Never Run Out of Stock Again: The AI Secret of Successful Ecommerce — Fabricio Miranda | How AI Boosts Inventory Planning Accuracy, Why Inventory Management drives E-commerce Success, What Ecommerce Brands should do to improve Inventory (#344)

In this episode of eCommerce Coffee Break, we explore how AI is transforming one of the biggest challenges in modern eCommerce: inventory management. 

Our guest is Fabricio Miranda, founder and CEO of Flieber.com, an advanced inventory planning platform for modern commerce. 

Fabricio shares insights on why traditional methods like Excel fall short, how Flieber's AI-driven solutions are helping brands streamline inventory processes, and how businesses can improve their forecasting and stay competitive. 

Tune in to learn how you can avoid costly inventory mistakes and drive growth. 

Topics discussed in this episode:  

• Why inventory management is the most critical part of e-commerce success 
• How traditional inventory planning tools fall short in the modern omnichannel retail landscape 
• What makes Excel spreadsheets inadequate for complex inventory forecasting and management 
• How AI and machine learning algorithms are revolutionizing inventory planning accuracy 
• Why data visibility is crucial for effective inventory management and business insights 
• What steps brands should take to improve their inventory management processes 
• Why data contextualization is essential for leveraging AI in inventory management 
• How proper inventory management can significantly reduce losses from stockouts and overstocks 

Links & Resources

Website: https://flieber.com 
Shopify App Store: https://apps.shopify.com/flieber-lite 
LinkedIn: https://www.linkedin.com/in/fabriciomiranda/ 
Instagram: https://www.instagram.com/flieberinc/ 
X/Twitter: https://x.com/fabfcmiranda 


Get access to more free resources by visiting the show notes at
https://t.ly/RtRkC 

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Transcript

Welcome to the eCommerce Coffee Break Podcast. In today's episode, we are going to talk about how AI is simplifying the hardest and most complex problem in modern eCommerce, inventory. Joining me on the show is Fabricio Miranda, founder and CEO at flieber.Com. So let's dive right into it. 

This is the eCommerce Coffee Break.

A top rated Shopify growth platform. Podcast dedicated to Shopify merchants and business owners looking to grow their online stores, learn how to survive in the fast changing e commerce world with your host Claus Lauter and get marketing advice you can't find on Google. Welcome to the show. 

Hello and welcome to another episode of the e-Commerce Coffee Break podcast.

Today we wanna find out how artificial intelligence AI is simplifying the hardest and most complex problem in modern e-commerce. We're talking about inventory with me on the show today. is Fabricio Miranda. He is the founder and CEO of flieber.com and inventory planning platform for modern commerce. Born in Brazil, became an entrepreneur at 26 years old and was involved in multiple ventures.

Fabricio moved to the yes, in 2013 and has founded. And co founded five businesses since then, and they're all currently, currently operational. So always great to talk to another entrepreneur. And that's welcome. Hi. How are you today? 

Yeah. How are you doing, Claus? Thank you. Thanks a lot for having me. And thanks everybody who's listening.

Let's dive right into it. So inventory management is a critical part of every business and a lot of businesses have problems with that. So why is inventory management so critical for e commerce brands? 

Yeah, inventory is the most important part of e commerce. So if you don't have inventory, you don't sell.

If you have more inventory than you should, your money is all gone. All trapped in inventory. Uh, and if you'll see the success or in success of companies in history, uh, brands in history, they're almost always related to inventory mismanagement and in history. For some reason, there was not a lot of investment in technology.

It's a very complex problem to solve. It has a lot of different intricacies. Each company ended up solving their own processes with. Internal tools like Excel spreadsheets or internal legacy systems. And now we're seeing all the mass, you know, when COVID hit, we saw what happens when you don't invest in technology in a segment.

So the whole supply chain was disrupted because mostly because of, of this lack of visibility, clarity, technology connections, and et cetera. 

You mentioned Excel. I think that's where everyone started some part in their business life on obviously running a business, specifically e commerce business is not the right tool.

What is the biggest mistake that you see when it comes to inventory planning? 

Yeah, Excel spreadsheets are great too. I'm an Excel geek. I know everything about Excel. I use Excel in my daily life. But it's a great tool for certain things, and it's not great for other things. And one thing it's not great for is if you have to process loads of data and different, um, different sources of data, you have to connect to different systems and download reports and upload reports into Excel spreadsheets.

And then. You have to manually create formulas connecting all these different reports. And all of the process is not only very prone to errors, but also what, um, what happens is, is, is you get wrong. You have to simplify a lot of things. So I give an example, uh, using sales velocity. A lot of people use the concept of sales velocity, uh, for forecasting.

So they basically get like the previous 30 days of sales, get an average of that. And use that as their future sales. Um, and the only reason why this concept of sales velocity exists is because Excel has a, a cell that you have to add something to a cell. So it's the, the, the, the, the whole paradigm of Excel is basically using cells to make calculations.

And in this case of forecasts, cells are not the best way to make calculations because you're, uh, first you have to go back in your history. And you have to see your whole history of sales. Then your history of sales has the anomalies that you want to cancel out before you'll do any kind of projection to the future.

For example, you were out of stock. You don't want to get out of stock and project that as a zero sales for the future. Because you're going to be out of stock again. So you need to go back and you need to adjust for anomalies, stockouts, uh, price variations. You increase price sales, go down, you reduce price sales, go up.

You don't want to feed that into algorithms because what's going to happen is that you're going to, the algorithms are going to think you have seasonality in that period when, Actually, it was a price variation, uh, listing suspensions on Amazon, influencer campaigns. There's endless things in the past that you have to first resolve to be able to do a good forecasting.

Um, in an Excel spreadsheet, you just can't do that. You can't run algorithms. You can't go back in a string of data and start adjusting that string of data. All of that becomes really hard. And even if you try to do it, I have some spreadsheets that I Was able to create kind of almost a flipper light in the spreadsheet.

It breaks very easily because it's so much processing that suddenly you don't, you can't use that spreadsheet anymore. It doesn't open anymore. So Excel is just not the right tool. 

Yeah. And I don't think Excel in the first place was, um, built for that. Uh, we just misused it over, over time. Now. Obviously, inventory planning becomes incredibly complex and you gave us just some examples specifically when you're selling on multiple marketplaces and omnichannel every DTC brand, every brand out there is omnichannel.

The same applies to being on every marketing channel you've managed in TikTok growing very quickly. You might not have any numbers in the past. Can you break down why traditional tools in inventory planning fall short in solving such challenges? 

Yeah, the traditional tools that were made for the regular world of retail, they were made to a for a different paradigm in the regular world of retail, you need you needed products on shelves to be able to enable your sales.

So your sales were made. After the product was physically on a shelf. Um, so all those tools, they are made in, in the concept of having distribution to as many shelves as possible to enable sales. Um, and all of the forecasting is done with, uh, I usually say, you know, if you're building a, a, a building, for example, if you were off by one centimeter.

It's not that bad, but if you're building a built in closet. If you're off by one centimeter, you're going to have a hole, you know, between the closet and the wall. So in the old world of retail, since everything was about volume, everything was about distributing to as many shelves as possible and send it to distribution centers, it was very inefficient.

It was very volume driven and very inefficient. The tools would allow for adjustments that were not, you know, to the, to the most, uh, pristine accuracy in the new world of retail, you sell, you A digital interface in e commerce and you would just fulfill the, the, the units later. So the more efficient you are in inventory, the better.

So there's this huge shifting paradigm from very volume driven and very low efficiency to very low volume, high efficiency in this new modern world. Besides that each channel has their own intricacy. Um, if you go on Amazon. The paradigms of Amazon are ranking reviews and other things that allow you to sell more or less.

So when doing a forecasting for Amazon, it's a lot different than doing a forecasting for Shopify, for example, where the paradigm is more, uh, if you're able to drive traffic to your website, there's no, Customer there for you already, like you have on Amazon, there's no ranking and et cetera. So paradigms are different wholesale, even more different because wholesale, you're receiving a purchase order from your retailer and you have to fulfill the purchase order.

So it's zero, zero, zero. Suddenly you received 2000 units, but then zero, zero, zero, 3000 units. And the old tools, they are just not able to deal with those different intricacies. It's a whole new business, the whole new world of modern commerce. So that's why we say that we're building inventory planning for the intricacies and specifically designed for modern commerce.

Yeah, just let's dive into it. So Fliba is a solution addressing these challenges and providing a competitive advantage by taking all these moving parts into consideration. Talk me through it. How does it work? 

Yeah, so we connect to different sources of data. Uh, so you can have Amazon, for example, we connect to your sales inventory for sales from Amazon inventory from FBA.

Um, you have, uh, Shopify. We connect to, uh, sales and inventory. If you're using, I mean, now it's delivered. So now it's outside of Shopify, it's a Flexport, but at the time that Shopify had delivering it, it would get the delivery information from Shopify and now we get it from Flexport. Uh, we connect to any, any source pretty much, uh, off data.

Uh, and even if we don't have a native connection, we have manual ways to connect to that with the Google Sheets since, uh, using Google Sheets as a middleware. Uh, then we get all that data. The first thing that we do is we normalize that data, as I was saying before. So we go back in history, we start adjusting the anomalies so that you don't feed your forecasting with the wrong set of data.

So we adjust for stockouts, for price variations, for, uh, outliers such as influencer campaign, uh, uh, listing suspensions, any, anything that happened in the past that changes the behavior of the data, uh, abnormally, uh, we will, uh, correct that and we will substitute those sales for what would have been the sale if that anomaly did not exist.

Then we have a much better just that by itself improves in 40 percent up to 40 percent the quality of the forecast, even if you use a moving average and see moving average. If you use over data that is not pre processed versus data pre processed. It's a 40 percent better better forecast that you get.

And then after that, we generate forecasts. We have set up 16 algorithms, all machine learning, Okay. Based on and if you don't like it, you can also use your moving averages or last year sales. We have flexibility and then after that we start plugging inventory to this calculation. So we imagine that everything is strings of data like we have the future sales, the future forecast, then we have.

We start plugging inventory today. We start plugging peels and seals that we will arrive in the future and we build a future inventory forecast. Once more, no formulas. Everything is based on strings of data, which is the beauty of using a system versus a spreadsheet. And then with that, we give you full visibility of what's going on.

Uh, we are able to solve, for example, if you have the same 3PL serving multiple channels, we can do that. If you have, um, the same fulfillment serving multiple channels, we can do that. If you have like East West, uh, warehouses, we can split the sales to the right warehouses. We have kits and bundles, back orders, all those complexities of.

Bottom commerce are dealt by this, this very sophisticated by far. And if any competitor is seeing, I challenge any competitor to go against us and, and, and check our inventory consumption machine, uh, it's by far the most sophisticated in the market. 

Hey Claus here, just a quick one. If you like the content of this episode, sign up for our free newsletter and become a smarter Shopify merchant in just 7 minutes per week.

We curate content from more than 50 sources, saving you hours of research and helping you stay on top of your e commerce game with the latest news, insights and trends. Every Thursday in your inbox, 100 percent free. Join now at newsletter. ecommercecoffeebreak. com. That is newsletter. ecommercecoffeebreak.

com. And now back to the show. Yeah, a lot of moving parts there. So you just mentioned, um, one number on there, uh, on results that people saw. Can you share a specific case study or success story? You don't need to name the brand where Flyby has significantly impacted the business. 

Yeah, yeah. Uh, we have, we have, uh, uh, I think that the most Your case is COVID because COVID is something that you can't predict.

Uh, it's impossible that any algorithms would predict that COVID would exist. Right. And it transformed, completely transformed how brands would sell, especially online. So we had a customer called Bonsia, Bonsia brands as a coffee, uh, brand, and they would sell 1. 5 million on Amazon. Um, which was already pretty good, but it was a small, a small brand overall.

Um, and then COVID hit, but he would buy coffee at the grocery store. But when COVID hit, there was no grocery store and you have to buy coffee. Suddenly what our system started showing was sales are going up, then the forecast starts going up because forecast starts going up. The consumption of inventory starts being deeper.

You start consuming more inventory quicker. Uh, and then because you start consuming inventory quicker, we start saying you need to replenish sooner. Uh, and then we started generating alerts, you need to replenish, and they would replenish, and you know, that was not enough. Next day, you need to replenish more, and they would replenish.

Up to the day that was not possible to replenish anymore, first because of their factory shut down, and then they didn't have enough time to replenish. And then our system starts saying, you're going to run out of stock. So since you will run out of stock and since inventory is not able to arrive before that, because it's physical, it's impossible for that to arrive.

You need to start changing your sales. So they started changing the sales first, reducing the advertising span. It was not enough sales would still go up, go up, go up. Then they started increasing price to hold sales. And, you know, as a result, uh, six months into COVID, they were the only brand on Amazon with coffee.

And they went from 1. 5 million in sales pre COVID to 6. 5 million in sales post COVID. Uh, and they sustain it up to today. They are over 5. 5 million in sales. Because they, they were so good at, at managing that, that, uh, the, the, the two sides of the two levers that they have the inventory first. And when inventory was not possible, the sales lever.

And this is what we bring as, as a result to, to all the brands that work with us. Is a clear visibility of what's going to happen with inventory and solutions using both levers, inventory and sales. 

I think that was a very good example to show that customer satisfaction means product availability and also brand recognition.

You build up your product. Brand reputation by being able to sell. I think a lot of, and that includes me with my Shopify store that I had for many years. Um, there might be just a marketing campaign and you said that's very successful and then you're selling out and then a lot of customers will not come back.

So you're losing them and that might be. Specifically with coffee, for instance, returning business, subscription business, and you will never get back. So it has a lot to do with brand recognition and customer satisfaction there. Now, what are the first steps that a brand should do when they want to improve their inventory management?

Yeah, I think the, the, a lot of people think it's rocket science to start. Yes, there is a little bit of rocket science with all the machine learning algorithms, but that's not what I like to say that makes most difference. I think the most difference that it makes is having visibility, data visibility. If you have data visibility.

Everything else is kind of solved as a consequence of that, because data visibility gives you insights that you wouldn't have if you did not have visibility. So the simple fact of what happens a lot with us, it's very funny. Uh, Customers just connect their accounts into Flibr. And for the first time in history, first, they see a supply chain map.

Uh, so they see how the data is connected. And, and visibly see, you know, the boxes, you know, have Amazon here, have FBA here, FBA is connected to Amazon, and you have a 3PL that is connected to FBA, but this 3PL is also connected to Shopify as a fulfillment, and all of that for the first time is visible.

Second, they see their whole sales history in charts that they can interact with, compare periods, and they can do that for multiple accounts. So on Amazon, you can do that for a single account. So imagine if you go to the business reports on Amazon to see your single account, uh, performance, but imagine if that same view was able to show you all your accounts combined across all the channels that you have.

So that's the first thing we bring. Then forecasts is the biggest one because he forecasts. For the first time, they see seasonality in their forecasting a chart because they are now using a sales velocity, which is a cell, which is like a flat number. They see seasonality. They see for certain periods how the sales are going to be.

They can compare and change and adjust and create promotional campaigns, for example, that they say they're going to sell more during that period and see immediately the impact that they have on the forecast. Uh, so all of those things in forecast they see for the first time they see a chart also showing their inventory consumption by inventory location Uh, and with all those intricacies that I was talking about if you have bundles and kits and bundles for example That will be taken into consideration It will be you'll be able to see that those kits and bundles how much they are consuming from that inventory location And how much another?

Brought the product on Amazon is consuming and how much, you know, product on Shopify is consuming. All of that is visible. Um, and you can start seeing also when you need to replenish and products that are overstocked and all of those things are brought to your, to your, your face. And you're able to, for the first time, see a bunch of those things that you don't see hidden in cells on Excel spreadsheets.

So that by far, and that is not rocket science, that is, you know, strictly showing the data that is already there, right? And that is by far the biggest aha moment that the customers have. And then when they start, uh, simulating purchases and transfers, seeing that they can select a different period and see how much they're going to.

Uh, consume on that specific period, according to all the seasonal patterns, all the planning promotions that they have, but cross channels and all those things, uh, then it becomes a huge no brainer to use that and customers just don't leave. We have, uh, our, our, uh, churn is less than 3 percent a year, so it's really, really extremely small because customers just cannot leave after they start using this, this type of tool.

Okay, on that note, who's your perfect customer? Are there specific industries or verticals that are using your tool more than others? 

Yeah, uh, we, so the perfect customer is any brand, uh, any brand that sells anywhere, but mostly companies that are mostly selling online. Um, and 5 million to 100 million in annual sales is the sweet spot.

We do have brands that sell in the 1 to 2 million range. Uh, some of them really, really active. But those brands are more like visionaries. They are trying to, you know, set up process, which, which is great, but the market hasn't gotten there yet. Brands that are at 1 million, usually they're trying to survive.

They're trying to increase sales and make a business out of this thing. Uh, when they get to 5 million, it's unbearable. It's really hard for you to manage inventory, especially if you're multi channel. So five to a hundred million, we have a few brands over a hundred million. We have a few brands, less than a hundred million, but most less than five million.

I'm sorry, but most of them are in this five to a hundred million range. Uh, apparent apparel companies are. Challenging because of the way they operate. Apparel companies usually operate with collections. They don't care much about the life cycle of the history of the product, replenishing the same product in multiple years and that impacts in the in the way that our forecast is done.

So in this case, when we have apparel brands, when we have apparel brands that are not very complex, we keep them. We have a bunch of customers in apparel. But when we have very complex ones, we sent to, you know, companies that would be kind of competitors of ours, but they are specifically targeted in apparel brands and we, we want the best for the customer.

So we just sent it to them. Uh, but outside of apparel, like it's a great fit, you know, supplements, vitamins have a lot of intricacies. Uh, they're a great fit. We have a bunch of supplement Vitamins brands, uh, yeah. Yeah. 

Okay. You said earlier that, um, your algorithm needs some time to, to kick in. Walk me through the typical onboarding process for a new user.

What kind of steps are involved? How long does it usually take? 

Yeah, that's, that's a great question because a lot of people think it's extremely complex to onboard in such a system. And it used to be, we used to have like a 25 day, uh, onboarding and now it's literally minutes. Yeah. Okay. Thanks. Uh, so what happens is that, uh, when the customer goes and connects the accounts, we start downloading the information from that account.

And the first thing we download is the product list. Uh, and we'll show the product list in a matter of two minutes. Uh, we'll show all the product lists from the accounts and they are able to just, uh, define because the product lists come by SKU. Um, if you have only Amazon, you can choose the ASIN to be the, the code that is going to identify your products.

But if you're multi channel, You usually have some internal code across that you use across all the channels, uh, so you're able to input those codes, uh, and kind of map those products into flavor. That's the first part. Um, and then we're downloading the sales data. We're downloading the inventory data from the integrations.

You also just have to say what is the fulfillment and the storage warehouse for each one of your, um, of your sales channels. And when you do that, we just map that and the system, it's just a matter of waiting for the system to download all the data. So the setup itself takes 15 to 20 minutes. Uh, we help customers set up if they can, uh, schedule a meeting with us.

And it's a half an hour meeting. So it's really, really quick. We set up the whole system in that time and it's just a matter of waiting for the system to download from the integrations in a matter, depending on the size of the brand, it can take, you know, 45 minutes or it can take eight hours for us to just download that information.

And when the customer gets back to the system, they receive a notification when they get back to the system. They're able to see the whole system with all the forecasts and everything else already set up in front of them. And then it's just a matter of them, you know, adjusting whatever they want to adjust in terms of parameters, defining what is the lead time of each one of the products, defining what is the minimum days of stock that they want to keep, the safety of stock, defining what is the overstock trigger, uh, defining what is the maximum stock that they want when they replenish, and when they define those parameters.

Everything is going to be, uh, already operating. So it's extremely simple and, and straightforward. 

Okay. Let's talk about the pricing structure. Obviously you said there are smaller brands, there's bigger brands. How do you charge them? What kind of pricing structure do you have? 

Yeah. The pricing today starts at 299 for brands with less than 2 million in sales.

Uh, and it goes up to we have grants paying over 20, 000 a month, depending on the size and the complexity that they have. Um, everything is on the website. So if you go to the website, you can see a pricing calculator showing exactly how much you would pay. Uh, we also have free trial. We have a 30 day free trial.

And, uh, we give, uh, for now we were giving a 30%, uh, discount for the first four months because we're, uh, just going out of the beta of the new version. We launched a new version of the system in March and we're dropping the beta now. So, uh, we're incentivizing customers to come to this new, new version of the system.

Um, and, uh, and yeah, so, so it's, it starts really, really low, the price 2. 99 per month. And the average that we have per customer is around 2, 200. For a month, that's the average that the customers pay us because we have bigger customers today. 

No, that makes perfectly sense. And obviously if you're running out of stock, that will cost you much more than Oh, much more.

Yeah. I usually, 

if you, if you check, if you check the numbers in your first purchase, you're able to recover the whole year off of fees that we charge because it's, it's what customers are, all the brands, if you go to, to, um, uh, any research online. Uh, there's eight around 8.5% in losses due to stockouts and overstocks.

Mm-Hmm. . Um, so it's, it's crazy if you're selling, you know, a million, if you're selling, you know, a thousand, uh, dollars, uh, that would be 85 right? Dollars. If you're selling a million dollars, it's $85,000. Uh, if you're selling $10 million is $850,000 that you're losing. And, uh, you know, if you're paying us, you know, for 10 million, if you're paying us six to 8, 000 a year, uh, that's a no brainer, right?

You get a like 72, 000 back. 

No, I think it's, it's overall, it's a no brainer. Um, before we come to the end of the coffee break today, is there anything that you want to share with our listeners that we haven't covered yet? 

Yes, I think the AI part of this whole thing, how AI is going to influence, uh, inventory moving forward.

I had a webinar the other day about the importance of data contextualization. I just want to preach that. Um, in retail, if you go to almost any segment and you get the raw data, you're able to get the raw data. You have already the diamond in your hands and you're using, you know, data. for that. And you're feeding that data into algorithms, and that is working.

In retail is one of the only segments that even if you're able to get the raw data, that data is still wrong. Because that data will have stock outs, that data will have moments that you had price difference, you you have Influencer campaigns, you will have all those things that I mentioned throughout the episode.

So it's really important for you to create in your company, if you're a brand, uh, to create a discipline of contextualizing that data. The way that I would do it is every single day, go to yesterday, And start annotating what happened to each one of the products that had some kind of anomaly. Uh, even if you have 2, 000 products, the 2, 000 products are not going to have anomalies on the same day.

So just identify the, the, you know, 50 ones that had anomalies, you know, it had a stock out, or you had. Some promotion that you made on that day or your competitor were out of stock, and that's why you sold more on that day. So all of those things, I call it data contextualization. You create annotations for that.

That is going to be worth so much money in this new world of AI. Today we're still in the early days, but very soon you're going to be able to just load that into AI. And brands that have data contextualized We'll have a crazy competitive advantage over brands that have uncontextualized data. So start contextualizing your data.

We do that automatically at Fliber, but you don't have, if you don't use Fliber, just do that in your spreadsheet. Create a process where you change the sales for what would have been the sale if you're not out of stock or if you didn't have that influence for campaign and annotate what happened on that day and just store that.

That is going to be worth so much for you in the future. That's going to create a huge competitive advantage. 

Okay. Thanks for sharing that. I think that's a very important point that you made there. Um, data is so important and I think a lot of businesses sort of survive with average data quality. Um, and they could do so much better by just doing what you just said, or just making sure that the data is cleaned out and is really usable data, put it that way.

Um, so I think that's very important. The AI obviously will help with that. Where can people go to find out more about you guys? 

Yeah, if you go to Fliber. com, F L I E B E R. com or, uh, if you look for me on LinkedIn, Fabrizio Miranda, uh, you, you probably will see my name on the call out of this episode. Um, and you can contact us at any time.

We have also an Instagram account, uh, at Fliber. Um, and, um, just contact us anytime you, if you go to the website, you can start a free trial. If you want a demo first, you also have the option of asking for a demo. Uh, we're very, very customer centric. If you go to g2. com and see the reviews about Fever, you'll see what our customers talk about us.

And I invite all the brands to, you know, start coming to this new world of data, data, data. I usually say that, uh, marketing in the past was about location, location, location. That's what we would learn in our MBAs. And now it's going from location, location, location to data, data, data. You have to start soon.

Uh, otherwise you're going to miss the, the, the, the party. 

Yeah, couldn't agree more. I'm a marketer for 25 years and data is really the key point of doing marketing nowadays. So 100 percent right. Cool. Fabrizio, thanks so much for your time today. I think you gave a very good overview where inventory management is right now and what a state of the art and I hope a lot of people will reach out to you.

I will put the links as always in the show notes. Then you just want to click away. Thanks so much for your time today. 

Awesome. Thank you very much. Well, thank you. everyone who's listening. 

Hey, Claus here. Thank you for joining me on another episode of the e commerce Coffee Break podcast. Before you go, I'd like to ask two things from you.

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com. Thanks again, and I'll catch you in the next episode. Have a good one.