So there are many things coming our way that when we started defining these distribution networks will be interesting to manage. So it's a perfect place to be I think supply chain at the moment because we will have to deal with all of this. And I think this decade, this is the type of constraints we will need to live with. Having said that, I wanna walk you through a case study in Latin America. That case study didn't necessarily have all of these constraints because I did that a couple of years ago but it just have a lot of the concepts that you guys are learning in the course. So that's why I wanted to bring this case study. It was very applicable to the realities of that company. And then I also wanna tell you how did they end up deciding what network to use? So as you probably have learned there is this concept of the total land debt cost, right? Where you take into account the cost of holding the inventory, the cost of moving, the cost of the warehouse itself and everything it's an algorithm and it optimizes depending on service levels that you need and the cost that it's associated to that service level. We initially use that model to have an idea on if we were to decide how many warehouses we need, basically the algorithm will tell us. Before I go there, I forgot to mention this is a cell phone device distributor. So I had the opportunity to already find the distribution network from the scratch. So we literally could say you need only one warehouse or you need 10 warehouses or whatever really needed, it was from scratch. So the algorithm will always tell us like you only need one. But what I can tell you is given the realities of the country, one was not the answer. And the reason behind that is there was a lot of unit implications. They were actually running 11 warehouses. So it's not like they could just shut down all of a sudden and say, oh, we're just moving to one, right? The other piece is in the case that you moved to one eventually, within that sector, if the union at some point protests, then you need to have redundancy to distribute from somewhere else. So there were other implications that we have to take into account in order for us to make the decision. But what I can tell you is that we had very clear criteria from the start on what was a good distribution network and what was the criteria for us to determine that that's the way that we wanted to move forward according to the strategy that the company has. So as I said, the total amount that costs did play a large part just because we could model from a cost perspective how much that's what was gonna cost. But it was not the only factor. As part of this work, we also had opportunity to define or decide with what 3PL partners and what transportation partners we were gonna be playing. Here is where it starts getting tricky, right? Because in order for you to identify who you're gonna work with, it really depends for what channel. In this case, they were distributing the cell phone devices in B2C channels, B2B, and then they do have own stores. So each one of the channels has a completely different requirements. So at the moment of us assessing, for example, the warehouse partner, we needed to take into account that each operation is different depending on the channel. And also there is an extra cost associated to certain requirements, right? For example, if you wanted to go to B2C, there was a requirement that the customer needed to pay in cash. So whoever is distributing that device, the money management is something that no one wants to do. So it becomes more expensive. It becomes a more expensive channel. So even when the greatest team of things, we said like we only need one and if we just look at the total numbers, this is the best partner and we only need one, the reality was that looking at the requirements of the channels informed a lot of, maybe we need these three additional partners that can help us play in these other areas of the country just because they know how to move product in that country, in that area of the country. There were also dangerous areas that they needed to have experience with and those lines or those distribution lines tended to be more expensive as well. So again, the channel search mattered a lot on deciding what partner we were gonna work with not only from the workhouse perspective but also from the transportation side. As I said, there was the reality of the optimization or the outcome of the optimization versus what the country was leaving and there we had a clear scorecard and we knew like these are the things that we're willing to compromise and these are the things that we're not. So that was pretty straightforward with them just having clarity on that. And as I mentioned, that selection of the trial and transportation partners was not only about the cost, it was also about the level of experience that they had trading this product but also even how they contractually or wanted to work with the company was relevant, right? Because there were certain quotes that will be, I have a fixed and if you only do this fixed then it costs you X but when you add these value added services I'm gonna charge you separately and there were others that were included. So all of this to tell you that when you create the total landed cost it's not as straightforward as just plugging numbers. It's really has to do with the data that you have to play and you have to make certain assumptions because it's never gonna be perfect but it's not as straightforward as one thing like you receive data there are different types of quotes you cannot compare one versus the other what does this quote include? Can I take this call to just like plug it in the model and see what happens? Like it's not like that I think there is a lot and that's where we all play. There is a lot of thinking on can I really compare a quote of a transportation that it's dedicated for certain region versus another one who quote me, I don't know I will give you five trees but then if the fuel changes and I will charge you more depending on the price of the fuel. So that's another type of modeling. So in the baseline everything was compared apples to apples but the plugin of the numbers matter a lot on how we interpret the data. So with that being said I just wanted to let you know that all of these concepts that I learned at MIT did help me to establish a conversation with the client and also be able to decide the best network for them but I can tell you that a lot of the real considerations happen and it's just what it is and we all will have to go and face it. So that's fun. Awesome. Well, thank you guys. That's a fascinating presentation. I love the context of just the challenges that we face in global supply chains and the opportunity I guess that presents for us as supply chain professionals and those who are learning and evolving in this space and so on. Super exciting awesome. Appreciate your presentation. So I wanted to kind of be focusing on a little bit building on your case study presented there and kind of that extra calculus if you will that goes into that sourcing decision or that network design and then also that sourcing decision. You mentioned that there's often multiple partners or players, all three PLs, technology partners, transportation partners as well but the warehousing as well. I'm wondering, even all those different partners that's kind of a complex ecosystem you mentioned that some might have specializations for example, like in geography but they're also like is that technology interface between all those different partners. So carrier has to be able to talk to the warehouse TMS for example, all those kinds of considerations if you could expand on that technology interface or that information flow if you would train all those different and how that kind of comes into that calculus how you would maybe then make those selections and design that network. Yeah, that's a very good question. So actually before we went into the RFP one thing that we made sure as with the client was that we had clarity on what pieces of the order to cash cycle we wanted to outsource, right? Because there are different models that you can establish with a TPL like they can run you from the planning like they can run you the entirety of the supply chain if you allow them to when you will have to pay the cost but I think before we even launch an RFP we will literally say what pieces are we willing to outsource what pieces are we requesting my partner to have in that case actually we were requesting them to have the WMS and then we requested them to have the TMS in case of the distribution one but also we wanted to pay to them this is the inputs that I'm gonna give you and these are the outputs that I want from you. I think having clearly from the start on like what are the exact flows that you expect with your partner clarify a lot not only for them in when they quote you because there is typically like a quotation for the systems piece but it also reduces the complexity on trying to integrate everything and trying to have the status of your order all the time. I think we're clear on like I wanna have a status at this point or when I receive inventory I wanna see it in my system reflected with this SLA so having that from the starting that order to cash cycle was like key. When we've allotted the technology of the partners majority of them will have very standard tooling I think it has more to do with the company itself depending on the industry companies have different maturities and majority have a lot of like in-house developed and that's what makes it very complex to plop into another system but again, if you have clarity on exactly where are you gonna feed them and then where are they gonna feed you back it should be very straightforward. I never saw a partner in the RFP that we did that didn't have solid technology it was more of us on saying are we ready to receive it this way? Are we ready as a technology team we're in the company to actually plug what we need which is most of what needs to happen like the responsibility of enabling that but I can tell you that most of the partners have what we requested them but that clearly helped. Awesome, thank you for sharing that Annalise and I'm now thinking like in this implementation of the network models and how important it is to consider all these layers of complexity and we think usually of them was multi-dimensional models and as we usually teach it or learn it it's more about adding elements to the objective function and making it a multi-objective function or probably just adding things like constraint. So probably we've learned that a constraint could be the number of miles that we're going through in terms of the carbon emissions those miles will generate or adding carbon emissions to our objective function and an additional thing beyond coast or surface level but we were wondering how do you actually make it work because you have mentioned there are other items like ethically sourcing is it that you make a pre-filter before you go into the algorithm and input the data or is it that you make a risk assessment and then define what elements are you going to incorporate here? How do you actually make it into action? It's a great question too because I think little do people know that before we even move into the modeling itself there is like a lot of prior conversations that need to happen and I was mentioning before about the clarity on knowing like what do I want to source? Do I want to source all? What's my strategy? I think if at the beginning it's clear like let's say the company wants to do I don't know one day delivery because that's our dream. I think then the distribution network will follow and different distribution networks serve different purposes but I think as supply chain practitioners we do have the responsibility on connecting whatever we set up to serve the greater purpose and I think that's where we gain a lot of power because there is always conflicting priorities and there is the conflicting priority of like I want to deliver everything every single day to you every hour but that's not very sustainable unless we have a fleet that it's all electric and other considerations but there is a trade-off there like what's more important for you to follow the trend and then just deliver everything every single day or actually make a claim and say like you know we're responsible we are just gonna like optimize our routes and we will deliver when we can. So it's a lot of prioritization and prioritizable conflict but if the company has a clear strategy does get started out from the start but what I can tell you is that having a clear scorecard criteria on how we're gonna grade the different proposals and the different networks that we have was critical. So for example, security that's something that I didn't mention, right? But these phone devices are like high value you do need a lot of like the facilities need to be guarded like even the security practices that they have in the warehouse matter. So that was like a high priority because there was a lot of lost and deft and they were having a lot of cost inefficiencies there so that was a priority for them I know your priority was that insurance was like all the devices being moved was covering the price. So those things are clarified from the start before you even start defining anything otherwise what's gonna happen is well the number and the algorithm says something very nice and this is what we think a supply chain we should go and do but then it's very easy to get in the rabbit hole and like well but that's not my priority I don't want this. So having those conversations from the start help I'm setting that baseline so you can make faster decisions as you move into the modeling because the modeling will never tell you what you want them to hear, right? But at least you have data and you can say like we made this decision because this is our priority and this is how much money we're living on the table and that's clear. Awesome, thank you Annalisa. The, actually the empowerment I guess you'd say really the initial point of how it empowers us as supply chain professionals, you know and it's often we call them like the model and the output of the model and the algorithm and here's the answer, you know it's $5 or whatever it is, you know but there's so much goes into, you know the supply criteria and that balance scorecard that you mentioned but then also really also mentioned kind of establishing that work hash cycle and that strategy and to know what flows on before you even started modeling so I guess that's awesome to hear. So before we can just, yeah go ahead. One thing I also wanted to add is every industry is different like to me there are distribution networks that are very hard to change, right? Like you already built a manufacturing facility and like moving the manufacturing facilities not as easy but I can tell you that in consumer goods for example like bottling water you could turn up and turn down facilities as you please. So that's something also to consider like the easiness to move your decision some decisions are like immensely critical you cannot just be moving manufacturing but there are others that depending on your industry you can be more flexible on them. Yeah, that's something to add. Yeah, that makes sense. That flexibility I think is what's something that some companies are maybe looking for in this dynamic environment right now for sure. Awesome. Before we transition to questions from the audience thank you for all of you who put questions there in that Q&A feature, speak into those and keep putting those in there and do please use that Q&A feature but maybe one last topic we wanted to discuss before we get to those questions is kind of the topic of data. You mentioned this when you were talking about the case study and how that data is critical inputs to the model also probably it's critical to some of those pre the strategy decisions and some of the before work that goes into the modeling but with supply chains we also know that data ecosystem is complex I guess you could say are fragmented or siloed as a comment term that we often hear sometimes that data see or it's just not connected maybe there's tons of data but it's just not connected and it's hard to kind of link up like that for the cash cycle, all those pieces together. Same thing with some of the sustainability criteria you know, CO2 emissions for that kind of thing. So I'm wondering if you can do some of the challenge and opportunities, maybe even barriers you just kind of on that data ecosystem from this process. Yeah, so I'm glad you mentioned this because I remember like learning the concept I'm like, yeah, this is great like we can optimize and blah, blah, blah getting the data is not that easy. I mean, the transportation and the warehouse cost was pretty straightforward if you will just because we weren't getting quotes and then we have to standardize them but for example, the forecast of the demand per channel, you would think like, oh, that's very straightforward which was asked for that information. Well, it was not that easy, right? Like it was like merging two companies like it was very complex task. Another thing that I found fascinating is inventory holding costs for some reason it becomes very controversial, right? Like convincing the leadership and making them realize, I mean, you need to bring finance on your side always but making them realize like there is a cost associated for us holding this like there is a capital cost, right? Like there is a trade-off of me having that inventory sitting and diversifying five different warehouses instead of one. It's a very difficult thing to explain, believe me. Like I think the whole concept that we probably have a risk pooling and like when you have more concentration you can aggregate errors. It's a concept that not necessarily everyone has. There is also the fact that I might have the priority of being sustainable and having causes my top priority and that's why big conversations need to happen but sales might have the priority of like fulfilling orders as fast as they can, right? Even for a retail channel and having five warehouses will meet what they want, right? But it's just having clarity about their different teams and why a decision is being made but I can tell you that the data is not the easiest task to get. Like there is a lot of cleanup and agreement on like this is the forecast that I'm gonna be using because if you increase the demand in certain lines then your cost will look attractive at some point but the reality is that not for everything that forecast is accurate. So there is a lot of critical thinking that it's necessary from our side on knowing the business and also like getting grain and not just like plugging numbers and saying like this is what the model says it's more than that, yeah. Thank you, Annalise. And I like how it always comes together to communication and the importance that it has that everyone can speak the language of the others stakeholder the fact that we need to convince someone in finance we need to understand what the finance person needs. We need to talk with people in manufacturing we need to understand human resources needs because it's important to know as you say can we just close down this factory and then open it up? Not only from the manufacturing perspective but from the union perspective or the impact on the society. So I loved how you brought it all together to communication and not being silos as Kellen was talking about. And I want to close us with one last question and this is because we know your credential holder and an MIT alumna. And even though we have people from like this is a very broad audience they have different level of experiences joining us here today. But we want to know what is your recommendation or your advice for those that are just shaping their supply chain field. Someone probably has been in another field for a long time and then they want to switch to supply chain or someone probably is just starting a supply chain and want to know what's next and we love your career and we would love to know what is your recommendation for them? Yeah, definitely. I think that supply chain is like a puzzle solving. So like if you love solving puzzles and also like using data it's very analytical, honestly. I've never been in a project that doesn't use data. Then you're in the right place but you need to be very comfortable like dealing with complexity and also with a lot of ambiguity. Ambiguity and also with not being light. I think supply chain historically has a seat in the table that is like you need to do what I tell you to do your Disneyland. So I think that mindset is evolving as we move and we became more on the hot seats in the last couple of years but it's just very, it's like a very fascinating career for me and a very fascinating topic just because you get to work with everyone in the company and like different departments and you need to learn to speak a language of others. And I guess it's the same for other departments and they might say the same about supply chain like I need to learn to talk to them but the reality is that within supply chain if you wanna make change happen is a lot of communication but a lot of data and also like knowing about the business and knowing about like this is what they want to achieve and put yourself on the shoes of like if I wanna make this company successful how I can contribute to that objective. So what I recommend always is if you're starting your data analytical skills right like that's fundamental like there is no going in supply chain without it especially in these times a lot of the decisions will rely on data and again, there is the soft skills and I'm sure that other careers will have those soft skills but I think in supply chain a good place to start is having those data analytics skills. Awesome, well, hopefully, you know, your energy is very empowering inside hopefully our audience out there is really feeling that it crosses a little bit, I think well, I appreciate your insights. So maybe we're gonna launch into our we're gonna pull some questions from the audience Q&A before we do that, we wanna launch our second poll so I think that's that second poll real quick here. Well, that comes up, the question was the most in part of today's and we always, you know in through our webinars as we've been in so, you know, some of the options you understand how to incorporate real life considerations in my models and maybe you found your data management supply chains was expanded by today's webinar just take a quick minute and chill out well that all in some this year from the Q&A and so I'm gonna kind of maybe buy in a couple this really builds on what you just in there what Laura was mentioning with communicating aligning stakeholders, that part of it. So there's a couple of questions here one from Rich and one also from like Michael and I guess the question that all or the way I collapse these two together is the concept of change management and aligning stakeholders and what happens if there is an alignment, you know what happens if the sales team is really pushing for a forecast and the, you know warehouse separation can't deliver on that forecast and how do we, how do you approach that misalignment and then how do you, you know work to some of those change management pieces to gain alignment, I guess. Yes, there is always political games right in companies but letting that aside I think what has been proven successful at least in my last year like couple of years in my career is the simpler you can explain something even if it sounds like, oh my God, I'm explaining a baby it's the best way you can go. I think also putting yourself in their shoes, right like it sometimes is hard like creating a lot of empathy for someone in sales who is really like trying to make your quota and that's how they're being paid and saying like, look, I understand that's your priority but like having someone mediating also helps too like a finance person, right like that person is taking care of the cost aspect of the company so that person will help you translate a lot of what you're saying but putting yourself on their shoes and trying your best to really say like, I understand that's your priority this is how I will be able to help and I can understand this is a con but this sounds to me more like an edge case or I think understanding the other side of the story help us to supply 10 practitioners I think historically we tended to be like oh, I just work here in isolation and this is why I just run this network and that's it again, to get by in you need to put yourself in the shoes of the other and understand their side of the story and like what really worries them and some people don't like being exposed, right like they don't like to be told like, I don't know what I'm talking about you can take them aside and say like let me explain you this like one to one and get you to the details and I mean, not everyone has the same and a legal background too so like regression model or like optimization model or like this is a black box I don't wanna do this so what is this black box? Why are we gonna decide on this black box? So having like a little bit of empathy on that and then being clear on like, look this is how it works in a very simple matter matters I normally learned a lot and I developed this skill by listening a lot to earning calls from companies because in earning calls, they typically teach or like tell them about their strategy in a very simple manner sometimes a very, very, very complex model so just take a step at that it's just very interesting how communication could be simplified from our side because as soon as someone doesn't understand you and it's about them changing but not you it's not gonna work, so. Thank you Annalisa and definitely like empathy and knowing that not like we can all be experts but probably just in different fields and that we need to be able to explain our knowledge in different levels depending on the stakeholders we have in the other side so that's a perfect advice and I love that we're touching a lot of soft skills because we usually move on like more into the technical side so I love this, thank you for bringing that audience I have another question, this is from Joseph and this is not fully related but it's on technology and supply chain and they are very interested in knowing about your experience they are asking if you have seen a widespread implementation of digital twins in the supply chain in any of the projects you've worked for? Yes, definitely it's very difficult because normally in technology we'll see it like I've seen and I can share it after the call and I can share the white paper it always needs to have a purpose and I have actually seen it in telecommunications because there is a lot of reliability that you need in the cell site towers specifically in the network towers that you need to have so having a digital twin on what's actually built on the site helps so I've seen it I can tell you that digital twins is not for everything like it's not like digital twins let's just go do it for everything like it's not like that I think again when it's like very capital intensive I think it makes sense because then you really wanna know what's installed because if you need to maintain it then you need to bring a crew if you have a digital twin that typically will help you so or simulate like what happens if I change how I position the different towers what's gonna be the megahertz that I'm gonna have so it has just cases I think one as a supply chain practitioner needs to be careful about it and really think what purpose is this serving and what is it contributing for the company again capital intensive companies I do think they have a use case for sure yeah and I'm happy to share what I think it's public so I'm happy to share more about it I think I know companies and a lot of owners are into technology and it seems to be kind of one of those almost buzzword a little bit but also kind of one of those areas where there's a lot of attention being focused on right now so that's a great practical experience and I also loved your tip about earnings calls that's a great tip it's a critical message that a company has to put out there it can have such a big impact on their finances and a lot of other things and so they have to be clear and so precise with their language and so to learn from that great tip hopefully I'm definitely gonna do that for all of them so awesome so let's take a quick look at our results for our poll here the question was what's your priority session for you it looks like the majority here enjoy learning or understanding how to make real-life considerations so my network design was like that was kind of the core message today here so that's me I don't know if you have any answer or a thumb up over it that's cool I'm glad people are learning how to use all of these real constraints again modeling does help like it's not like something I've never seen it like it does help it gets you half there but it's not everything and again part of also our work is making that model credible but also do not overcomplicate it especially because a network is such an essential piece of the company you really want people to understand it yeah it's not something that you're doing an algorithm for allocating inventory and only us take care of it it's everyone will be impacted by your decision so do not over engineer it awesome yeah that makes a little sense awesome so I'm going to pull one question in here from the Q&A from Gulam Argoosh this kind of really builds on some of the comments earlier about the challenge of the data but then also your point about operating sustainability and how it's becoming more important we're increasingly the focus of a lot of companies is incorporating its capability criteria whether it's emissions or whether it's the social sustainability criteria into these models or into the process and some of those strategies before you even get to like an optimization model and I'm wondering we obviously data with you know things you mentioned you know inventory holding cost was a challenge from a data perspective what about sustainability side like is data a challenge or emissions accounting you know actually any basic emissions sometimes can be you know somewhat straightforward but also you know like where do you get data from some of those calculations and especially you know some of the other considerations that sustainably not just emissions yeah so I will talk about two different examples the reality is that calculating emissions is very challenging like really like we really wanted to know exactly how many emissions this cub has is extremely complex I know there are models and there are formulas to estimate but it's known that those estimates have a very large amount of error it's a baseline and those models can help you and there is even systems now that can help you calculate the emissions of your organization like there is information like okay this flight will be X and you can parade it and do your best and have a very broad estimate but again it is never going to be like super precise not now I mean it will I remember having a class in MIT and we were literally trying to trace like what was the emissions of this banana like it's almost impossible but again you can make your estimate and there are partners out there are going to help you on providing the data and that could be one avenue the second avenue that I've seen more from the optimization perspective is and this one I actually saw it with Josue who was in in the prior session with fuel like gas fuel cars there is a whole and there is a white paper on this as well that depending on the weight of the car and then how elevated the topography is it's the emissions that you will have based on the burnout of the gasoline right so the whole optimization of the route was based on the optimization of emissions and again this is like a gas fuel type of car I can tell you that there is a lot of research that is taking into account this consideration and it's actually being implemented I actually do think they have a company that implemented a lot of this modeling and that I can tell you that it's back based because they do control like this is the stops that we're going to have this is the topography that we are going to have and we perhaps need to first download everything that is heavier so when we go into you know slope we are a little bit lighter so you born less fuel so it's more to optimize the fuel consumption as you go on in in effect or as a result you will have less emissions so I've seen it on that avenue I can tell you that the one is more precise I do think it's more applicable but yeah there is the other avenue where you could estimate and do your best and it's the best wish for everyone but that the one that I talk about that route optimization it's the one that it's more clear to me and I've seen it implemented so Thank you Anneli for sharing those different avenues because of course it will also depends on the size of the company and the possibilities and the resources they have access to different industries also manage different levels of access to information or data systems or even different regulations will require different things from them so it's really important that you mention like this there are different possibilities it will depend on each case which one will be the best fit for you or your company so in the interest of time I think we can start wrapping this event up we truly appreciate all your insightful comments like we've learned a lot not only about a network design itself or the high-tech industry but also like the challenges you will face like when you're trying to implement something there will be complexity there will be resistance there will be a lot of features and factors that you usually don't have in your mind when you're just working with algorithms so we love to hear those from you I don't know if you have any final words for our audience Nope, I mean you guys are in the right spot the next decade again a lot of these concepts are evolving and I think the fascinating part of to me is like we're being part of that change and you know like think about okay now I need to have electrical vehicles so this car needs to be downtown these amounts of like there are new things coming our way that no one has thought before and it's just exciting honestly like I'm just excited for what's coming so go for it if you want to be in supply chain just do a lot of analytical work that's gonna take your places for sure yep thank you thank you for sharing your passion in supply chain for sure and I'm killing any final words yeah no for sure I just appreciate your time sharing your knowledge your time and your experiences today and definitely your energy your digital environment here your energy definitely across those digital wires very well so I appreciate you sharing your time on Elise sure awesome and anyone in our audience remember this is the second webinar of a three webinar series so we hope to see you in the last one which will be around mid-march so stay tuned and reach out to us if you have any questions see you in the next one thank you Annalise thank you Ghislaine thank you everyone for joining us thank you everyone