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cover of episode EP 573: Master Data in Warehouse Automation with KNAPP’s Marinus Bouwman

EP 573: Master Data in Warehouse Automation with KNAPP’s Marinus Bouwman

2025/3/24
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The New Warehouse Podcast

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Marinus Bouwman
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@Marinus Bouwman : 我在KNAPP公司工作,主要负责软件产品管理和超本地解决方案的业务拓展。我参与开发了Keysoft Packmaster和Keysoft Genomics等软件,这些软件帮助我们发现高质量数据对自动化仓储的重要性。在与客户合作的过程中,我发现他们常常低估数据质量的重要性,自动化仓储对数据精度要求极高,任何细微的偏差都可能导致机器人损坏产品,造成停机和损失。因此,我们需要一种新的主数据类型——自动化主数据,它包含更多属性且质量更高。KNAPP通过提供数据分析、测量设备和轻量级解决方案来帮助客户改善数据质量,并通过教育和实际案例来提高客户对数据重要性的认识。我们还与GS1等组织合作,共同提高供应链数据质量。此外,我们正在开发多站点服务平台(MSCC)来预测设备故障,减少停机时间。关于微型配送中心(MFC),我们发现“MFC”这一术语已不再适用,现在更注重为超本地电商业务提供高度自动化的解决方案,这需要考虑成本、最后一公里配送、库存管理等多种因素。自动化解决方案的实施需要考虑订单量,只有订单量达到一定程度才能保证其经济效益。未来微型配送中心和小型配送中心的业务将会增长,但速度可能不如预期快;改善数据质量需要供应链各方的合作。KNAPP提供免费的试点项目帮助客户评估其主数据的质量,并提供咨询服务帮助客户选择合适的超本地电商自动化解决方案。 @Kevin Lawton : 作为主持人,我与Marinus Bouwman讨论了KNAPP公司在仓储自动化中的主数据管理和超本地解决方案。我们探讨了数据质量对自动化仓储的重要性,以及如何解决数据质量问题。我们还讨论了微型配送中心(MFC)的概念及其发展趋势,以及如何选择合适的自动化解决方案。

Deep Dive

高质量数据:自动化仓储的基石——KNAPP的实践

我最近与KNAPP公司产品经理兼业务拓展主管Marinus Bouwman进行了一次深入的访谈,主题围绕着仓储自动化中的主数据管理和超本地解决方案。这次对话让我对高质量数据在自动化仓储中的关键作用有了更深刻的理解,也看到了KNAPP公司在解决数据挑战和推动行业发展方面所做的努力。

Bouwman介绍说,他参与开发了Keysoft Packmaster和Keysoft Genomics等软件。在这些项目的过程中,我们发现,高质量的数据是自动化仓储成功的关键。以往,我们可能只关注产品的尺寸和重量等基本信息。然而,随着自动化程度的提高,对数据精度的要求也大幅提升。在高度自动化的仓库中,即使是毫米级的偏差也可能导致机器人损坏产品,造成代价高昂的停机和损失。 这与传统人工仓库的情况截然不同,人工操作具有一定的容错性,而机器人则需要精确的数据才能正常运作。

正是基于这样的认识,我们提出了“自动化主数据”的概念。这是一种新的主数据类型,它包含了比传统主数据更多、更全面的属性,并且对数据的质量有着更高的要求。例如,在食品零售行业,构建一个混合托盘可能需要大约60个属性,包括产品的易碎性、是否可倾斜、表面是否平整等等。获取这些属性并确保其质量,是自动化项目成功的首要条件。

然而,现实情况是,许多客户往往低估了数据质量的重要性。他们常常认为自己现有的数据“足够好”。但通过KNAPP提供的预先数据分析和测试,我们发现很多客户的数据存在各种问题,例如尺寸偏差、条码重复等。这些看似微小的错误,在自动化环境下会被无限放大,最终导致严重的效率损失和经济损失。

为了帮助客户解决这些问题,KNAPP提供了一系列轻量级的解决方案。这包括提供数据分析服务,帮助客户识别数据中的问题;提供测量设备,例如多功能扫描仪,帮助客户收集更准确的数据;以及与GS1等全球标准化组织合作,共同提高供应链数据质量。更重要的是,我们注重教育,通过实际案例向客户展示高质量数据的重要性,并帮助他们改变观念。

除了数据质量问题,我们还讨论了微型配送中心(MFC)的概念及其发展趋势。Bouwman指出,“MFC”这一术语已经不再完全适用,因为实际情况远比简单的“微型”概念复杂得多。现在,我们更注重为超本地电商业务提供高度自动化的解决方案。这不仅需要考虑仓库的规模和自动化程度,还需要考虑成本、最后一公里配送、库存管理、不同温度区域的管理等多种因素。一个成功的超本地解决方案,需要一个完整的生态系统,而不仅仅是一个自动化仓库。

我们还探讨了自动化解决方案的适用性。并非所有企业都适合立即实施高度自动化的解决方案。订单量是关键因素之一。只有当订单量达到一定规模时,自动化解决方案才能带来经济效益。 在初期,企业可能更需要关注市场培育和客户行为的改变,而不是盲目追求自动化。

总而言之,高质量的数据是自动化仓储成功的基石。 KNAPP公司通过提供全面的数据解决方案、教育和咨询服务,帮助客户克服数据挑战,并根据客户的实际情况选择合适的自动化方案,最终实现仓储效率的提升和业务的增长。 我们相信,未来微型配送中心和小型配送中心的业务将会增长,改善数据质量需要供应链各方的共同努力。KNAPP将继续致力于提供创新的解决方案,推动仓储自动化行业的发展。

Chapters
This chapter explores the issue of idle robots in warehouse automation and introduces Zebra Technologies' solution to increase asset utilization and reduce costs. The solution focuses on optimizing pick paths and improving cubic capacity.
  • Idle robots reduce efficiency and increase costs in warehouse automation.
  • Zebra Technologies offers a solution that requires 30% fewer robots, leading to increased asset utilization.
  • The solution optimizes pick paths, improves cubic capacity, and provides a cost-effective entry to automation.

Shownotes Transcript

Translations:
中文

You don't want your employees to be idle, so why would you want your robots to be idle? Zebra Technologies is addressing idle robots by bringing their latest solution to the market, which requires 30% fewer robots.

resulting in increased asset utilization and a cost-effective entry to automation for your fulfillment operations. The Zebra Symmetry Fulfillment Ecosystem is bringing you balanced utilization, more cubic capacity, and optimized pick paths so you can operate at peak performance. Want to take your fulfillment operation to the next level? Learn more at zebra.com or click the link in the show notes.

The New Warehouse podcast hosted by Kevin Lawton is your source for insights and ideas from the distribution, transportation, and logistics industry. A new episode every Monday morning brings you the latest from industry experts and thought leaders. And now, here's Kevin.

Hey, it's Kevin with the New Warehouse Podcast, bringing you a new episode today. And on today's episode, I'm going to be joined by Reese Bauman, who is a product manager and business development over at Kanap. And we are going to talk a little bit about Kanap.

Knapp, what they do and what he's focused on over there. But we're also going to talk about some of the foundational elements of automation, which a little teaser is data. And we've talked about that on the show before, but we're going to dive a little bit into that and talk a little bit about master data and how Knapp is bringing to market this product software that helps with that. And we're also going to dive a little into the

MFCs, micro fulfillment centers, and talk about what does that term actually mean in 2025 and how is that evolving? So, Rhys, welcome to the show. How are you, sir? Thank you. Thank you for having me.

Definitely. Happy to get you on. Happy to talk about this. Very interested in data. I believe in data. I'm a data believer, I will say, which I think is a good thing. It's not supposed to be a religion, right? But yeah. Definitely. Sometimes turns into one. Yeah, absolutely. So very interested in getting into this conversation with you and hearing your perspectives on it. But

Before we get started and get into that conversation, for people that aren't familiar, why don't you tell us a little bit about Knapp and also tell us a little bit about your role itself and what you're focused on because Knapp covers quite a few things, I believe.

Yeah, with pleasure. I joined Knap about seven years ago now, 2018, February 2018, and have been a software product manager ever since. First product I worked on was Keysoft Packmaster, which basically calculates how to build mixed palettes.

And through that, we figured out that we need a lot more information than just dimensions and weight. You need to know crushability of something. Is it tiltable? Is it maybe transparent? Has it a flat surface or not? I found out that we need a lot more good data, a lot more attributes, but also in high quality, which led to the creation of the product Keysoft Genomics, which I'm also the product manager. And as of two months ago, I'm also working on business development for our MFCs. Okay.

our hyper local solutions if you like so mfc cfcs and it's been great fun knap is a is a great company to work for i i think like 15 years ago we weren't that well known in the states but right now we we've done quite some interesting projects with companies like kroger walmart

And it's been a roller coaster. Obviously, the pandemic was an interesting time, as well as the after pandemic time. The subsidiary I work for is mainly focused on food retail. Knap works in different areas like healthcare, fashion, general retail. But I'm mainly busy with food retail projects. And yeah, I think it's a very interesting industry to work for.

Yeah, absolutely. And certainly an interesting time, I'm sure, working in food retail during the pandemic there as well. And certainly I've seen your guys' name all over the states now at various conferences and things like that and some of the projects you've been doing. So great to get you on here and learn more from your perspective too. So Knapp is dealing with a lot of automation projects as well, right? But as you mentioned there, there was kind of a...

a recognition in a sense of, you know, some of this additional data information that needs to be captured, understood to really make some of these projects successful. So, I mean, tell us a little bit about, because, you know, obviously we talk a lot about automation and, you know, we go to these conferences, we see the videos now all over, whether it's LinkedIn or YouTube, whatever the case may be.

And automation is, you know, it's cool. It looks it's very visual, right, which I think, you know, captures people's attention initially. But obviously, there's there's a lot more that goes into making those automation projects happen. And, you know, those physical aspects move in and do what they do. So tell us a little bit about, you know, what kind of goes into behind the scenes of some of these automation projects or just the solutions in general.

When KNAPP started doing more and more automation projects, I mean, we grew rapidly for the first time, I think about 40, 50 years ago, mainly in healthcare with our A-frames. And then it became big with the whole shuttle systems, you know, good persons. And this was back then, this was the main focus. How can you get more steel in there? How can you get more innovative robots? But as you mentioned, there's so much going on these days. If you build automation that is not necessarily related to the machinery itself,

Especially if you look at e-commerce or e-grocer, location becomes all of a sudden extremely important. Where do you put an MFC? Where do you put a CFC or fulfillment center in general? It is different than with DCs. Getting the right people to organize something like that, there's so many things that you need to think about in an industry with a very low margin of

adding new processes, selling products maybe slightly cheaper is a big risk. It's something people want to think about.

You mentioned it briefly before, master data has become a big, big topic all of a sudden. It's not just dimensions and weight anymore as it was in manual warehouses. Now, we need roughly about 60 attributes in order to build a pallet, a mixed pallet in food retail with a robot. And you simply don't get this information very easily, neither from suppliers nor from the retailer.

So you got to come up with getting these attributes and also getting them in the right quality. I guess that was one of the biggest things that surprised me six, seven years ago. When you started working with customers, with clients, they would always come and say, you know, our data is fine. Data is not a problem. And then you would run some simulations. You would do some pre-palette building simulations with Packmaster and you would see that half of the dimensions were just one, you know, one inch or one millimeter, which obviously couldn't be true at

And that's just the dimensions that you can't get right. So that is a big challenge for sure.

Yeah, yeah, absolutely. And I love that you said that, you know, customers would be like, oh, yeah, there's no problem with our data, right? And then I'm sure you uncovered some things there. But, you know, tell us a little bit about that because, you know, I think too, as we see a lot of transition and I think, you know, you mentioned the food retail space, right? I mean, there are certainly a lot of, I think, you know, entities, warehouses, either 3PLs or, you know, older businesses

and companies that have been around for a long time kind of in that space, you know, they may think that, yes, their data is fine, right? Like you said, because the way they've been doing things, which may be very manually, didn't necessarily call for so much intricacies in that data. So, yeah.

Tell us a little bit about, you know, what kind of from a master data perspective, I mean, what what typically is kind of overlooked and, you know, why does it matter to get that in line before trying to start to automate some of those processes? If you look at the data that they are familiar with, like like maybe a barcode or something.

Like I said before, dimensions, the level of accuracy that you need for a highly automated DC or FC is completely different than for a manual one. If in your manual warehouse, you maybe have your dimensions off by one or two inches, not much will happen. You can still fill your high base. You can still do your picking. There's nothing going bust.

If a robot thinks that something is an inch smaller, but in reality it's not, it will run into it with, I don't know, two, three tons of force, the product is broken. And especially in food retail, you have quite some stuff to clean up. So you have a lot of downtime.

Like I said before, GTINs or UPC codes in the past, maybe like 20, 30 years ago, it wasn't that strict. So, you know, suppliers were using the same barcode for maybe two or three products at a time. And so a lot of systems have legacy barcodes in them now. We sometimes get a telegram for an item that has 40 barcodes in there.

But we all know that there's only one physical barcode on the product. So which one do you take? Those sort of things, you know, just getting this unique identifying in place is a tricky one, which is, by the way, one of the reasons why we started working very closely very early on with organizations like GS1, Global Standard One, which especially in food retail are building the standard worldwide for that industry, but also other industries.

but they also say they have an issue with the quality of the data. So on the one hand side, you have a number of attributes that you need, but you also need a quality in a certain quality range. Like I said, dimensions,

It's typically not much more than five millimeters. What is that? Quarter of an inch, something like that, that you can be wrong. Weight, similar stories. We've heard stories of people having their weight wrong in their WMS system, and all of a sudden, none of the pallets would go into the high bay because they thought it had 20,000 tons in it instead of 200 kilograms. And then you're there, and then the robot says no, computer says no. So

It's a different world for sure. There's a lot of people working on master data. You got data managers working on PIM systems, CRM systems, ERP systems, and they're doing a great job and

It's complex enough already as it is. The focus there, I think, is very often towards POS or end customer. And what we're seeing now is that we need an additional category of master data called, well, we're calling it automation master data to have all these attributes in there in a certain quality. And we hope that we can get more data managers on board to help us with that.

Because the earlier on in the supply chain, we can get that right, the more we'll benefit from it. Right now, we're doing it the other way around. We're getting this data when the item hits the docks in the automated warehouse. And that's pretty late on in the process. Interesting. Yeah. Yeah. And I think that's a really great point there where you mentioned that, you know, the weight is off and, you know, all of a sudden, you know, they think that this pallet weighs 20,000 tons, right? If you think about...

you know, manual operation. Well, human is going to say like, Oh, I know this doesn't weigh 20,000 tons. Right. So, you know, I'm just going to navigate around that and, you know, still get the task done. But the robot, right. It's very, it's,

black and white in a sense, right? It's either can fit in my parameters or it cannot fit in my parameters, right? And so that creates a delay certainly in downtime, which obviously is costly no matter how long it is. So, you know, very interesting there to hear that. And I think that it's certainly, and I've talked to people and heard about different projects, you know, certainly something that

you know, has come to light, I think, is that, you know, there's a lot of bad data out there in a sense, or just, you know, the quality is not as great. And I think about,

you know, in my career, we had one situation where we would induct these cartons onto the conveyor and they would go around zone pick type situation. And we were, it was a book publisher and every year we would do calendars as well. And we would get the big desktop calendars and the one year they didn't update the, the dims on them. So you watch the conveyor and there's all these small boxes with like big, I

big wide desktop calendars just riding on top of them. And then, and I was in charge of quality and they would come to our mezzanine and,

And we'd have to swap them all out and get them fixed. So it can add up like very quickly when you have that, you know, mix of automation and human element too as well. Because the humans like, oh, well, this is what it's telling me to do. So I'm going to do it in a sense. Right. And so there's two sides there, I think, where you have, you know, fully automated where the robot, right, is just going to say like, oh, no, I'm stopping. Like I'm not doing anything anymore.

And human is interacting with the automation. Like, well, you know, this is what's telling me to do, right? System's smarter than me, you know, so I'm just going to do what it says. And then, you know, you run into these issues. So tell us a little bit about how, you know, your focus with Keysoft and, you know, Knapp. I mean, how do you guys help tackle this and how do you prepare people to undergo kind of this data cleansing in a sense before doing like some type of automation project?

It's a lot of education that goes with it. They have to talk to people. They have to, you know, sometimes feel it, find out that the data might not be good enough. We do upfront data analysis so people can give us their current master data. And then we do a quick couple of test runs with it.

We send over measuring devices like multi-scan, which basically measures the dimensions and weight. And that's connected with our software, Keysoft Genomics, that people can then use for two, three months to get a representative amount of data in there. So we can do an even more in-depth analysis. We're still doing pilot projects with retailers and the local GS1 member organizations to

to basically see where along the whole supply chain data gets corrupted or quality is lost. So it's mainly about bringing attention to the problem and also proposing lightweight solutions without them having to invest millions of dollars in doing that. So it's a lot of manual work. It's a lot of hands-on work.

But that works. If people actually see where the data goes wrong and then what the effects of that are, it immediately changes their whole mindset and it's a different conversation afterwards. So that is very rewarding, I would say.

But yeah, it's not a straightforward one. It's not a straightforward one. If you go and talk to the data people, they're like, yeah, we're not an automation. We don't want to talk to you. If you talk to a warehouse manager, he's like, yeah, master data, I'm getting that from somewhere else. I don't want to talk about it. It's like, well, if you don't get it right, 150 million machines you just bought are not going to work. So we might want to have a talk about it.

Yeah, yeah, absolutely. And I think there's, it's kind of seeing a bit of a change there, I think, where, you know, because I've been a warehouse manager before and, you know, I've had managers underneath me and supervisors and, you know, certainly dealt with the people that are like, like, I don't care about data or I don't like data and do not show me an Excel spreadsheet. Now I'm worried about that. Like, I just want to be on the floor and just manage the people and, you know, which is, which I think,

was fine, right? But I think you're seeing where you need to be more in touch with that data and more in tune to be able to navigate that. And ultimately, I think we still very...

early i think in in penetration of automation as a as a whole absolutely yeah absolutely i mean i mean i think you see two big changes first of all we like in the past and we still do we we measure every item probably 100 times when it goes through a fully automated system every time you know something comes from the highway to the dpal and it goes into a shuttle system etc there there are measurements there there are sensors there and in the past we would just

Measure it, check it, okay, and then throw it away. Now we are saving this data. In case of genomics, we're actually gathering it to start learning from it. And all of a sudden you see, instead of a standard histogram that you would expect from a dimension distribution, you would see multiple populations in there because the customer has multiple suppliers who have different molds for their PET bottles. That's one part of it.

Another part of what we started doing at KNAPP is what we call the so-called MSCC, which is our multi-site service platform, where we do also gather information, especially on machines. How often has it been used? How often has this conveyor been used, et cetera, to basically give feedback to our customers saying, okay, and, and,

In the next five days, this specific conveyor or this specific part might fail because you've used it so many times. If you replace it now, it's only going to cost you two minutes because that's replacement time. You have no downtime instead of waiting for it to break.

So, yeah, we're starting to learn to get a lot more out of data. But I totally agree with you. We're just at the beginning. It's not just the warehouse, is it? You got this whole circular economy thing coming in our direction where we need to know at any point in the supply chain what is what and where it is and what its condition is, even if it's being reused. And so everything's going to have to work so much closer together that I think doing nothing is not an option.

Yeah, absolutely. And I think it's, you know, getting more and more the case too. And we see, you know, certainly more solutions coming to the market as well, which I think, you know, is just justification that people are asking for it or there's demand for that to be able to happen. And I think that's quite, quite inevitable. So, so very interesting there on the data side. And you mentioned in there a couple of times, these MFCs or micro fulfillment centers. And I think,

We've certainly seen a lot of micro-fulfillment centers, companies focused on those come and go at this point as well. There's been a lot of up and down, I think. And certainly we see those tending to be heavily automated too as well in a lot of scenarios. So tell us a little bit about, because you're heading up the MFC part of Canap as well, right? So tell us a little bit about

You know, as we're in 2025 here, I mean, what does an MFC even look like or what does that mean these days? Because I think there's a lot of varying definitions I've seen. Yeah, absolutely. And I think it's also a term that is no longer fit for the market. So I think when we started working on these type of solutions,

2018, 2019, we basically called them MFCs, micro fulfillment centers, because the idea that was, you know, I just build them next to a supermarket and all of a sudden I can do e-grocer or e-commerce.

We've built quite a lot of them, some very successful, some a little bit less successful. And what we've learned over the years is that it's not just a question of, hey, how big does it need to be and how much can I put in there and how much can it grow over the next two years or five years? But.

Because it's so close, so hyperlocal, as we call it, to the customers, costs, last mile delivery topics. How do I deal with my margins? Do I give the transportation the last mile for free? Do I charge people for it, which then influence maybe their buying behavior, inventory management? All of a sudden, I need to deal with different temperature zones. It's

It's a very, very, very complex system on a very small surface, mainly operated by people who might not be used to the automation degree.

And what we've also seen is that, you know, some actually did want to buy or build a very small one with two aisles next to the supermarket. And some wanted to build one in the middle of five supermarkets that look more like a small CFC, if you like. So we actually stopped using the term MFC per se and really go for highly automated solutions for the hyperlocal e-grocery business. That's a very, it's a mouthful, I know, but. Yeah.

Unfortunately, it is that complex. Our customers are struggling with a lot of questions and our solution, the hardware that we're delivering is most of the time just a very small part of it. It's not nice that I say that because for us it's very important. But for the retailer, how do I get these people there? How do I train them? What do I do when something doesn't work? How do I do the last mile topics? It's so much more complex than a regular DC solution.

You need the right team for that. You need the right people to pull that off. It's difficult. Yeah. But that's, I think, because that was what your question was. What is MFC in 2025? I think it's no longer just an MFC. I think it's a range of solutions from very small to almost as big as a CFC, depending on where you want to put it in your system.

Interesting, yeah. Yeah, and I think it is interesting too because we think, you know, a smaller fulfillment center, easier to handle, right? But as you said, you know, there's a lot of complexity, right? Especially when you're looking at from a grocery perspective, right? You have those temperatures for sure, which make it complex. And then you have timing of, you know, when can things go out and, you know, do you get congested? Do you get backed up in that situation? So, I mean, where do you see...

I guess the idea of, we said highly automated, hyperlocal centers. I think I almost got it there. It's definitely a lot more than NFC, right? Yeah, I might need to work on that a little bit more. So, I mean, where do you see like the right focus?

fit for these? I mean, like what, what industry does this make sense? Because, you know, we certainly saw companies that were just trying to do, uh,

micro-fulfillment to, you know, do delivery in 15 minutes, right? Or deliver something in two hours or same day. And some of the things that, you know, I think got into the mix there were things that maybe people were like, well, I don't need this in two hours, right? Like I'm fine ordering this normally. So, so what, what,

Where do you see kind of like the right fit for this type of solution? Is it just in the grocery industry or do you see other applications? What type of, I guess, companies or products should be looking to have this type of setup? I think that's an excellent question. Anything that goes with e-commerce could use at some point probably hyperlocal solutions like MFCs, CFCs.

Because, I mean, before the pandemic, it was going up, I don't know, it's something like 3%, 4%, 5% a year, the whole e-commerce business. Some countries were far more, well, adapted to it far more easily. Like we've done some stuff in Israel that went really well. I know that some Asian countries are really picking up on the e-commerce stuff. They have high percentages.

Whereas Central Europe and I think also the States back then, it wasn't that much. It was going up slowly. Then you had the pandemic where we couldn't do anything else but order online. It went up crazy. And all of a sudden, we all thought naively, I think, that this would go on forever. And obviously, it didn't. When the pandemic went down, people went out and went shopping in brick and mortar again. And somebody told this to me a month or so ago, and I think it's absolutely right.

The buying behavior, especially in food retail, to change that, to change people's buying behavior, apparently is something deeply psychological and very difficult. So, you know, you saw your mother go to the grocery store, your grandmother goes to the grocery store, you go to the grocery store to pick up your groceries.

you don't get them online. You might get your toilet paper, but you don't do the rest. Apparently, it's in ourselves. I don't know. I don't know if that's true or not, but what we see is that the behavior is very different per country. In certain countries, people pick it up and it goes faster. In other countries,

It takes a longer time. But having said that, it does go up. It goes up every year, the whole e-commerce business, the whole e-grocer business. So I think eventually it will be interesting for all branches of

Of course, we do a lot in food retail, but I think general retail is going to pick up more. Obviously, fashion is doing this already big time, but why not healthcare? Now we have two or three times delivery to a pharmacy, especially here in Austria. I don't know what the rate is in the States, but a lot. I think the next step is that people are just going to get their stuff delivered directly at home. And why not? Yeah.

So to answer your question on what branches, I think almost all. Everybody that's doing something online will eventually go there. Do they need a 2L MFC or a small CFC or do they need a huge VC? Well,

Unfortunately, I don't think there's a real one fits all answer to that. Everybody has a different region to work in, different demographic stuff, the whole infrastructure, where are your shops now, where's 80, 85% of your customers within that range, et cetera, et cetera.

You need to find a partner to discuss that with, a partner with experience to really figure out what is the best solution. And you know what? If you don't have enough orders yet and you're just building up your marketing and people are just getting used to buy your type of products online, I think automation is just not ready for you yet. You need a certain amount.

of orders to make it worthwhile. Otherwise, you're just losing money. Having said that, if you have a certain amount of orders, automation is definitely sure there to give you that 3%, 4%, 5% margin, for example, in food retail. Yeah, absolutely. And I think that's a great perspective there because I think there is, you know, opportunity. I mean, we see certainly where

Certain stores are leveraging their brick-and-mortar space, essentially, as like an MFC, too, and doing fulfillment out of them to be local in that sense. But I think that's a great point and kind of ties the conversation all together. You've got to look at the data, in a sense, right? I mean, where are your customers at, and how many orders are you getting, and what makes sense there to make that step? And is there demand, right? Like, you know, is...

you know, there's certainly something to be like, Oh, we could, you know, deliver your lipstick to you in two hours. Right. But you know, how many scenarios are there where, you know, you need lipstick in two hours? I mean, I have no idea. I don't wear lipstick. I'm sure. I,

I totally agree with you. I think, for example, from our point of view, we were naive there as KNAPP as well in 2019 and 20, where we were just trying to sell that for every situation. Literally thinking, you have 200 orders a day now, but if you have this automation, it'll go up to 600. Well, guess what? It does not. A lot more comes into play, exactly like you said.

do the customers indeed need that speed? And if not, then maybe a different solution is better. So I totally agree with you. It doesn't fit every situation. Yeah, absolutely. I think it's very interesting to see how that's evolved because it was such a hot topic and certainly something that I think caught a lot of investment too in the investment space and these different companies trying to

do micro fulfillment in different types of ways. So it's interesting to see how that's kind of changed and evolved a little bit. And even the idea of like, well, everybody wants everything right away. I think that that perspective has changed a little bit too. And, you know, even though we saw, you know, Amazon kind of pushing that in a, in a sense, I think that they have, they obviously have the infrastructure to try and push those types of things. And then if, you know, you're just an e-commerce brand trying to,

jump in there and do the same type of thing, you certainly don't have that infrastructure and, you know, demand built up quite yet. So, so very interesting to talk to you here, Reese, and learn a little bit about your perspective. And I think, you know, the data side, all things point to data, I will say. And, you know, I think even in our discussion around MFCs, it certainly proves that as well, you know, what does the data tell you that you

should be doing and what would make sense for your market and the customers that you're serving. So very interesting to talk to you here today and learn a little bit about this and what you guys are doing for the data side of things and also the hyper local, highly automated MFC side of things as well. So if people are interested in getting in touch with you or learning more about Knapp and what you're doing, what's the best way to do that?

Yeah, thank you very much for having me as well. It's always nice to talk about it. And I like to talk about it too much sometimes. So thank you for giving us that time. And yeah, I'll work on the marketing message for the automated solutions in the hyperlocal e-gross market. Yeah, I mean, this is supply chain. We love acronyms. We need an acronym. We need a proper acronym.

we'll figure it out now it was and we'll see where it goes but i'm i am convinced that this mfc cfc business is going to grow it's not going to go as fast as we thought but there's so many reports and so many people you talk to to say well actually you know it's going to come the pressure is not there yet you know three four five percent growth a year doesn't really justify that you immediately switch everything

But I guess being prepared, talk to companies like us, talk to people, figure out what, you know, from what point on would it make sense and in what form would it make sense? That's one thing. And also the whole master data story, as you say, I mean, it's really my favorite topic, to be honest. I've been working in this for so long. We have to figure it out. We have to figure out how we get better quality data and more attributes for this data in the whole supply chain.

far earlier on. And this can only be done by building bridges between suppliers, retailers, organizations like GS1, suppliers like KNAPP, and maybe even also end customers. I don't know. Yeah, absolutely. And what's the best way to learn more about KNAPP? To learn more about KNAPP? Well, you can give me a call, but we have an office in Atlanta where we have almost 800 people now. So they're more than happy to talk to you in the United States.

Obviously, we have our website. But I think if you want to learn really more about where you are with your master data, drop us a line and let's talk about a pilot project. It doesn't cost anything. It's free. It's together with GS1. So it's a nice project. It takes about three, four months. And then you get a really clear view on where you are with your master data when it comes to automation. That's something we offer regularly.

When it comes to automated solutions for hyperlocal e-grocery, I guess a conversation would be best as well. Just sit down, look at your order structure, look at what your plans are for the future and get a real feedback from our experience from the last six, seven years. What would make sense and what would not?

All right, great. And we'll definitely put information so people can easily find all of that at thenewwarehouse.com as well as in the show notes here. So, Rhys, thank you very much again for joining me on the show today. You've been listening to The New Warehouse Podcast with Kevin Lawton. Subscribe and check us out online at thenewwarehouse.com.

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