Okay, so there is something very interesting going on right now. There is big companies like Google and Microsoft trying as quickly as possible to put AI tools inside their workspace or office products. We've got Google doing that with text generation and image generation and we've got Microsoft doing the same thing, but better because you can actually ask the AI to interact with the document. As an example, tell Excel please make me a table with blah blah blah. But there are a couple of issues with this approach. Firstly, they are scared and they are not releasing at all these products to the public, but only giving them to a few selected companies. So all of us mortals who might be interested in AI just can't access these products. Also, they quite don't care about whether the models are open, available to everybody. Actually, they are probably happier if they are kept private. I would say it's a bit of an issue that open AI decided that chat GPT-4 should be given no details about whatsoever and that it should be kept as proprietary as possible. Yes, they are claiming that it's risky for it to be open source because of AGI risk, but are you really gonna say that we should just all trust you and that just one company should have access to this technology? Is that gonna end well? But next cloud is trying to address that in my opinion in a very nice way. So firstly, next cloud is kind of the same as Google Workspaces and Microsoft Office 365 suite, so it's trying to give a complete suite for companies to do company stuff. We've got shared documents, shared drive, and a lot more applications like the calendar. Very interesting things. And then of course, they are now releasing their hub for which includes AI tools for everybody. So nobody's gonna be left out, thankfully enough. So which AI tools we're going to dive into shortly, but of course the question is, is that gonna be a problem? Like, are they using something that's very close source and stuff and not the yarn? In fact, they have created some sort of a thick commission that analyzes these AI tools depending on the availability of the model, training data, and of the original code to give some rating. And then the user can actually choose whether to use or not at all, depending also on this ethical rating that next cloud gives. And I do think that this kind of things really try to push forward for something that is more open and such. I do think that will get more open source or at least available for everyone AI tools in the future. So what are they including actually? First of all, we've got Whisper, which is made by OpenAI. It released just this September. And in theory, it should be speech to text, almost as good as humans would transcribe something, which is extremely useful. You've got any document or any text field within a next cloud app. You just type slash, you select speech to text, and then you just start talking and everything you say is going to be transcribed within that document. That is generally something very useful. You can even choose to translate automatically to English only right now. And you can choose which model of Whisper to use because they do release those very interesting feature. Next up, of course, is GPD because who's not going to include GPD these days? And again, you're able to choose the model to use. You can give a prompt, see the result, and eventually insert it into the document. And in advanced options, you can even select to show more than one result. All of this is extremely useful in my opinion if you have to do some early draft to fill and or elaborate for more later on, but if you need some brainstorming or some template template real quick, then for me it's quite useful. Fun fact, I promoted a t-shirt some days ago, and the design was actually completely invented by chat GPD. I just asked what's going to be as narcole little fun slogan to support Linux, and they came up with it. It came up with it. Then we've got image generation through stable diffusion using Dolly, so still open AI for that.  Here's a tool to quickly find them, which is not what this feature does. This feature creates new images and does not find anything. Seriously, who thought of that?  I think this is useful for early drafts or to give some sort of reference to an artist to do the final work. So how are all of these features actually exposed to the user? Because it's very important that you're able to use all of these, especially the speech to text in all text fields. And what they are currently doing is inserting what they call the smart picker, which is a very interesting feature, which is basically the same that I think Google document has, but also Notion and probably lots of other people. That is, if you press a slash, you're able to select a lot of things to choose from to insert in the document, such as speech to text or image generation or text generation. But also, very interestingly, we got maps. You can actually see an open street map dialogue, you can select a place, and that place is going to be added to the document. And if it's a text document, you can even embed the whole map inside of it, which is super useful. And you've also got GIFs, obviously, but also videos through PeerTube. And my videos are on PeerTube, so you can actually search for me and my videos should pop up, maybe not, but who knows, and more because I forgot. Yes, there is one feature which I don't see they use case for, but I guess it's nice to have this feature. That is, you're able to search through actors and movies and series the movie database. Okay. It's, I guess, very nice to have it handy. I don't see the use cases of it, but it's good to have. So along these smart picking features, there's also a new application which is called tables. Now I've did a bit of research and it seems like table already existed since one year ago. So I guess the thing is now it's part of next cloud, maybe they upstreamed the work or something, but it's very interesting. They market it as an alternative to Microsoft SharePoint, which it isn't to be clear, but it does allow you to create tables. You can create a lot of columns, you can give a different type of data to each column. So you might have one column that's text, one that's numbers, one that's checkboxes, one that's a slider, one that's a date, this kind of things, and then you just insert rows. And, and this is very important, you can now insert a table within a document using the smart picker. So everything actually clicks together in such a nice way. I know I don't quite sound very excited about this year, but when I first read about all these features, I was like, you know what, I should try next cloud. Maybe that sounds actually pretty useful. So what else they do have a next cloud talk application, which allows for video conferencing as well. And now they've added a possibility to record some talks, which is extremely important, because a, I guess, if you do like a lecture, you might want to record a lecture for students that weren't able to go there. Or in general, if somebody misses a meeting, you might want to record it for them. But also, even if not for that, my mind is terrible. And I don't remember half, if not 90% of the things we talked about. So please record all the meetings and send them to me because I'm going to forget everything. Yes, I do take notes. I'm not good at it though. Another nice feature is breakout rooms. So basically, you've got a group of people and you can ask next cloud to either automatically or manually, or allowing each person to choose, divide these people in smaller groups, you can decide how many, obviously. And then each group is going to get its own video call. And then you're able to move people between groups and also send messages to certain groups. As an example, saying, okay, are you done? Every other group is done. Are you done? These kind of things. These are actually pretty common and I guess useful in some e-learning platforms. So it's very nice to have as well. And then there's improvements to next cloud file versioning.  Otherwise, if I mess up, I have to go the list of all the time and that I edited a certain file and try to find the exact revision I want to get back to. This time, I can just write like final, final, final, actually final. And then I go back to the revisions and search for that word. And it's gonna pop up so much, much better. Especially if you have to do that for a document that somebody else has made, because who knows what's on their mind. If they actually give names to revisions, which they want, then that's gonna make it much easier to undo the mess that they have done. So another nice thing is that now revisions are saved one for each minute in the last hour, one for each hour in the last day, one for each day in the last month, one for each month in the last year. Actually, the announcement just says and so on, but I guess it's like one for each year in the last decade, one for each decade and I don't know. This is very similar to what back in time does as an example with backups and it allows you to see all the changes that were done recently without having to have terabytes of storage to save everything that word was done each minute into the last few years. I guess that's pretty useful or decades or centuries. Now, all these AI stuff, yes, is very nice to have, but it does come with some compromises in order to actually deliver it so quickly, which I guess is why Microsoft and Google are taking more time with it. So firstly, all these AI tools are not able to have any context regarding what you are doing. So you cannot just pop up an email exchange and say AI, please summarize this email exchange unless you copied the entire thing and paste it inside of the GPT tool, I guess, which okay, okay. Also, you are not able to interact with the document as Microsoft is claiming to do, so you cannot just say, you know, automatically sort my files into subfolders. I think that would be an awesome use case to implement and it shouldn't like be impossible with the technology we have right now. In fact, I do want to digress slightly to talk in the end of what next loud in my opinion could do. So we currently have a proof of concept implemented for Unity that allows you to tell something to GPT and make GPT implement that something in Unity, the game engine, which is extremely interesting. How I guess this works is that Unity has some kind of public API and that they tell the GPT thingy to use that API to produce some code to do the thing that the user asked for. And that's a good idea, I think. And I even like tried to figure out how it could be done to Kelly Plasma because just imagine having a little application that you go by and say, hey, little application, please change my theme to dark and move my panel to the top and switch between the monitors and everything just magically happens. That would be awesome. That would be awesome. And I don't think that it's impossible. I thought that we can like take the API of Kelly Plasma and then allow the GPT thingy to search through the API upon request. You can tell GPT to follow some sort of syntax, which I think is what Bing does as well. So you can figure out a way for GPT to end up spitting some code that uses the Kelly Plasma API to change your desktop. And the issue that I encountered in trying to figure out actually how I would implement this is that Kelly does not have a good unique single API that allows it to do everything. And I would have to spend months just trying to make sure that the documentation is accessible to the GPT thingy just to have a documentation that is one page and easy to use. But I don't think it's feasible right now. But it's such an interesting way. And to be honest, I don't see why next cloud couldn't try to do the same thing. If they have some internal API, they could try to expose its documentation to GPT as again, I think others are doing as well. And by doing that, they would be able to make GPT make requests to that API to actually interact with your documents as you're using it. So that in my opinion is something that I'm really hoping for next cloud to investigate, or at least be able to optionally give some document to GPT, where you just, you know, say this document in the prompt, and this automatically gives some context about which documents you're talking about, that would be also extremely useful. And again, what Microsoft is claiming to be doing. So this is the way forward that I would hope next cloud to go on. I'm already very happy with what they're doing right now. But if they do that, then I'm going to switch to next cloud same day. Nonetheless, this was a pretty exciting features launch thingy. So whilst wishing my while switching the best luck to next cloud, I do want to say thank you a lot to all the patterns that are currently chipping in something to make this channel exist, because it's it's not completely free to maintain. I'm actually like paying a lot of things for the lights, LEDs, camera, editor. So if you're able to chip in something, I'm trying my best to improve the quality of the videos a lot. So thanks for following. And if you're able to chip in, I've got liberal pay people, passion, kofi, I've got tires, benefits, all that boring stuff that every YouTuber is doing. Come on, you know that. Bye.


Hey guys! If you haven't seen it yet, this came out a few days ago, I only just saw it now, and I'm getting massive, massive Killian frequency vibes. You guys have definitely got to check out the new Candyman Paper trailer. This is a trailer that was made by Manual Cinema, a production company that specializes in making very particular but very intricate shadow puppetry shorts. They've done music videos, they've done shorts, they've done little movies, and now they've gotten to do basically kind of the lineage, the history of Candyman. It doesn't reveal anything more than what has been previously said in either the 1992 movie or the trailers for the new film. It is good. The instant you hear the... It instantly brought me back to the feelings I had while watching the Creolean frequency, which if you guys haven't seen it yet, it's on Netflix. It's a very, very cool unique show, that kind of art style, that music, that off-the-norm but very, very gripping visual slash audio narrative. That is what this trailer is. It is super. It's not even a trailer, it's more of a short film. It doesn't say a damn word, but you get so much from its visual storytelling and the complexity of these puppets. If you go and look on some of the behind-the-scenes on manual cinema's website, you'll see that these things aren't just paper cutouts.  These guys know what they're doing. They're kind of doing a pseudo mixture of photo photography and shadow lighting. Some of the stuff they're using reminds me of the tabletop overhead projectors that my high school teachers would use for presentations and what not. It was really cool to see how it's done. So I would definitely check it out. It's got me even more hyped for the film, which has now moved from June to September. I've heard a lot of things about the Candyman movie. I've never seen it, so I admittedly will probably be seeing the original before I see the new film. Like I said, check it out. Also check out manual cinema's website. I've put both links in the description below. Either way, that's all from me. I hope you enjoyed this video. If you did, leave a like and if you're interested in more, subscribe, and like I said, go and check out that trailer. It's really good. I don't even know if you could call this a trailer. It's really a short film and it's a fantastically made short film. One of the coolest things I've seen on the internet in the last few months, so definitely check it out. Otherwise, that's all from me. See you guys next time.


Welcome, everyone, to our MicroMasters in Supply Chain Management Life event. My name is Seba Ponce, I'm the executive director of this program and also the director of the OmniChannel Research Lab at MIT, the Center for Transportation and Logistics. The MITx MicroMaster is a global, massive, online and open program. It's truly a massive program. We have more than one million enrollments in this program and learners from all over the world, more than 190 countries are represented in this program. So our mission here at MIT is to educate the world for free in supply chain management. But here we are also trying to provide you the latest trends and cutting-edge technologies in supply chain management. Today we have the privilege to learn about the AI role in supply chain management. For that, we have a great guest speaker, Dr. Edgar Blanco. Edgar is the VP of supply chain strategy at Walmart in the Centroid team. He has an impressive background in supply chain management, working for a wide range of organizations like MIT and companies like Amazon and Walmart. He's, I would say, one of the most knowledgeable people I know in this field and has been working in many different topics and areas like e-commerce, network distribution, carbon footprint, and last mile deliveries for more than 25 years. It's truly a privilege to have you as our guest speaker today, Edgar. Thank you so much for joining us for this live event. Eva, thanks a lot for your kind words. Thanks for having me and thanks everyone for attending this webinar and making time on their busy days to spend the next hour with us. And I really love this topic and I'm looking forward to our conversation. Excellent. Thank you so much. So just a quick theme for our audience. Please use the Q&A feature in order to bring your questions for our guest speaker. Okay. So Edgar, let's start by laying down some concepts and establishing, I would say, a common language for our webinar. Okay. So the first thing I want you to do is start by your definition of supply chain management. Great. Well, Eva, that's a great place to start. And since this is an AI flavor webinar, what I decided to do when you and I were talking a little bit earlier about starting with this question is that, you know what, let me do this. Let me put it to chat GPT. He tells us what supply chain management is all just bear with me. I'll just take a couple of minutes to read what, verbatim what chat GPT told us supply chain management is the process of planning, optimization and overseeing the flow of goods, services, information and finances as they move from supplier to manufacturer to wholesaler to retailer to consumer. The goal of supply chain management is to efficiently and cost effectively coordinate all these activities to ensure that products are available to customers when and where they are needed while minimizing ways and costs. It involves various components such as procurement, production, distribution, logistics and demand forecasting. And it plays a critical role in the success of businesses across industries. That's kind of the end of chat GPT. I think it's pretty good. I'll say I'll stick with that. Definition is pretty close to how the way I will do it and maybe next time we should add GPT to our panel. I think it will help us. I truly believe that humans matter now more than ever. So I think I am still going to invite you the next time. But I also agree that AI solutions definitely will enhance and augment our skills. So, but I was really surprised was really a comprehensive definition, isn't it? I agree. It's getting better. Okay, so I know you have been working in supply chain management for more than 25 years. And as I mentioned before, touching many different key areas in supply chain management like e-commerce and network design and automation, many different relevant areas. Could you tell us a little bit more about your passion for supply chain management and your experience, of course? Great. Thanks. Yes, I'm a total supply chain geek. I think I started my career in science. I did my PhD, applied for and when I started from the very beginning, since I was a kid, I always loved math and tech at that time, I guess. And when I was starting kind of my journey in my advanced studies, I was actually looking for a problem to solve. And I just found logistics. And at that time, I thought, well, it's a pretty straightforward problem. It seems like fun. I can do a lot of cool things there with the math ML. And a little, I knew that this would have become my career and actually has become my passion. And I'm looking to already 25 plus years and many more to come. I've been able to marry this passion with science, tech, then product. I have deep, deep respect towards people, operations that make things happen, teams that just coordinate and translate all these ideas that maybe science and math can point us to. And now technology increasingly part of it. And I think I've had a place to work with people that are brighter than me, at my side, in my team, alongside them. And just, I'm always at all on what we're able to achieve in this field is like magic. If you think of your day-to-day experience, you want something and you find it. And behind the scenes, there's all this network of information flow, et cetera, that makes it happen. And I'm just at all that I kind of made this my career. And I would love to say that I had a vision. They said, well, I want to end up alive, but it was not. But definitely I was very lucky to find all these things in one place. That's awesome. And I'm also happy to hear, glad to hear, that you love math. This is something that I'm trying to communicate to my teenagers, to my sons. So math is so important. Okay, so 25 years working on supply chain, contributing in many ways. And you have also observed the evolution of supply chain and how technology has been impacting many areas in this field. And now AI, artificial intelligence. So when we refer to AI in supply chain management, what are we referring to? Yeah, so let's kind of start level setting some of the definitions. And by the way, I would prefer to charge EPT. Probably we'll do a more structural answer, but I'll do it this time on my own words. The way I like to think about AI and I'm sure there's similar definitions out there is just a simulation of human intelligence. And I emphasize two words here, simulation and human intelligence. And so what we're, but this is all about is algorithm software, hardware that make machines kind of do things that we will usually associate to human intelligence. And since the word intelligence is so broad, then that also makes AI pretty broad. On one extreme of AI, we have these very specialized decisions. They're two pretty hard, very difficult and very impactful in our organizations, but they are very well defined, narrow. And on the other extreme of AI, we have these very ambiguous and, you know, decisions that encompass multiple dimensions, that tasks are kind of hard to define sometimes, but we all kind of know what I want to get out of it. And AI is trying to cover in some ways both extremes, which is makes it something hard to follow what people are talking about. But the first AI, the one extreme that is specialized is what, at least when I was starting, it was commonly referred to as machine learning, where you have a specific output that you're trying to do, you train a machine to know what is right and wrong. And then you use computer behind the scenes to make that decision more accurate with more data and better over time. And it's very powerful and complex. Now, the second time is what we're kind of talking about really here is that it's what lies ahead, is how can we just move out of those narrow, well-defined machine learning kind of models that mimic intelligence into really the more ambiguous, long-term, very complex decisions that supply chain management professionals and people face every day across the supply chain. So I think that that's, in my words, AI. So then what is AI in supply chain management then? So supply chain management is all about coordination, planning, dealing with a mismatch of supply and demand, making decisions all the time across all levels. And basically, AI is just bringing more of that ambiguous extreme capabilities into the supply chain management field. So this simulated judgment is just going to get more and more encompassing, which allow us to be used in different parts of the business. I really like how you frame it and how you relate AI with supply chain management using this data and human judgment at the same time and thinking in AI as a tool to simulate human judgment. I really like that part. Now I would like to dive more in that connection, this connection between AI and supply chain management. So since supply chain management is comprehensive and needs this coordination and also brings this complexity, that is kind of a common factor that we always have in supply chain management, what do you think are the top supply chain management areas and processes that will be more impacted by AI? This is the next question I know is a broad question, but we can see how we can structure that. Before answering that question, Edgar, I would like to connect with the audience and launch our first poll, because definitely we would love to hear from our audience about what they think about that. What do you think are the top supply chain management areas that will be impacted by AI? The poll is open now, so please take some seconds to answer the poll. While our audience is answering the poll, Edgar, let's start with your pick number one topic. Sure, good. Let me start before we look what the audience have to say about the areas that are more relevant. But again, I think the poll touched on those key areas. Again, you can slice and dice supply chain management in different ways. On one end, you have operations, on the other, you have demand forecasting, inventory, supplier, and customer experience. But let me start with automation, because this is one area that I think has been really advancing very fast. From the very beginning, just with automation, naturally moves into ML and increasingly into more advanced AI, more on the narrow kind, but increasingly into the more ambiguous kind. So again, robotics really, really continues to unlock capabilities in all those, I'll use the technical way, like nodes and arcs of the supply chain. Those could be warehouses, those could be stores, those could be fulfillment centers, those could be ports, or in the links between those nodes, planes, trucks, vehicles, drones, and so robotics and automation naturally is one of those areas where this activity is increasingly adopted in a huge, huge, huge value. This is true for Walmart. We have kind of launched tremendous progress in this area. I also made your comments around this automation. Again, allows for precision, also allows, and most importantly, at least from my point of view, has allowed the operators or associates, in the case of Walmart, that are in those nodes to spend more time in areas first are more fulfilling for them, but also that are more value added to the organization. So that's kind of one part in operations of AI. Again, robots are better at finding corners, identifying objects, needing less help to solve the task, and then the operators that run those buildings can spend more time on solving these complex supply chain trade-offs. But also an area that I'm super excited with in operations. And it's a think of these smart assistants that help those humans that are working in those nodes or those stores to figure out complex tasks. Again, have the privilege to walk into our newest facilities that are very large. They have lots of operations that we can do. And sometimes you can be surrendered and knowing what you do next or even asking for advice or training in a new task. Imagine these AI assistants helping our associates and the humans in those nodes and links to just be better. It's like having, you know, I remember every time I trained in the organization, I always get like an onboarding buddy, a person that I can be around and that I can ask all the questions that I have about everything. That kind of AI is very, very important and very powerful. And I think that's one of the kind of a little bit of a twist on operation AI improvement that I will expect in the future. Yeah, I can imagine to have a body trained based on previous experiences and helping the associates. This sounds really, really awesome. I'm very powerful to transfer knowledge because this is one of the big issues we have in big organizations to transfer this knowledge from one employee to another. Okay, so before you move into the second topic, let's try to have a look to the results. So thank you so much for filling out the poll. Okay, so 43% of our audience, Edgar, pick the month forecasting as one of the top SEM areas impacted by AI. I think we are fully aligned with that. What do you think? I love it. You have a pretty savvy audience. That's for sure. Again, the foundation, if you think of what we do in supply chain management, this coordination and this mismatch between supply and demand starts with demand. And usually we associate demand and all the process around management, demand creation, and demand management with forecast. So absolutely. I also agree, if I always think of the first area that will have any impact across the board and can propagate in an organization, especially in supply chain management, it's forecasting. Poor forecasts, as we all know in this group, adds friction. You have to deal with the uncertainty that you have your missed forecast. In efficiencies, you may need to carry more inventory, do more rebalancing, just because the forecast is not what it needs to be. And all those gains that you do in forecast quickly propagate and improve across the company. So I totally agree with the audience that that's one of the areas where I will think AI will be very impactful. And for that reason, ML has been extensively used, always to bring more data to help improve forecast. So it's a natural progression. But even though we've done tremendous progress and there's continuous innovations in ML, what I call this narrow AI, and I'm sure it's going to be more to come, everyone that hopefully in this webinar has had experience with demand forecasting, there's so much more we can do. I think the way AI will is going to permeate first. But it will be just kind of the same way, like more features to the ML model, more layers to a neural network, more powerful computers, all those are going to come for sure. But is this judgment piece that comes into forecasting that we have been trying to capture through this narrow AI view? But even anyone that has been in a meeting with an experienced demand planner, that planner, you know, you sits on a room, looks at some forecast data and quickly says something's off you. Why is this off? Not because that person has all the features, you have the experience, you know, he knows where to look, he knows what to do. And also is able to ask the top questions and highlight those anecdotes, you know, that I say make humans great at destroying all this narrow ML. For example, you know, we all know by human nature that if there was a rainy season or it's sunny, or if the holiday was on a Monday or a Friday, all those things we kind of know. And when a forecast doesn't match that into issue, we at least challenge the forecast. And most of these narrow ML models have a very hard time to bring these multiple dimensions. Sometimes I'm not featured. It's like these expertise. And I think that that is as AI gets more powerful, where I would think, imagine this demand planning meeting where when the forecast is presented, already comes with questions to say, hey, is this right? Are we seeing the same system, the same weather, not just taken for granted and have a prediction, but really have a conversation with an AI agent that helps you get narrow and better. So I think that's definitely most impactful and one that I'm also super excited about. Yes. And adding these judgment components into forecasting seems like a revolution, especially for those data-driven organizations. And we can talk later more about the role of humans here. You also mentioned those of you who are already working in demand forecasting, you should be, you are aware about this impact of AI in this area. For those of you that want to learn more about demand forecasting, I truly encourage you to join our supply chain fundamental course. We cover demand forecasting and the next topic we are going to be discussing now. So the second topic that our audience picked was inventory management. Edgar, what do you think about that area? Well, again, this audience is pretty aligned with some of my own thoughts. Again, I spent a lot of time in demand and forecasting earlier in my career, but lately I've been personally spending a lot more time on inventory management. Again, inventory management, one of the key inputs, of course, is forecasting. So I'm always keen to innovate in that area. But inventory management is once you have a forecast, good, right, mediocre, excellent, you still need to make decisions about flow. Where is the inventory? How do you move it? How do you make the trade-offs with limited capacity? And how do you position it? And this is so rich and complex that here's what I also think that an AI engine is going to help guide action and I think will be one of the game changers as well in this revolution. Again, on one end, we have the sophisticated models that can predict safety stocks that can find optimized trucks in terms of order sizes, etc. But in this field of inventory management, of determining where to put inventory, how much to carry, how to plow it, and this is like, I was the other day doing some math and I was looking for how many options you have most of the time to explore and look around and these are like quadrillion of options. And machines are great at estimating many of them as a quadrillion of options to monitor, track, improve. And in that quadrillion, you need to find similar to what you're talking about forecasting. Are we having a risk in one item, in one FC, in one item, in one store, in one item, in one input center? And if we are low and there's risk, why is the risk we should worry about? It's something we should take action. And if you think of how we work today with technology on inventory management, we have models that predict and give us recommendations and then we have people that look at those recommendations and kind of inspect them and similar to demand forecasting. But in here it's exponentially more complex because you have a multiple geographical difference, but also time difference where you need to take action. And I think that's where AI is going to transform this function. And people that work in inventory management today, it's also spent a lot of time on auditing the narrow AI recommendations. And they don't have a lot of time to actually take action as strategically as they want to, just because of the amount of time they have. Imagine you have this AI engine that will help you pass parts of the data, point you to directions, maybe make recommendations to look for some other things, get more information, and then learn from the loop on a regular basis. Then it will just make inventory planners across the organization to truly be more focused about the customer, the strategies that will drag customer experience and all the auditing and risk management will start to be powered by these AI machines. So I think that that's definitely, in my mind as well, the second layer of impact in supply chain. Yes, I fully agree on that. It's not only to know the inventory management models that you need to know and the inventory policies. It's also to see how you can translate this context into the code and into your models that is sometimes the toughest part of this. Okay, okay, perfect. So looking to this comprehensive way of supply chain, we also need to look at suppliers. You also mentioned a little bit of customer, but what about supplier management? How do you think is going to impact upstream the supply chain? Now it's kind of maybe not on these two other that I also think are very impactful where AI is going to continue to improve. One is supplier management, the other one absolutely customer experience. I kind of left it last a little bit, but we all know the customer conference and everything we do is just for that customer. But I'll explain maybe why I put customer experience last in at least for this webinar. But supplier management, as I said, when we talk about operations, we talked about demand forecasting and then we talked about inventory. Those three tend to focus within our kind of what we think of within our control in some way. But supplier management and customers outside of it is where AI is going to help us be better partners with our stakeholders outside. And I think that those are again very, very, very important processes in our supply chain management domain. And let's start with suppliers. Suppliers regularly observing their performance is what we do. Finding opportunities to find joint value, changing the way we receive products, the way packaging is being designed, potentially new product development cycles. And if I look into what ML, our narrow AI has come, it's kind of pretty narrow. The impact that we have been able to see. Again, there's always great risk management forecasting engines out there. But supplier management is a lot more about these relationships. And the relationships are very, very ambiguous. This is where exactly one metric is not great. We have performance cards, but these performance cards are augmented with the contents of the supplier. And I think that's where machine learning has been probably helping on a few of those or identifying disruptions potentially, but it's not even close of what we need to transform this field. So I think this is a big opportunity, but I don't, again, since we have not done the first wave of narrow AI really influence here, we have maybe a little bit longer way to go on how we're going to incorporate the supplier in supplier management. But again, this is that ambiguity is exactly where AI could help. How do you bring the context of a supplier relationship with you as you think of how to improve your inventory flow, your purchasing, etc., etc. Again, people who have a big part still on this, but how can we bring AI into this would be a really something I'm looking forward to see. And in that case, it's not only the context of your organization, it's the context of your stakeholders, which make the problem even more complex. Okay. And last but not least, customers. Yeah, I left a last because probably a lot of people in the audience hear a lot about customer experience is constantly on the news. Again, we're all customers, we all get impacted by technology. We've seen the examples on how this already changing, you know, we talked about child GPT on how you look for information. We all have so have familiarity with assistance in our homes. And again, those same assistance have been kind of naturally projected into why you work in a store. And you have an assistant that help you, hey, help me find the items that I want to make my lasagna, right?  And all those parts of customer experience are super critical. And again, continue to invest. I use them last, not because they're least important, mostly because we all have maybe seen them already materialized, because that's also a very broad use. We have return management. Every time you have returned a product, and you need to get into a customer service agent, we all know how frustrated it is to have to wait for 10 minutes, you know, and go through 25 prompts and get to a person. And then that person has the information you need about you. And you have to give it again. So if you think about that, that's definitely moving very fast. I've been interfacing quite successfully with chatbots that help my queries. Initially, I wasn't really skeptical, but now I am increasingly relying on them because they do have the information. I don't have to wait online, and I can get 85% of my queries resolved pretty quickly. And the more conversations comfortably though, the experience will elevate. And I think this is true for Walmart, it's true for every organization that has a customer facing. And this is the same if you have to be a B2B as well. At the end of the day, where all people will want to help organizations and having a good customer experience is paramount. So again, this is an area that is, for obvious reasons, very advanced. It's widely used, especially in OVNI and e-commerce. And I will just continue to expect innovations. I put it last because we're kind of on the supply chain inside organizations. Yeah, I truly believe that these type of AI tools are going to impact customers' omnichannel experience, but not only online, but also in person. For example, with the virtual dressing rooms, offering items that are customized to customer preferences while they are trying garments in the store. It's another big thing that I think we are going to see a lot of progress in that area. And as you mentioned, impacting omnichannel and e-commerce. Okay, so that's great because I think we covered the key areas in supply chain and how AI, do you believe, is going to be impacting these top areas and also with some vision to the future. Let's try to move now to the key considerations because there are key considerations we need to take into account before implementing any of these AI tools. So what do you think are these keys for a successful AI implementation? So absolutely. I think that's a great, great kind of follow-up of our previous discussion. We can dream the future, but the most important thing, first and foremost, is really people. And we, again, when we talk about technology, we always get carried away with what it could do, the power of codifying this thing with intelligence. And of course, that's wonderful because Ghani has very, very far. But as we move from this narrow AI to more general AI, we are moving from tasks that are well-defined to organizations and people. And what we do is all about people. For example, at Walmart, we're very proud of being clear that we're people-led and tech-powered. Similar with AI. AI should be people-led. And people will come first on technology next. If you kind of go back and see the way I think about these examples of AI, I always think of AI as enhancing the decision-making capabilities. That means that the first thing that we need to think about is the people, as we think of these technologies, and how they're going to be impacted. And at least from my point of view, not in the same way of narrow AI, we're training a machine.  We all welcome automation. We all welcome efficiency. Everyone that I always was kind of reflecting the other day, we tell my colleagues, you know, of course, I'm used to look at the physical maps and passing pages, but I enjoy more the scenery. And having a map that tells me where to turn left, turn right is much more fun than just looking at a piece of paper. So again, this makes our experience better in whatever we do. So humans remain the loop and are the center of our efforts. That's kind of, for me, the first key of success. Without that, I think we would be one of those dystopian futures that we're going to get there, but it's just avoid that journey. The second one, again, once we talk about people first, you're always going to have to talk about ethics. And that's in the news all the time. And it's super complex, because we're going to bring simulated intelligence into our team. That simulated intelligence needs to somehow encode our values in our organization, whatever the organization stands for, the mission, that makes you make different judgment decisions, very different based on where you are and what your field is and your area of business is. And the best way I always like when I think through this, and you heard me in Matthew's example, think of a new person you hire into your team. You're getting great technical training, you spend a lot of time with them through their induction to share the values of your organization, because you believe that that person needs to understand and for them to do their role right. As we go into this complex AI, we need to somehow bring that into those AI assistance. It's no different than having a new associate or a new person joining your team. You will also, that means you will need some sort of AI ethical office. I mean, we still have them. It's not that we don't trust the people that work with us, but we just want to make sure that whenever they have a certainty, they know where to go. And then we have a debate and what is ambiguous, we just make it clear for everyone. And then we just continue to progress and stay in the right path. That same rigor needs to be in AI. And I think the other one, it's related to transparency. One reason we love to work with people is that we can talk to them, we know who we're talking to, we can give them feedback and they can take it and get better. And it's not, again, it's not different with AI. Simulated intelligence, simulated judgment. You need to get people first, clear, I think of guidelines and just be, have those clear ways to get feedback and have transparency. It's not about kind of telling you how I build the engine. It's not about giving trade secrets of within the AI. It's about the respect we should have with everyone that interfaces with this technology. Our employees, our customers, our partners, our suppliers, they, if we are going to bring this technology, we should bring them along in the sense of telling them what we're doing, asking for their feedback so it gets better. That way we build the trust and we can then train these AI systems to get better and do our job better. And then last one, this is not like a deploy and leave kind of situation. If you get into this journey and everyone that has done even the narrow AI implementation, these AI systems should be monitored continuously. But you need to make sure there's no biases that you can usually get in without paying attention. Talk about the compliance and the overall behavior of the system. Those systems should have the ability to be expected and audited. Like we do when we go regularly through our organization in any function that has impact. We just make sure that we are able to ask questions and the system should be able to tell us. So I think that those are for me four key considerations. And again, there's always a list of things you expect in any technology problem. Make sure you have a clear objective, make sure you have the right security, you have the risk assessments that you do continuously planning. I think those are common in technology and big deployments of any change in organization. But the four ones that I mentioned above, I believe are key specifically for AI. People, ethics, transparency, and just continuous monitoring. You need to maybe this journey we're in. Yeah, it's interesting because last week I attended a conference and we were discussing about the leadership style now. And definitely people first, it was one of the key takeaways, but also living based on values. And this is now more important than before because, of course, we need a KPI and performance metrics and still needs to be in place. But the leadership style needs to focus more on values than on the performance metrics by itself. So very good points, Edgar. Let's try to move now more into the existing challenges and common pitfalls when implementing AI because it also exists. And there are many. So let's try to cover that area now. Yes, I think that if you think of the opportunities and the key things you have to consider, those are usually your risks, but I think summarizing a couple of different bullets. One is people first. Never forget that. Get your ethical compass straight. Once you have an AI system in your organization, it will make you verbalize that. And I think that is the other one. And in this journey, we have to be very humble of what we know and what we do not know. Bring the right talent to help you in the journey. I think that in every organization, having part of being humble of what we know and don't know about this technology is important. Otherwise, you will just get the tidbits you get in the news and bias your own journey based on what you see everywhere that gets the headlines. And that's not always what matters the most. So be humble. And by bringing the right people and the right talent, not because you don't know what you're doing in your business, because these questions that I brought up in terms of ethics and people and inspection and transparency are both technically challenging, but also require the right orientation to be able to deploy that skill. And complexity will always be a case. Again, none of this, even the narrow ML everyone that has gone through a journey of bringing a new system of forecasting or inventory knows it. You can make it as complex as you want. The more complex, fantastic, harder to inspect, takes more time. So things are pretty complex the way they are. So you don't want to make it even more complex by taking all these kind of areas at once. Be humble, experiment, don't get carried away, but at the same time, we all need to bring value. So that those kind of four things for me always in the forefront, people first, never forget that. You're going to get the corporate strength. Be humble.   Definitely, you want to show progress. You want to get them excited. You want to deliver value, but this is pretty complex, even the way it is today. Imagine all these visions that we were going to discuss. Yeah, and connecting with the complexity part that you mentioned, certain tasks such as routine and repetitive tasks are the most likely to become automated. However, these complex tasks that you are bringing to us today are the ones that I think will be augmented by AI. And supply chain problems are great examples of how AI can enhance those tasks and augment it rather than replace those tasks. So, yes, and we also need to look at how companies navigate this technology world. And I like the way you frame people first and values. Values matters more than ever. So now, Edgar, looking at the future, how do you see AI augmenting human skills? Because you have been discussing a lot about the importance of human in all of this new environment. How do you think it's going to augment these human skills? Yeah, it's a fantastic question. AI is about augmentation, augmentation of our potential, our potential of humans, you know, so we can do more. And it's always hard to verbalize this in terms of technology. So what I prefer to do is is kind of to verbalize it. This is kind of the dream I have as we single is come in and help us transport, envision that everyone in your company is able to hire these fantastic associates out of college, super bright, fast learner, great potential, and that is eager to get guidance from you.  And then you can bring this associate to every meeting. And in that meeting, you can ask the AI associate, the AI technology, to pull data for you, show me this, bring me this, and even better, it will come with some kind of things that it will ask for you to weigh in and to help you understand in that meeting what you need to decide. And if for whatever reason, it brings you something that is not relevant or points in the wrong direction, you can put the agency back. And it will quickly adjust, forget, recalibrate, no hard feelings. In mind, you have like this sidekick by your side everywhere you go. How efficient will your meetings be? How much more time you can spend in the more even more complex and the more you teach that AI what matters to you, the more efficient you can make it.  And the more you're able to cross train this AI, the more things you can do together, same language, same, same decisions. That is the vision. That's the technology we need to aspire towards. But you need to be a good mentor to this AI as well. You need to be better at your job than you were before because you're mentoring this new system along your journey.  So that AI is looking to us to guide you. You need to be then growing all the time. Because as you teach and the AI knows how to do things that you probably need to spend more time. Now you have time to think around the corner. And then point the AI, and this is the journey of business, the journey that I look forward to be part of. I think this is the mental model we should take. It's not about the techniques. It's not about the neural network. It's not about how many layers you have. It's how do you establish this journey of communication? And maybe you're associated, maybe wrong. And it's okay. You are there to supervise them and help them and then grow and grow and grow. And this is the journey, I think, of augmentation that I envision. Hopefully we'll get in this in my lifetime. I would say it's the journey and the mindset. We need to start the mindset. So this is truly a changing and very dynamic environment. And we need to prepare for that. You mentioned that we need to mentor on that. We also need to prepare our students for this future. And we need to prepare the future supply chain professionals to be competitive in this context. So we are almost wrapping up. Edgar, what are the key skills you want to see in the future supply chain professionals? In addition to knowing what you need to do, and I think that that's always the starting point, you need to be AI savvy.  Anyone that comes to your attention now is going to be a key part of adapting, evolving, and embracing this Azure in the right speed with the right mindset. AI savvy in one. Critical thinking is always going to be even more powerful. The more powerful you have systems that help you and amend your capabilities, the more critical thinking is needed from you to help disambiguate even more complex and more impactful decisions. Again, people first. I see people, people solving problems. So you always have to communicate. Communication is critical. Supply chain is all about coordination. And even with the most powerful technology helping us out of our skills and our AI assistance, we're still going to be in control. And we need to communicate and convince people and persuasion. And I think that's going to continue to be paramount. So AI savvy, critical thinking, communication. And as I have learned in my own life, lifelong learner, I'm obsolete every month pretty much, you know, so you need to have that attitude of learning. How do you organize yourself to stay current? And to continue to help the organization, you learn within the job for sure, you learn around the organization, but you need to have that lifelong learning attitude. It was hard, at least maybe my I'm glad to hear you mentioned that lifelong learning is key. And I want to launch now the second poll and see how our audience is trying to keep up to date with all of this technological revolution. So this is a trend we are also observing at MIT, the Center for Transportation and Logistics. More and more companies are coming to us and they are asking to help about how they can train, how they can upskill their associates. And I truly believe that online education is an effective and very powerful way to help the industry and society in this endeavor. Thank you so much, Edgar. Thank you for sharing your experience with our MicroMasters in Supply Chain Management community. It has been wonderful to have your brilliant mind sharing your insights about this relevant topic. I'm going to share the last poll because I'm very happy to see that most of our audience are planning to take the MicroMasters Program in Supply Chain Management to upskill their knowledge in supply chain. That's great. And remember lifelong learning is key. Stay curious and don't forget about the importance of upskilling in supply chain management to remain competitive. Thank you so much, Edgar again, and thank you everyone for joining us today. Thanks to the team behind the scenes. Thank you. Thank you, everyone.


Hi you guys, Lindsay here. Welcome back to my channel, Inside to Him. All right you guys, how are your dresses coming along? How did your sight seems turn out? What did you think of French seams if you had never made them before? Today we are going to be working on steps 16 through 24, which means we're going to be making the neckline drawstring, the little thing that goes through the neckline here to basically hold your dress onto your body, and then we're going to be doing the waistline casing, which will give us this little cinched in waist look. Now if this is optional, so if you didn't choose this for your dress, then steps 20 through 24 are not going to apply to you. Like if you're making A or B or any variation of length just without the waist line, then you don't need to worry about the waistline casing. That's just for those of us who are making the little cinched in waist definition dress version. All right, so this is going to be fairly straightforward. I think today we should be able to zip through this, but we need pattern piece number three. All right, so three is just a long rectangle. Looks like this. Let's move this out of the way. Keep in our instructions closed. Okay, so yeah, we've got pattern piece three and you can see here that there is a line that goes from one end all the way to the other. So we're going to need to make note of that line. There's also a little notch here on the end, on one end, but not the other. So our very first step is going to be to sew the two rectangles together at the notched end. So this end here that has the notch, we're going to sew them at a 5 eighths inch seam allowance. And we don't have to worry about finishing off the side seams or anything like that because this is going to end up being encased whenever we end up folding this and making it into like a like big tube basically. So the first step is to sew this at a 5 eighths inch seam allowance. All right, so I just put a couple pins into the edge there to keep it together, especially because my fabric is a little bit shifty. And then I'm going to line up the edge of the fabric with the 5 eighths inch little mark on the throat plate and drop my needle and sew it. Super simple. Now we're just going to press this open over at the iron. Okay, while we're over here, the next step is to fold this rectangle, what is it? Right sides together along the long end, like so. So you're going to have one long like skinnier rectangle. And I just want to press it very, very lightly. Again, because I have a shifty fabric, I just want to make sure that I can keep it as even as possible. And pins don't always do the best job of keeping it that way. So I just want to give it a very light press. I'm even going to knock my heat back like almost to off just to make sure I don't put like an actual crease in it, but that it kind of holds the little press that I want to put in. See what I'm talking about? It's not like a full-on crease. It's just a very gentle, gentle press. Okay, now that I've got that gentle press in, I am going to take my pins and I'm just going to throw a couple of pins in here just to try and keep that edge closed. And then we're going to head to the machine and sew along this raw edge at 5 eighths of an inch seam allowance. All right, so like I said, we are going to be sewing this raw edge at a 5 eighths of an inch seam allowance, but we're also going to be closing up the short ends as well. So while we're sewing, we need to leave an opening somewhere in here that we can reach in and pull it out, right sides out. So what I like to do is take, I don't know four or five inch little section, put a pin in vertically like so, and then put in another one. And this will just remind me of stopping and starting points. So I will sew this along this edge, then turn and start coming all the way down this raw edge here. And then when I get to my vertical pin, then I will back stitch and I'll stop sewing. And then I'll start again here back stitch and then continue sewing this side the same way. All right, so I have half of it sewn. I stopped at my vertical pin. I'm going to remove that and then I'm going to skip over this whole area. All of this is going to be open, you know, not sewn shut. And then I will start it here at this other vertical pin, drop my needle back stitch and sew the other side. All right, there we go. We have our little casing or a little drawstring sewn, except for this little section here. So let me take you back over to the table. We'll trim this and turn it right sides out. I'll show you some good ways to do that. Okay, we are at the very end of step number 17 where we trim these seam allowances. And basically you want to take your embroidery shears again or very small sharp scissors and you want to trim these seam allowances down to about a quarter of an inch. Okay, and when I get to the area where I have left the opening, what I'm going to do is trim that, but then I'm going to turn back into the seam allowance and leave the full 5-8 seam allowance for the opening. And that's just going to be easier for us to turn that in later and sew that part closed. So it almost makes this like, I don't know, plateau looking thing? Hello Earth Science. Um, so that'll make it easier for us later on just to leave that all that seam allowance there. All right, so we've got all those seam allowances trimmed now and this is what your drawstring should look like, something like this. Now we need to pull these little edges out through this little opening that we left. And for me the easiest way to use that is to do that is to use a bodkin. It looks like this minus metal and it has a ring on one end and this little like, I don't know, thingamabob. A little mermaid reference on the other end. Link in the description box for where you can find one of these. For one I remember they're very affordable. I might have had this one honestly since I first started sewing. I think it came in my like beginner sewing kit, but you basically pull it all the way to one end, then you work that thingamabob into the corner of the fabric, trying to hook it. It takes a minute but there you go. All right, so now we have that and we are going to pull the fabric around without it coming loose. Let's try again. Try to be gentle to not leave like a gigantic hole in your fabric, but at the same time you need it to be strong enough to hold the fabric on the end. There we go. I think I just felt it lock into place. Yep. All right, now you're just going to pull the fabric over the end of the bodkin like so. Being very gentle, you do not want this to come undone halfway through. And then once you get it through the other end, take the bodkin out and you can finish the job yourself. And what you're left with is a little number like this. Try and tuck that little, some threads come out. No big deal. Just try and tuck them back in and try and make this end as square as possible.  You press the seam allowance toward one edge, which is why we didn't want to put a huge press in the fold line because we need it to go the other way now. So you don't want to be working against yourself. So do the other end and then go press out that, press out the drawstring and then we'll be back here. All right, you guys, now we are on step 18. And 18 is going to have us close up our little opening that we made. So you have to turn these seam allowances into the casing, into the drawstring like so, like so, have it all even and all those turned down like so. And then it's going to have you hand stitch these closed like with a needle and thread. And I don't know about you, but I love hand stitching. And if you note, since I left my opening very close to the center of this drawstring, there's a really good chance it's going to end up inside the front casing. So technically, it'll never be seen if I do all of this correctly. So I'm going to very neatly and gently go to my sewing machine and just put in a couple of stitches, stitch along this edge and stitch it closed. It goes by 1000 times faster. And like I said, I'm sure you'll never see it. But if you're worried about being it being seen, feel free to grab a needle and thread and go ahead and whip stitch this little guy closed. But I'm going to the machine. I am going to go to the iron first though, and press this so that it stays nice and even in the machine. And then I'll stitch it closed. And then after that, we're gonna be feeding it through the dress. So we're getting very close to being able to like try this on, which is a lot of fun. So I'll meet you back here. All right, you guys, this is a very, very exciting time in the journey of making any garment where we are going to put this drawstring into our dress and then we'll be able to like actually try it on and see sort of how it's going to look. So the easiest way to get a drawstring through a casing is to use a safety pin. Safety pin, like so. You want to find the front. Okay, we are going to be feeding it through one side of the back first, then through the entire front, and then through the other side of the back. And I like to pay careful attention to like a right side and a wrong side. I know it's hard to see maybe, but there's definitely one side of this that looks better than the other. On this side, the seam is visible, but on this side, it isn't. So I want to make sure that when I feed this through, that side is the side that is out. So it's not like this. It's like this. So when I get my safety pin, we are simply going to put this through one end, like so. Now we have a safety pin on the end of our drawstring, and then you simply start feeding it through the casing. Again, similar to using the bodkin and like pulling the fabric, the tube right side out.  Now try and keep it flat and pull it a majority of the way through. Something like that. Maybe leave, I don't know, what is that, two feet? All right, keeping it flat again, flat, flat, flat, no twisting. Turn your whole garment and then feed it through the other way. I mean, feed it through the front. All right, and once you've got it through the front, then take your safety pin and put it through the other side of the back. You'll be heading from the outside toward the center this time. All right, now it's just a matter of finagling this whole thing so that you can take your safety pin out, so that the drawstring is evenly distributed. Sometimes they'll have on the pattern piece, like dots or something, where it will mark like the shoulder seams and stuff, but they don't have that on this one.  But I really just want these strings to be somewhat even, as well as this distance here, the part that's going to be on the shoulder. Just fuss with it a little bit, it's not going to be perfect at this point. When you try it on, it'll be a lot better. That's closer. All right, cool. Very good. So you can see they have you insert the drawstring through one back casing, through the front casing, and then exit through the remaining back casing, having ends even, which we've tried to accomplish here. And then when wearing dress, pull up drawstring and tie in a bow in a back, distribute fullness evenly on front and back. So there will be an element of scrunchiness in the front too. So keep that in mind. But if you'd like, go ahead, try this guy on, get all of this distributed how you like it, and then you can even take a needle and thread and sew this down from the inside, so that it stays even after you wash it. Because inevitably, if you wash it and it's not sewn down, then the drawstring could even come out, certainly may not stay flat, all of that. So try this on and finagle this drawstring as best you can. All right, I have thrown this onto my dress form. Let's give us some more light. People are always asking me if it's worth it to get a dress form or not. And quite honestly, most of the time, it's not. But in cases like this, it can be really helpful because it's just me here. I don't have anybody to help me get this centered so I can use this as like a jumping off point. It's not exactly my body. I mean, it's close, but you know, nobody is this symmetrical. And then when I put it on, make any little last minute tweaks that I need to make. But basically, I just tried to even this out as much as possible and scrunch it all together and yeah, just try to make it as pretty as possible. And then I will throw some pins into these areas here. And then I am going to stitch those closed just because I don't want to keep doing this every single time I go and wear this dress. All right, you guys, we are moving on to step 20. And again, this is optional. This is only if you want your garment to be cinched in at the waist. If you don't, then just stop the video now and catch up with us next week when we will be going over all the finishing details, including the hem of the dress. But if you want some waistline waistline definition, then add a little casing.  Looks like this. It looks like I got purple to match the outside, but certainly I could have done white on the inside since this is kind of white. I don't know. You can also do contrast if you want. It's your dress. You can do literally whatever you want. So if you look at bias tape, basically what it is is it is a piece of fabric cut on the diagonal where it has the most stretch. You can see it kind of stretching out a little bit there. And then the raw edges fold it over. This is what's known as single fold bias tape. If it were double fold, it would be folded again like so. That would be double fold, but we need single fold because we're going to be making, we're going to be using this to make the casing. We're going to be feeding the elastic through this little funnel type thing that is created. So step 20 is actually very helpful tip. And that is to sew down your seam allowances. If you did not do French seams, I do recommend doing step 20, but because we did French seams on mine, I don't think it's going to be that big of an issue. This is basically just to prevent like when you feed the elastic through, you're going to use the safety pin method again. And sometimes when you're feeding it through, it can get stuck here, you know, and not want to go through. But I don't think it's going to be that big of a deal. So I'm going to skip it, but this is a really helpful tip. So feel free to do step 20 if you want. I don't know that it's going to be that big of a deal for me with the French seams. So I'm going to go to step 20. And that is basically to take your bias tape. I'm apparently unwinding the whole thing. I will go to the iron. I will press this out because it has all of these little creases in it from being folded in the packaging. And they want you to start on a side seam. Remember the placement line that we copied over? There it is. And basically you start at a side seam. You place the lower edge of the bias tape along that line, like so. Try not to stretch your fabric, but also not have it bunched up either, right? So you place it all along there. You will add pins and then you will stitch very, very close to one edge and then very, very close to the outer edge and then we'll feed the elastic through the middle. That's the plan. So you take the edge and you just tuck it under like that. Place that along your side seam, like so, and then just start pinning from the underside. Place it on the lower, place the lower part of the tape on the placement line, and then just start pinning. This is going to be one of those things that you really, really want to take your time with because you don't want this thing to be crooked. If it's crooked, then it'll be crooked on your body, trust me. So really, really take your time. Like I said, go press out all of the little creases. Place this exactly on the lower edge and then I'll meet you at the sewing machine and I'm going to show you pretty much a full proof way of getting that stitching very, very close to the edge of the bias tape very as easily as possible. Anyways, so I'll meet you over there. With most sewing machines, you should get a foot that looks like this and it's used for a blind hem. You can see it has a little like a little itty-bitty piece of metal kind of going through the center of it. That is the foot that you want to use when you have to edge stitch something. An edge stitch is really what it sounds like. You're stitching something very, very, very close to the edge, which is what we're doing with this bias tape. So find the part of your garment where you started the bias tape. For me, it's right here. I like to put the hem side in, but that's just personal preference. I'm also going to move this camera out of the way a little bit. You want to put all this in here and you want to make sure that everything is flat and that there's not anything else bunched up. There's a lot of fabric here. You know what I mean? So just make sure that it's not bunched up.  Alright, now you're going to place the presser foot where that little piece of metal is right on the edge of the bias tape and the edge of the bias tape is right along the blue line. In theory, where's my blue line? Oh, there it is. So as you're sewing, you're keeping this, you're keeping this edge on the edge of the bias tape and the edge of the bias tape on the line. It's kind of a lot. You might want to hang back your speed and go real slow. You also need to move your needle. You also want to move your needle to where it's just on the very edge of that bias tape. I'm going to leave mine about that far. I want to say it's like, I don't know, 16th of an inch, maybe closer? It's very, very close to the edge, but I'm going to go real slow and really take my time with this. I recommend you do too. This is easily the trickiest part. Take a deep breath. It's going to be fine. I can't think of anything else to say about it. You just kind of got to go. Trust yourself. Don't forget the back stitch so that it stays in place forever. Again, I'm just keeping the middle bar that runs through the middle of this presser foot right on the edge of the bias tape, and then I'm holding the bias tape down right along that blue line that I have. If you are very, very new to sewing and this part is freaking you out, then I would recommend basting all along the center of the bias tape just to hold it in place, basting it to where the edge of the bias tape is on the blue line. That way you have one less thing to worry about. You know the bias tape is in the right place. This, I'm trying to kind of manage a lot of things at once, so if you want to eliminate some of those, then go ahead and baste this down where it needs to stay. Kind of readjusting as I go, making sure the bias tape is always on the edge of that blue line, and I'm going to knock it up a little bit. I'm feeling a little more confident. I caught a little bit of the fabric when I started this seam, so I'm just going to unpick some of that. Alright, and as you're getting close to the edge, you're going to want to trim back the edge of your bias tape where we left that really long tail. We obviously don't need all of that anymore, so I'm only going to leave about 3 1⁄8 of an inch, and then I'm going to fold it back like I did on the other side, making sure the bias tape lines up nice and pretty, and then finish off the seam. Okay, and you can see here what we've got. We've got one edge of the bias tape completely sewn, and then the folded over edges kind of sort of meeting. Not exactly perfect, but now we need to turn the garment around and do this exact same thing to the other side, or because we're using this funky little foot, what you can do is just move that little middle piece, that middle guide, move that to the other side of the bias tape, and then move your needle to the other side of the guide. Makes sense. That's why I love this one so much because, A, it keeps it nice and even. I can even stitch all the way down, which you can see here. It looks like pretty perfect to me, and also you can move your needle around it, and the fabric always stays in the same place. Now, when you're sewing this, you just want to make sure to keep this nice and flat, to not stretch out your fabric in any way, and just let everything lay nice and flat against the base of the machine. We are all done. It actually looks really cute in the lavender, but you can see how even all my stitching ends up being by using that foot with the metal guide in the center that you can use. Also, just full transparency here, you know. It's not perfect. I imagine yours probably aren't either, so we will all commiserate on what it looks like on the inside that no one will ever see except for us. Yay! But what you have here, if you realize it, is a little tunnel. Can you see how the tweezers are going inside this little tunnel? So we are going to be feeding a little itty-bitty piece of elastic all the way through this tunnel, all the way around, pulling it tight against our body, or as tight as we want it to be, and then you have a cinched-in waist. It's perfect. So let's head back to the table, and I'll show you how to thread the elastic and measure it and all that stuff. All right, so the elastic that you need for this is 3 eighths inch wide. I like to get the braided elastic whenever it's going inside of a casing. They have all kinds of different kinds. I've been thinking about doing a video on the different types of elastic. There's some blogs about it. You can do a little bit of research on your own if you want, but let me know if you'd like a video just showing the different kinds and what I like each one for. But I like the braided for this when it goes in a casing. It's actually nice and soft and just comfortable and not, I don't know, not too stiff, you know, because there's not much between this and my body. And if it's real stiff, like not pliable this way, I don't know, it's just, this to me is more comfortable. Okay, and then you're wondering, okay, well, how much length of it do we need? Well, we have piece number five for that. This is the guide for the elastic. So you'll take the elastic. In theory, you will have pressed this as well, maybe with a little bit of steam.  So I'm making a medium. So I'm going to snip it off right there. Inevitably though, I do end up adjusting this a little bit just for comfort, but this will give you a good jumping off point. And then all of this is extra for your next project. All right. And then similarly to how we fed the drawstring through the neckline casing, we are going to do the same process for the elastic. You put your safety pin through one end. This time it doesn't matter what end, because there's no seam or anything. This is all going to be hidden.  And then you just keep pushing it through. And you're going to do this all the way around your garment, making sure that this doesn't go all the way through. Keep a little bit of it hanging out. Also make sure your elastic doesn't twist.  And as I suspected, this is where they were nervous about the safety pin getting caught up in the seam allowance, but mine just skated right over it. So I'm glad I didn't bother with that other step.  Right, we are through to the other side. So pull it hot, kind of redistribute everything, then you're going to take your, the other end and snap this. I think it might have gotten a little bit twisted, but that's okay. And then put these together, put the safety pin back in. That way this doesn't ever come unthreaded or get stuck inside the casing while we kind of try and distribute all of this gathering. Almost little by little. The casing will, the elastic will all be eaten up inside of this casing. You just kind of have to keep working it. Be patient. So undo your safety pin. Carefully hold these two ends together. And then you're going to go to your sewing machine and you are going to stitch them like on top of each other, kind of like, I don't know, a sandwich or something. And you're going to stitch a box with an X in the middle. That's what I like to do. And there is a five eighth inch seam allowance allowed here. So, you know, overlap them by five eighths. Don't be skimpy. You can change your presser foot back to your regular one now.  And I mean, this is a little bit funky because you just don't have a lot to work with, but just get it as flat as you can. Again, it does not have to be perfect. So now we have that completely attached. You can kind of see the stitching there. And that's not going anywhere. So yeah, we'll just redistribute this back into the garment and then we will have our casing completely sewn. Yay. Um, I think we're, this is the next to the last video before I really show you it all done. So we are finishing. So next week we will do the hem and we will make a belt if you're making the belt. And that's really it. We're rounding out our garment, as you can tell, looking pretty good. So let me know if you guys have any questions. Let me know if you guys have any problems. Let me know what you think. Cute, right? All right. I will see you guys next week. Bye.


To celebrate our 20th anniversary of Lakeland News, we're taking a look back at some of the memorable local stories we've covered over the years. We're calling it 20 for 20. 20 stories in all, one for each of the 20 years we've been on the air with Lakeland News. Now in 2015, the economy in Crosby was experiencing a revival thanks to the creative use of old mining trails in the area. Reporter Scott Sheehan took a closer look at what the present and future look like in the area thanks to the recently created mountain bike trails in the Crosby area. Ready to hit the trails, Cyan and the Lakes hope is to create enough mountain bike trails so an experienced mountain biker can ride for three days without repeating a trail. Cyan and the Lakes Chamber of Commerce Executive Director Jessica Holmweg says these trails and scenery add another unique element to the Brennerd Lakes area. We're a tourism destination and not only biking but all silent sports. We have a unique area. We have our mine lakes are great for fishing and paddle boarding and kayaking. There's just a vast variety of things to do in this area and we hope that that brings people here and supporting the economy here and spending their dollars in our local businesses. Cuyuna trails are ranked nationally at the silver level by the International Mountain Biking Association and feature over 25 miles of riding spread across 800 acres of paved and singletrack trails. More trails are being planned for the future and the business community continues to grow as well. There are now 15 new businesses in Crosby that weren't there six years ago. And you can watch that entire story at our website lptv.org tomorrow. Next Wednesday night we take a look back at a story from 2016. Nearing the end of the 20 for 20 it's been a fun look through the years. It has been. If you enjoyed this segment of Lakeland News please consider making a tax deductible contribution to Lakeland PBS.


I think I was in Tulsa and I'd never been to Tulsa before and I'm in the car and it's a Sunday morning and Jesus was saying like pull out that that list and I pulled out the list and and there was a Course in Miracles meeting at a church in Tulsa and I was pretty close to the church and he said yes we're going to that meeting today and then I looked at the time and and it was like like this meeting is like 11 to 12 it was like as I'm pulling up to the church it's it's 10 minutes to 12th and I'm like I'm not going to go into a course meeting with 10 minutes to go in a city I've never been in and just walk in the door and Jesus is like yeah that's what I wish to do you know he's the one who knows the way and he's the one that's providing the way and opening everything up so I got in my car I walked in and they were having a Course in Miracles group they were having a big heated discussion on sexuality and I've walked in on the 10 minutes less 10 minutes of a heated discussion on sexuality and they were so heated they didn't even know I was there there was a lot of emotions and a lot of well I think this know he's saying this so I sat there and then oh it's noon oh who are you how did you get here you know it was not the thing that I was typically prone to doing but then very much oh come on we're going out for lunch come with us and then this man Jack he said come on to my apartment and do you play tennis oh I've got a jacuzzi over there and oh I'm so excited to have you here I would look up we're going to do a Course in Miracles potluck picnic on my boat this afternoon and I would love if you would come and I'm gonna call some more people and tell them they got a new person and on and on he said I got a few things to handle just leaving me in his apartment saying help yourself to the fridge and everything like this and well this will be the first of many similar experiences where Jesus says I will go before you making straight your path and living leaving in your way no obstacles to borrow your way nothing you need will be denied you not one thing in difficulty but will melt away before you reach it you need take out for nothing except the only purpose you would fill so I was beginning to trust Jesus like okay if I join with you and I fulfill my purpose you're telling me you'll take care of everything else everything else in time and space like yeah that's it everything oh that's a pretty big everything so in that I realized he meant everything when he said everything that everything has been arranged and orchestrated


To the Blue Ox in action tonight against Park Rapids, the Esauks leading five to three in the second. Zach Hawking at the plate. Lines went into right field. They lead six to three. Same inning, Zach Braun up for Bemidji with two on. He delivers the base hit to left, one run scores. They close the gap to two and then the Blue Ox bats would just explode. They win this one, 18 to eight. It's big blowout win for them. If you enjoyed this segment of Lakeland News, please consider making a tax deductible contribution to Lakeland PBS.