Mike Graen sits down with industry experts on Shelf Scanning Robot Technology and impact to the Retail industry. Panelists include:

David Pinn – CEO of Brain Corp

William Santiago – CEO of Badger Technologies

Brad Bogolea  – CEO of Simbe Robotics

Luis Vera – CEO of Zippedi Robots  

Hi, my name is Mike Graen. Welcome to the Retail

Supply Chain Leadership Initiative. In this University

of Arkansas Walton College podcast, we will explore the

importance of on shelf availability to customers for

both brick and mortar shopping, and online shopping. We will

discuss some of the challenges of getting product on the shelf,

including things like on hand accuracy, store operations,

challenges, and of course, the supply chain interruptions. We

will understand the root causes of these challenges and

understand what solutions are out there today to improve those

situations. In this podcast, I'll be talking with academic

leaders, practitioners and solution providers to get their

thoughts on what it takes to drive on shelf availability in a

retail environment. Let's get started.

We have been doing this OSA podcast since about April of

2022. And I've had some great feedback. We really have tried

to leave this with the goal of the initiative to surface the

challenges and opportunities of on shelf availability, focusing

on shoppers buy online pick up in store shopping, we have

received some feedback from the folks who are following saying

the name of our podcast is just too complicated. So we're gonna

refocus it to say, Supply Chain Leadership Podcast, Season Two,

focusing on Retail On Shelf Availability.

And we have a great topic this week to really feed into that,

which is the idea of using shelf scanning robots that you see in

retail stores. They go up and down the aisle and they are

doing audits on making sure that product is on shelf is the

correct product. And the pricing is correct. On today's podcast.

Please join me as I have David Pinn who's the CEO of Brain

Corp. BJ Santiago, the CEO of Badger technologies. Brad

Bogolea, who's the CEO of Simbe Robotics. And Luis Vera who is

the CEO of Zippedi Robots.

They're going to talk about the role that shell scanning robots

play, in addition to their other tasks in a retail store, to

collect this on shelf availability data, and to derive

alerts, through store associates and third parties. Please join

me in welcoming our guests. My name is Mike grain, and I'm

going to be the moderator for this particular session. Just as

background, I've been in the industry for about 40 years,

both with the CPG company like Procter and Gamble have worked

for Walmart and I have my own consulting company now called

collaboration, I'll see that was no no surprise focusing on on

shelf availability. This is going to be hosted. And you

know, as I shared last time, you know, part of your things, when

you hit the kind of that end of your career you start thinking

about is how to give back to the industry. So I'm volunteering my

time to give back to the industry in the spirit of on

shelf availability, because I think it's an important topic in

today's retail world. It's being hosted by a couple of different

groups. One is conversations on retail, which is led by Matt

Pfeiffer. It's a great, it's a great platform to be able to

hear from industry experts and hear real content about what's

really going on in the world. So if you're interested, please

connect with that he's on LinkedIn, as well. And he's

actually moderating this call. And then, of course, the

University of Arkansas, the Sam Walton College, we would

certainly not be able to do this without their help, and really

the retail supply chain initiative that is coming out of

the supply chain organization.

So a couple of logistical rules. First off, you know, that we

want this to be conversation and very interactive, although it's

hard to do that with as many people on the call. So we would

ask you to stay on mute unless you have a question or a

comment. That's primarily for the presenters who are going to

be here. For the presenters, we would ask that you keep your

video on so we can see you. And we'll use the chat function

primarily to get questions from the audience. Some of them send

in questions ahead of time. Others will certainly be using

the chat function to answer any questions. So we do want this to

be a very interactive session. And because we have, you know,

four direct companies who are directly competing with each

other, we're going to ask that everybody refrain from asking or

doing anything that violates antitrust, anything that looks

like pricing, or discounts or timing of changes or any you

know, things that are not of public knowledge. We're really

here to educate our retail partners in our in our FMCG and

CPGs. partners about the role that robotics plays in retail

specifically as it relates to on shelf availability.

Okay, So with that, I do want to go ahead and just set the

backdrop for this is, we showed this screen last time, and I

think is really relevant that there's a multiple ways to

measure on shelf availability. And some of these would work

actually together. The one we talked about a few months ago

was algorithms. So algorithms that pull in data and then tell

you or predict for you, when you actually have an on shelf

availability problem. We have a bunch of companies out there

that are doing typically crowd sourced store audits like field

agent, and in tracks and gigwalk. And some of those

folks, the one we're going to be talking about today is the role

that the shelf scanning robot plays in on shelf availability,

measurement, and out of stock detection, etc. And then last

but not least, specifically for apparel in general merchandise

kind of merchandise. RFID is playing a very, very important

role with some retailers.

So we are we are very, very fortunate to have a number of

different experts on the line. And we're gonna go ahead and

start this off by literally having them unmute themselves

introduce, we'll just do it in alphabetic order just to keep it

as fair as we can. We'll start out with David, we're going to

start out with you withBrain Corp, you want to give us a

little bit of an introduction to yourself and Brain Corp and kind

of things that you would like to share about Brain?

Absolutely. So first of all, thank you very much for

hosting us and for inviting us to this conference very excited

to talk with this extinct a distinguished group of people

about this important topic. So my name is David Pinn.

Currently, the CFO of Brain Corp going to be stepping into the

CEO role here in a couple of months. Just to give you a quick

intro about Brain Corp. We're a San Diego based company team of

innovators really focused on building intelligent tools. For

robotics, I think one of the things that really

differentiates our company is the scope and the scale at which

we operate. We have over 20,000 robots deployed across the

globe, pretty well focused in high labor rate countries, as

you would imagine. So North America, Western Europe, Japan,

and other countries in Asia as well. You can see some of the

Logos where we've deployed still today. And our focus to start

really was on floorcare robotics. And from there, we

migrated back into other areas as well, which I'll talk about,

if you just want to flip one slide over. You know, one of the

things that is unique about our business model is that we work

in concert with manual equipment, manufacturers. And so

Brain CT is a company that provides AI solutions to

manufacturers, we don't make robots, we make technology to

help others make robots. And you can see here, a pretty broad

portfolio of floor cleaning equipment. The one that I'll

call your attention to is on the upper left, which is a machine

that's got there you go, which is a piece that's got a scanning

tower on it. So this is a machine that can both scrub the

floor as well as scan the shelves. And that'll be the

topic that we talk about today. So big focus on retail, but of

course our deployments are also in airports, hotels, hospitals,

malls, universities, etc. So again, delighted to be here. And

thank you for including us.

Awesome, thank you very much. And we actually have

a small video here of actually a robot which is the shell

scanning robot looks like it's a new said Sam's Club set where

that is exactly right. Okay, very good. BJ or Mr. I'm sorry,

Mr. Santiago. I shouldn't call you BJ - William Santiago. You

want to take it up from here. You're from Badger Technologies.

Yeah. Hi guys. I'm BJ Santiago from Badger

Technologies, the CEO. [I] know many of the guys in the panel.

Luis, first time we're meeting but know the other gentlemen.

Great to meet you. Badger, as you may or may not know, is a

global automation solutions company where we combine

advanced analytics and machine learning to convert the

retailer's data into metrics that they can make decisions on

and improve their working conditions. You know, we are in

what we do today with evaluating shelf conditions and hazard

mitigation, which is another application we've got. We've got

over 600 robots deployed worldwide. We are currently in

now 16 brands worldwide, eight of which are in production.

Another eight are under pilot. We have robots deployed around

the world. We have customers such as the Woolworths group,

which is in Australia and New Zealand. We have a whole two

brands of them and Stop and Shop and giant company the Northeast.

We have Woodman's, which is a large Midwestern brand

superstore. And then we have a few customers, new customers

that are in the DIY and hardware industry, surprisingly enough,

that's becoming a big adoption for us. And then we have, some

of our other robots are deployed and different countries such as

Portugal, throughout the European Union and across

domestically across the US, we offer beyond just the shelf

availability with our robots, we also have different applications

that Badger brings to the market. We have a Inform robot

that is very modular it one brand name is called the Insight

that's the inventory control robot that will probably talk

most about today. But that same robot can also do an inspect

application where it evaluates the floor conditions of a of a

retailer store, alerting on slip and fall debris and just you

know, being a good hazard mitigation tool. And then we

also have the ability to one of our newest functions. And we do

have a one our first production customer, which is the National

Veterans Museum & Memorial in Columbus, Ohio, but we have a,

we offer a security bot, where the robot does. Basically third

shift augmentation, checking for windows doors, defibrillator

errs, fire extinguishers, things of that nature where they don't

want to use a full time employee to do those simple tasks. And

then we also have a UV bot. And soon coming the November

timeframe, it's right now it's in beta with some testing and

research and development is an RFID. Robot as well. So we have

one robot that can do three modular applications. And we

also have a separate robot for the UV and the RFID. Our robots

to date have transversed within stores over 1.3 million miles.

So very proud of that name, navigation capability without an

incident, which is great. I'm really super excited to be here

with my colleagues. I think that you know, for great guys, you

know, going after the same thing and servicing the customers the

right way. So, you know, just I'll learn today as well. But

Mike, thanks for having me. And Badger super excited to be here.

Absolutely. Thank you, BJ very much. I think the

next one is Brad, from Simbe. You want to go ahead and go

ahead and unmute and introduce yourself and your company.

Absolutely, Mike, and thank you again, so much for

the opportunity to be here. Look forward to our discussion today.

You know, at Simbe, we're a full stack provider of computer

vision and RFID based skosh shelf scanning solutions in the

markets. Our core flagship product is called tally, it was

actually one of the first production solutions in the

market to be available. And we've had the fortunate

opportunity to do large scale work with more than a dozen

retailers across more than four countries. So today as part of

our discussion, given we all have limited time together,

we'll get deeper into, you know how these capabilities unlock

value and what these solutions look like at scale. But look

forward to the discussion. And I believe Mike has a brief video

here just to highlight our capabilities and solution and

"Everybody thinks well, it's real simple. You know

action.

how much product you shipped to the store? You know what went

out the front end? Why do you get out of stocks? Well, it

assumes a perfect world. There's a lot of different variables,

and that's where Tally comes in."

"Tally increases our stock position, it will make sure that

our tags are accurate, our prices are accurate. It'll make

sure that the products are laid out in the right space mental

the robot determines there's an out of stock within 15 minutes

one of our teammates gets a message on their handheld saying

go determine if this product is out of stock and we need to

place an order when you think about the benefits that we will

have to our entire customer base. It's a great value

proposition. Tally knows how to interact with people in the

aisles that avoids them. If aisles too busy it'll skip it

and come back. So it's not a disruptor to our stores. It's

actually a major enabler."

Great, Brad, anything else you want to add?

No. Let's get into the discussion.

Okay, we got one more, Luis, you're up. Besides

being a really, really cool name, it's just fun to say. Tell

us a little bit about Zippedi .

Okay, so hi. So first of all, thanks for having having

us on. It's it's great to be with my get to meet all my

colleagues for the first time. We're probably the or the latest

comer to the party. But we had tried to do this in an earlier

company before. So basically our vision is is that you till like,

when you look at the internet, the first companies that were

able to get on the internet were, were the ones that had

structured data like banks and so forth. And we believe that

that, and our focus is to digitize the stores so that so

we can turn unstructured data of the store into structured data,

once you have that structured data, then you are able to do a

whole bunch of applications that will will will solve real

business problems. And so we've focused on first of all, getting

the accuracy we focus very heavily if that if that what we

call the digital twin is not accurate, then you're pretty

much dead. So we are first first order of focus is accuracy. So

we've got the accuracy down, we're up over 90-95% in the

metrics that we provide. And and then with once you have that,

that information, now you can start doing applications. So

some of those applications are out of stock, price tech, but

where we are venturing into other things we're working with

last mile delivery. And then there's, there's even, we're

doing some stuff with virtual online virtual shopping, that

this digital twin will be useful. So we actually started

in Latin America, which is kind of what what brain was saying

that the focus was high labor, we actually started a low labor.

So we we focus also on getting a very economic solution. And, and

today, we're we ventured into North America, and we have says

like about almost 300 stores deployed, and deploying very

quickly. So this is a thing that's, it's, it's, it is a

thing. And we're very excited to be part of it. So anyway, I

think and I think all of us can really help move the market on

this, because not not everybody understands it that way. So

anyway, thanks for having us. And I'm really eager to get and

move forward to the conversation.

Excellent. Well, thank you all very much for

being part of this on a busy Friday and probably been a busy

week for everybody. We have about 43 people on the line

right now I did have a couple of retailers asked me if we could

reschedule this this morning, some pretty important retailers.

And I told him that would be really hard to do. But we are

going to be making this available through 'Conversations

on Retail' and with the University of Arkansas online.

We've got these guys's permission to record this

session. So all the questions and all the videos that you've

seen will be available for you. So if you've got other people

who wanted to attend, but couldn't attend, for some

reason, just let me know. And I couldn't get that. Secondly, is

I've got a series of questions, we're going to go kind of go one

by one. But we do want to make this a conversation. So if you

do have a question, use the chat function to send it to me. And

once we get through these couple of questions that I've got for

these guys, I'll open it up for for the chat questions that come

through. Okay.

So the first one we're going to, we're going to ask Badger to

answer which is, okay, I see a lot of robots, I see a lot of

robots in stores. What exactly are the business drivers for

shelf scanning robots in retail?

Yeah, thanks, Mike. There's a ton of business

drivers, and the panel will probably share just different

variations of them. But from a very holistic macro perspective,

you know, ultimately, these robots are trying to improve the

on shelf availability of products. So the number one

business driver is to improve their sales. Most of the

retailers, we look at one to see a unit improvement of about 2%.

But it's evaluating his shelf conditions to make sure that the

products are there for the for the customer to buy. Another

thing business drivers giving them faster and more real time

metrics and views into the conditions of the shelves. And

when I say that, it's just a simple fact of you mentioned IRI

and some of the other data aggregator companies out there

that do great things, but most of the reports are at the end of

the day, and they're algorithmic and from the point of sale, all

of our companies allow us to give that customer several,

several views within the store throughout the day. And which

they can make actions on and do replenishments, and so forth

like that. And other business drivers, enhanced Analytics, you

know, trending data. And also operationalizing the data. I

don't know if we'll talk about that today, but that the

insurance that you can operationalize this data, and

that's a collaborative effort between our companies and the

customers and working together because they we can we can give

them as much data as they need. Right. But the fact of the

matter is, you have to match that up with operationally how

is there change management able to use that data. So that's a

big business driver. You'll see a lot of times we'll say, well,

we can operationalize that right now. From a very, very high

level, you know, some of the business drivers or identifying

out of stock, looking at the price, the planogram compliance,

checking for price integrity issues, whether the regular

price or wrong price, product detection, understanding if

that's the right product or the wrong product, right. So there's

spread in the industry where sprite boxes are over the coke

labels, right? An associate will walk by that for an inventory

control and think it's a beautiful looking shelf and

won't won't scan it are identified as being out of

stock. And our technologies are used to identify the wrong

products, and product location. You know, today we have a

customer that uses our product locator to actually update their

consumer mobile app. And so you know, we can identify where

things are on the shelf from an xy coordinate, be that

information to the retailer, and, and help them out in that

way. So if you look at it's very simple, the business drivers are

improved customer experience, improve sales, repurpose labor,

we all know there's a labor shortage out there. There's

other things around supplier collaboration that I'll let the

guys talk about working capital, right, having, you know, less

safety stock in the back of the stores. So ultimately, it's

improving that customer experience is probably the

number one driver while other organizations are using us.

Awesome. So Brad from Simbe, how does this thing

actually work? I mean, we saw your video where that actually

navigating through the store and going around people what exactly

is it collecting?

Yeah, absolutely. Mike, if we if we kind of take a

step back, I think there's an important question to answer,

unlike what does it take to make these solutions work in the

environment and the reality and the beauty of this type of shelf

scanning technology is it doesn't require any

infrastructure changes to the stores, right? By purely

leveraging the retailer's Wi Fi connectivity, a small parking

spots with on the store floor and, and a power outlets, these

solutions can get up and running rapidly. So the way they work is

when you initially unbox them, they're essentially using their

sensor suite to build a high quality sort of digital twin of

the store from a map perspective. And once you have

that map, the solution can understand where you know,

really all the shelving fixtures are in areas of interest that

you want to scan. And once those have been defined, it's really

about architecting missions, that make the most sense for the

retailer and their business processes. So today, we see

these solutions, you know, operating traditionally, during

normal store hours, you know, sometimes overnight, but often,

you know, blending seamlessly in with customer traffic, and

employee engagement in the environment. And it is it's

going up and down the store aisles. It's capturing things

like high quality 2d and 3d data to make precise, out of stock

detections, you know, do facings analysis, really extract the

price and promotional tags throughout the environments,

were often seen that about 30 to 50% of out of stocks are

controllable across the grocery space today. So huge opportunity

to get product back on the shelf. It's not unusual for us

to step into a store and see three to 5% of price tags being

incorrect for the first time. And as was just shared, you

know, the solutions are capturing up to date product

information as they go. So in this highly dynamic supply chain

environment that we're in product could be in a core

aisle, it could be on a promotional display. How do you

keep an up to date understanding of where that product is. So

those are a number of the capabilities on you know, sort

of the computer vision side on the RFID. Front, it's very

similar. So by using RFID antennas, you essentially have a

mobile RFID reading platform that can take precise inventory

counts. And cycle counts across these environments, as well as

be able to triangulate specifically where that tag is

in the store, which is deeply valuable for those high value

goods retailers or apparel retailers. When you know folks

can order online and pickup in store, they're doing shipment

out of store. But all of these insights are then essentially

made available through a lot of the retailers traditional

business tools. So it's their workforce management system,

their supply chain or computer assisted ordering system, as

well as the consumer mobile app and other third party systems.

Perfect. Perfect. Thank you very much. Luis from

Zippedi. We've got a two part question because I'm trying to

combine the questions I gave you as well as some ones that are

coming in, by the way. We already have like six questions

out there. So it's we're going to be moving to open questions

pretty quickly. So adoption is expanding: why? And to build on,

and that is, what exactly what exactly is Zippedi n the US? Do

they have any US locations yet? Or what's your plans in the US?

Yeah, so, so answering the first question is,

so I think this, again, the, especially the whole pandemic

thing has put a lot of stress on these on these companies, and

then you have the whole digital digitization movement that we

talked about. So, you know, we believe that there's going to be

one of these robots in every single store in the future at

some point. So I think it's just a trend that's that's going to

is going to be happening is going to get stronger. And as as

retailers start to appreciate the value that it this brings to

help streamline their operations and bring down costs and

efficiency, because we've been able to show that efficiency can

the the efficiency can increase by 30 to 50. In some cases, 50%.

So where you where you have one person doing one thing, you You,

you, you actually have so two people doing one thing, now you

have one person doing. So that's another with this whole thing

that's going on right now with the labor is again, it's another

huge, huge thing. So I just think it's, it's something

retailers need to do. They need to go from unstructured data to

structured data, so that they can apply compute power to their

to managing their stores up until they today is pretty much

manual, in essence. So and your second your second question ...

What retailers is Zippedi working with in the US?

Yeah, so we have a lot of like, non disclosed,

we're not allowed to disclose publicly, some of the big ones

we have. But we are we right now we're of the 300, almost 300

robots that I mentioned, it's about 5050 us and in Latin

America, but Latin, but the US is growing very, very quickly.

We expect this year, we have a good possibility of overcoming

something like 700 robots this year. So it's growing very, very

quickly. And it's, it's, it's huge. And then and on on on that

note. It's, again, we're we believe that retailers will see

the value and start implementing this because it's something they

they have to do.

Excellent. Thank you very much. David - last but not

least, Brain Corporations. You already mentioned that your your

robots perform a number of functions, obviously, floor

cleaning is one of them. Obviously, things like Shell

scanning robots, which is what we're doing here, that's one of

the value propositions where these robots can provide

multiple functions to the retailer. The question for you

is kind of what kind of alerting just in the shell scanning robot

part of this business? Does a retailer actually get with the

solution that you provided?

Absolutely. And I just to dovetail on what Brad

was saying, you know, the output of these robots go to a number

of different environments, right, so the output can go to

an inventory management solution, the output can go to a

customer facing shopping app, the output can go to a store

employee to help them, you know, reconfigure the shelf. And so

those alerts can go to a number of different areas, I would say,

you know, there's three large categories of alerts. When we

think about, you know, in store execution, you know, there's

stock level type alerts, you know, low stock out of stock,

there's price tag exception alerts, so the price tag is

incorrect, a promotional price is set wrong, you've got

planogram compliance type alerts, so the product is in the

wrong location. You know, sometimes they'll plug a hole in

the store, so it doesn't look like they're out of stock, but

the wrong things in the wrong place. So you know, those kinds

of alerts are really important, or the wrong number of faces. So

those kinds of alerts, I think, are really critical to help the

store execute using using this technology.

Perfect, perfect. Okay, we're gonna open it up to

the panelists. So any of you guys can jump in on this

particular question, and I'm not gonna go on my prepared

questions because we've got a lot of questions coming in from

the audience. BJ this one was specifically for you what when

does the Badger robot operate?

It'll vary per retailer and per application. So

typically, from a inventory perspective, the Badger robots

for most of our customers will run very early mornings,

depending on their geography location, the 536 30 type of

timeframe. What that enables you to do is to get a pristine look

at the store conditions after they have fixed the store from

the night before. gives them the ability to get corrections done.

Mid mid morning before the afternoon rush is. So from an

inventory perspective, most of our customers are doing early

morning scans. Now, that being said, we also run scans

throughout the day for all of our customers and typically

addressing their fast moving or D SDI type of items, right,

anything that's fast moving in the store, that they may not

have direct shelf edge cameras on there, you know, in their

stores, but they need to see it more often. Right. So if they're

not using fixed cameras, where they have to look at something

more frequently, then we're doing a lot more runs. And then

we typically do a, an evening run to and that will vary

anywhere from 630 to 930, in stores, and then we have some

international stores that are open 24 hours. So it's we run

based on the stores operational needs, right, based on their

supply chain needs and the way they're addressing other

replenishment. So hopefully that answers the question.

Anybody else want to add or build on that?

We get i I'd say, again, I concur. That That

depends on the retailer, we've seen that, that the difference

between when you're going with customers in what it does is

just make the scan a lot more inefficient and slower. But

eventually, you can deal with customers in it. But it's better

to try to do it. And when when traffic is low. That's either

either that or replenishment activities that are going on.

You're you're trying to look for those windows where where it is,

but you do not want to want to, you know, complicate the

customers when they're buying because his robot is going by

him. But they are fully autonomous. And they do wait

until the customer is out of the way. But still, it's preferable

when there's less customers inside.

Yeah, Mike, I'll add to that. Just real

quick. We're Lewis said, I think we all all of our companies will

work with the retailers on holiday schedules, you know,

high traffic times where we just may not run the robot. But

that's it again, in collaboration, we don't make

those decisions, we make them together. And that's driven by

the retailer.

Perfect, perfect. This one is a sensitive one

about it. We got to be really, really careful with this one.

But I want to lay it out there because it's a question on

people's mind. It's kind of a two part question. The first one

How does Brain charge their

customers for scanning the shelf service? That's a pretty

specific question. And again, via antitrust, we're not going

to get into specifics. But there was another question that was

similar. What is the business model for each company to sell

or rent the robot? And what's the price range? I'd like to ask

you guys to kind of avoid the whole price range, unless you

guys want to agree on one big number; so we're all going to

jail. But basically, I think there's a question out here is

okay, this really looks cool. There's gonna be an ROI. And

maybe we'll start it out with you, David, because you're sort

of the CFO moving into the CEO role. How do you ... how do

retailers think about the value proposition versus the cost of

this thing? And how do they end up paying for the service?

Yeah, that's a great question. So and I think I speak

for all of us that, you know, largely, it's a SAS Type

offering or a RAS type offering, as we say, in this industry. So

robot as a service, or software as a service, where people are

typically paying a a per store per month type of fee, I think

is a very typical business model here. So we won't obviously get

into pricing either. It ranges from zero to infinity. There you

go. In terms of in terms of the payout for the investment, you

know, I think it was stated here before increased sales, I would

say is, you know, by far, the biggest thing that retailers are

looking for here. So it's, you know, it's a 1% increase in on

shelf availability equals a 1% increase in revenue, you know,

that kind of formula, I think, is, you know, really driving a

lot of this adoption, there's, of course, also just

efficiencies in terms of, you know, having the workers in the

store, the associates being more effective at their job. And that

can be a big ROI driver as well. The third one, which kind of

combines these are increased customer loyalty, right? So you

have a better customer experience, where people being

able to find what they're looking for when they go into

store, you know, that's going to improve repeat shoppers. And so,

you know, really, it's that that kind of three part value prop on

on the payback side. On the cost side, of course, you know, we're

all working to bring down the cost of these solutions. You

know, I think on our end, you know, the Brain CT magic really

is about partnering with third party, existing equipment

manufacturers, so we're leveraging scale of existing

industries. We're leveraging existing service sales and

support networks. And so for us, you know, that is a lever that

we're able to pull in order to bring costs down and really give

retailers comfort in choosing a robotic solution from you know,

potentially a vendor that they've been using in the past

for other types of equipment. So we're very focused on you know,

those kinds of eco system synergies to bring down the

total cost of ownership and really amp Apply that ROI.

Perfect.

And I would add to that, that it's, it's these,

these, these you have to, you have to be able to manage these

machines, if you will, on a broad scale. And that's

something that the the retailer is not gonna be either equipped

or want to do. So. So that's why the RAAS model or the robot as a

service model is the most adequate in this in this case.

Mike I'll also the you know, there's a, we'll

see different variations per geo stew, I noticed RAAS as a robot

as a service is more popular, from our perspective, from the

Badger view in Europe, where the US is still mixed with a CapEx

approach, and our and a robot as a service. So again, it's just

varies on how the retailer's doing their, their economics,

right, and how they want to acquire it. But I think all of

us offer flexibility and addressing the way that they

want to buy the products.

Absolutely. Perfect. So so this is a this is a

question that actually got before the conference started.

So you probably won't see it on the live chat. But what

percentage of grocery stores in any market need to be covered

with robots for the solution to be considered mainstream, and

there's an add on today, it still feels like a niche.

Although each one of you talked about a lot of robots being

deployed, you still don't see them in major, you know,

retailers? So where do you think this is gonna go? Is this? Is it

a niche thing? Or is this something that's really going to

have the scale to really drive the industry in terms of a new

way of collecting this data?

So as I said, before, we honestly and firmly believe

that there will be one robot in every retail store. It enables

you to play compute power to manage your store, that is not

possible today. And so the answer is all. Go ahead Brad.

Yeah, absolutely. You know, we believe strongly

similar to what was just stated, this technology will become

ubiquitous, I think it's actually operating at a far

greater scale today than folks realize. Just because, you know,

the the press release and sort of public persona to sort of

this technology has maybe not been disseminated in in sort of

a number of cases. The reality is beginning to see this data in

a single store is incredibly eye opening for a particular

retailer, we all have to keep in mind that these stores have

never been captured at this frequency and fidelity that this

technology provides. And day one going into a store that you may

have a store manager that's been there 40 years, you will know

more about the sort of true state of the shelf, then then

they will and so that's why it is super empowering. Even

starting at sort of a smaller scale. We often see many

retailers today, starting with sort of the 10 to 25 level, unit

wise to get a good sample across their geographical mix store

execution mix, store revenue mix stores that may have different

assortments and it really helps to paint sort of a complete

picture of you know, what their environment looks like. Now, of

course, to maximize the amount of benefits you can get. It's

really about sort of going chain wide. But we're seeing retailers

today employ not only this data for themselves, but with a 10 to

25% sample of their stores being able to monetize that data

downstream with brands and other third party stakeholders within

the environment. So what's clear is this solution is not only an

operational and sort of data solve for the retailer, but it

can actually become a profit center.

How does it How does it become a profit center?

Yeah, absolutely. So when we think about the

stakeholders, this data is valuable, too much of the

discussion here has been, you know, retailer centric, as we

know, this solution incredibly benefits the consumer shopper

themselves by improving store execution. But there's so many

folks in the retail value chain that operate in these stores.

You know, we have brands, both warehouse brands and DSD brands.

So this data can be valuable for DSD players to understand are

they executing properly at a store level? We have DSD vendors

today that are changing their routing mechanisms from schedule

based to demand based based upon what they actually see.

Warehouse brands for the first time in their history, they can

really understand did our product not sell today because

nobody bought it? Or did it sell not sell today because it wasn't

actually on the shelf? And that was a question you were never

able to answer before until you have this level of

instrumentation. And when you think about the other players

this data is valuable to its folks like E commerce players

the INS The cards to the DoorDash is of the world to

optimize both the online experience of what products are

available now as well as to optimize, you know, picking and

path planning and which stores they go to. And lastly, as well

as the market inside companies, right? If you combine this with

the incredible datasets that the Nielsen's and I our eyes of the

world have, you can answer questions that have never been

answered before.

Awesome, maybe just to just to amplify, you know,

what Brad was saying about the the apparent adoption versus the

actual adoption? You know, there's a lot going on, you

know, kind of under the tip of the iceberg that's not in press

releases in this area. You know, I mentioned, you know, in my

introduction, we've got 20,000, robots deployed. You know, we've

announced a full deployment at Sam's Club, which is 600 stores,

I know, these guys have made a few announcements as well. But

there's a lot more going on that's not announced. And so I

think the level of activity in this space is understated,

really.

Yeah, we're starting to see now. You know,

it's great to have competition with our colleagues here, our

robots can be in the same brand at the same time at different

locations, right, they're just testing the abilities each

company might specialize in or have different nuances. And so

that's, you know, that's becoming more frequent. Also,

I'd like to add, and I don't know if my colleagues are

experiencing this, it's a very complex sale, unlike, you know,

software technologies and other things like that, when you're

mixing the analytic platform, or along with robotics. You're

dealing with a lot of people that can say no, versus Yes, you

know, you're dealing with the merchandising, the it the

finance, the supply chain, a lot of us start the innovation

group. So there's a lot of people evaluating and learning

the technology, we're teaching them at the same time, on a lot

of this technology. So there's a lot of people involved in, in

rolling this out. And getting as David just said, you know,

official press releases out. But I think we're all doing a great

job of diligently working with with many brands out there. And

so I think just the, the buying cycles are a little bit slower

than all of us would like to see,

Yeah, the buying cycles are slow. And then it's

still kind of niche, as you mentioned. But as as it becomes

more valuable, and people start to perceive it should become a

movement. It's what we believe.

Yeah, so So quick question. Well, I'm going to I'm

going to ask these questions differently. We're getting a lot

of questions around alternatives. In that first

slide that I laid out, I showed that algorithms can be used, if

you have high velocity items to show things are out of stocks.

We've got things like store audits, we have third parties,

like a field agent, or gigwalk, are tracks going in and take

pictures, they can do some of the things you're doing another

one that was added, which is a great one to catch, which is,

hey, we've got people who are putting fixed cameras in what's

the value proposition of the fixed camera, etc. So I think

these are all very, very fair. Help us understand why a

retailer would choose a shell scanning robot versus some of

these other alternatives.

Yeah, so so just like a little bit of background, I'm

a serial entrepreneur, this is my third company trying to work

on this problem. In the 90s, we were actually putting track

cameras on the roofs of supermarkets to do exactly the

same thing. Then in the 2000s, we were we actually started

putting fixed cameras on the shelf, the what we encountered

there was that the problem is that you need to the fixed

cameras are at will at the time, they also have the power of

them, but you don't need to power them now you can put

batteries on them, etc. But the megapixel of a camera across the

aisle does not give you can not give you enough information to

be able to, for example, draw out the planogram for example,

because you can't read the labels. So so what we found is

and in my earlier company, is when it was actually a customer

that gave us the idea to put in a robot 2013 And at the time

that technology wasn't there, it only became available like 2016

17 where it was actually possible to do this. When when

when at which point we jump back into this and build zippity so

fixed cameras are and we've tried everything under the sun

humans with cameras, we tried everything. And I we again

firmly believe that these robots are the only way to effectively

capture them today and probably in the next five to 10 years.

You can think that maybe in the future you'll have drones flying

through the thing and whatever but but today with given the the

the amount of data you need to capture and with a fidelity you

need to capture it. Robots are the way to go without without a

doubt after after 30 years of working on this problem,

And if I may add, you know, we definitely see the

autonomous robotic solution being the most scalable solution

in these environments, right? We're all talking about stores

here that range anywhere from, you know, five to 10,000 squre

feet to 300,000 square feet, right. And as you get into these

large format, FMCG environments, you know, the cost of fixed

infrastructure just doesn't make sense. And as we've just shared,

you actually do not extract the same level of information, you

know, whether it's the barcode, whether it's accurate product

recognition, whether it's price on the algorithmic side look

like this industry has had algorithmic based solutions for

a long time just looking at sales velocity data, those

solutions can work for some of the highest turn products. But

what we've seen is solutions like ours put head to head

against these solutions, you can you really begin to understand,

you know, the differentiation. And what we found is there's

such a small number of skews that the algorithmic based

solutions worked optimally for. And it's, it's still a

predictive results, whereas we actually have a definitive

picture of what was happening on that shelf at that time, the

algorithmic results still requires someone to sort of go

look in and sort of verify. So many retailers will operate

algorithmic based approaches in conjunction with these types of

solutions. But what we have found is there relevant to less

than three or 5% of the SKU base. And traditionally, we

still have information superiority, even over those

skews.

And we should add that, you know, the algorithmic

approach, it works for out of stocks, but it doesn't mean it

can work for out of stocks in three to 5%. But doesn't work

for low stock doesn't work for planogram doesn't work for price

level. So it's just going to be limited solution versus what we

Yep, perfect, perfect.

can do.

For all of us, if I may add this Mike; for all

of us to be successful. I mean, this was our architecture

approach coming out, was to have somewhat of an open

architecture, understanding that this is, I think, if we're all

honest with each other, it's yeah, these retailers need a

hybrid model, right? And we need to offer that IoT connectivity

to work with the fixed camera organizations or work with other

investments that they may have made, where the robot can maybe

be the main source of that information. You know, we have

clients today, we're we're taking fixed camera photos and

ingesting them as our robots are going through the store to build

them and when holistic report. So I think that if I understand,

you know, everybody's offering pretty well, you know, we're all

open to this more hybrid type of thing, understanding that these

retailers made big investments and some of this older

technology, and it just can't be ripped out. And so I think the

hybrid approach and open architecture is important moving

forward.

And the other very important thing is the speed of

deployment. So putting in fixed cameras in a store, is a pretty

gargantuan gargantuan task. And these robots like today, we're

deploying, like, just last week, we we deployed 12 robots in one

week, it takes it once you've once you've taught the

algorithms to read what they need to read, it takes 24 hours

to train the store, and then you're up and going immediately.

So having enough teams in parallel, you basically can

scale this very, very quickly, which you can walk through with

other systems.

Yeah, especially when you remodel a store and you

completely break down gondolas and reorganize stuff, you'd have

to go and change all the cameras around and changing the pattern

of a robot would be a pretty quick and easy thing. That's

And then And then, and then because the this robot

great.

can read the labels, if those changes do occur, then the robot

just itself learns. If you had fixed cameras, you got to you

got to read program, the the system to tell it this in this

space. This is what's supposed to be here, which is definitely

not scale.

So I want to I want to ask you a question, because

I'm getting a couple of little questions that are touching

this, but you guys aren't implementing a robot in a store.

You're implementing a change management way of driving

behavioral change in the store. Because if you just run the

robot and you give somebody 1000 different alerts, somebody's got

to eventually do something with that or nothing's going to

change. Walk us through the difficulty about change

management. Who are you talking to? Who owns that at the

retailer, if you're going to share it with a direct store

delivery player that you mentioned in there, Brad? Who

would who in the supplier community you're going to get

involved with? This is a big project. I mean, this is not

just implement a robot and let it run. There's a lot of things

that have to change to actually take the changes in behavior to

Get the product fixed and the pricing fix and center. How do

you mitigate through that?

Yeah, Mike, as you mentioned, there's so much

shared value, not only across the retailer themselves, but all

the other stakeholders operating in this environment. So what all

the companies, you know, on this call had to take is sort of an

incremental approach and sort of unlocking that value. How do you

not hit these retailers with sort of the data tsunami day

one, but help them understand sort of the holistic value. So

step one, we really see as really empowering the store

teams, which are sort of the first line of defense with

helping them understand the true state of their stores, and what

issues are actually can be controlled by them. That data

may throw flow through sort of our own applications, you know,

sort of initially. And as the retailer goes to scale, this

integrates more deeply into all of their existing, you know,

sort of work streams, or in fact, what we see as many of

these business processes that you just described, actually

being re architected around our data, we become the primary

function for backroom pools, or a key input in computer assisted

ordering, you know, sort of all of those pieces. The same is

true with the external players, you kind of see this, this

evolution, what is also important is really the retailer

analyzing this data holistically from a corporate business

intelligence standpoint, for the first time in their history,

they will have the ability to understand what the true

operational execution of these stores are. And you will be able

to understand why stores in the same region with the same labor

base, same customer base, you have one that has a 6% out of

stock rate, and you have one that has a 16 You know, why

that? Why is that happening? And you're able to really get to

sort of root cause of these questions. And we see

operational leadership within these businesses, you know,

really driving different processes incentive and

accountability around this data.

Awesome.

Like their way in and out, right? It's comments

around the store teams that, you know, most of our companies here

go and we run a story valuation process. And we'll interview

each one of the store managers below the head store manager, so

the meat department, the bakery, all the different ones that

really just understand what are they what are they charged with?

What kind of metrics are they being driven by what kind of

reporting, we would help them? Right, so but by developing the

robots data, to really enhance their their operational needs,

is where we all become effective. And I think we all do

that pretty well, it's just a matter of refining that, we just

go in and say we're going to scan you know, all these things,

they may have only 50 items they can make, they can correct per

day, right, and the DSD or fast moving aisles, they don't want

to report the 600 items that are out of stock, they're saying

give me the report of these 50 that I can take action on right,

it goes back to that operational data. And we also have to get to

the place where we're filtering data for them. And I think we

all do that well, as well from the supply chain. Understanding,

if we're integrated into the CIO, or the Supply Chain system,

and we understand what's on hand, we may see an out of

stock, but then the robot and our platforms know Wait a

minute, they have five on hand somewhere in the store, and

we're reporting on that. So they then they send triggers for

their associates to go locate that information. So it all

becomes process based. And it's the matter of just understanding

those

Or the reverse of that BJ, which is I see an out

of stock. But I also see our computer system doesn't have any

product in the supply chain for whatever reason, don't waste

your time. Maybe bring a manufacturer out of stock, but

but don't send somebody to go find it when you know, upstream,

there's no product in the supply chain.

Yeah, I mean, they'll have discontinued code,

they'll have distribution can't get it to them, you know,

different codes in there. So understanding those filtering

out to where it works is important.

Yeah, and that talked to the having the store

digitized. Once you have that information digitized. Now you

can do the kinds of things that you guys mentioned.

Okay, I think the the incremental approach is

everyone's talking about is just so vital, right? Taking a

retailer on a journey toward the destination that we're all

describing, you know, for us, you know, even getting someone

started with a floor cleaning robot, right? Where, you know,

there really is no inventory data coming out of that dream,

but the stores are getting used to having a robot driving

around, right. So it's really taking a retailer on that

journey on their timescale in order to reach you know, that

really holistic outcome that we're all describing together.

And I think that's a great point, David, and I think

each one of you have already said that a different way. Our

robot performs multiple functions of store. It's not

just doing shelf scanning, and it's all it does. It may do that

early in the morning, and then it may be dark and then charge

up and then like in your case, clean the floor it BJs in your

case, navigate for spills in your case, Brad, do RFID scans

it's doing multiple different tasks. Which then leads us into

the obvious negative question is, what about people's

perception that you're taking jobs away from people? I know

you've heard that before? I'm know you got an answer for that.

But wait a minute, you're taking jobs away from humans? We don't

like that. What? How do you handle that perspective?

Probably less from the retailer, but more from the customers who

are in the stores.

It's funny. I'll start at I wonder how much

everyone will agree with me. But we heard that a lot, four or

five years ago, not hear that anymore. And so I think people

really start to take note of labor shortages, and realize

that these robots are doing essential functions that really

can't be done in other ways. And so for us, just kind of given

the macroeconomic environment, given the labor shortages that

retailers are experiencing, given that the tediousness of

this task that really can only be handled by a robot. We

honestly don't get that that pushback at all.

We don't see anymore we address I'm sorry.

Did somebody go?

No, go ahead. Go ahead.

I, we, early on back in the day, when we

brought some of the whole brands on, we had developed a

ambassador program, we actually got a third party to assist us

in educating the shopper on why the robot was in there. And

that's evolved to educating the associates now and part of our

change management program is to have a FAQ for the associates

just basic things of answering questions that the shopper may

ask, you know, what is that robot doing? Why is it doing?

And that's helped all of us with the adoption rate. And I have a

funny story where I was next to this one gentleman and he's

we're in the chips aisle, and he says, what's that robot doing

here? And I said, Well, what's your favorite chip? And he said,

Cheetos, and I said, Well, this robot looks at the shelf. And as

soon as that Cheetos isn't there, it's making an alert back

that there's, there's no more Cheetos on the shelf. And he

said, Well, that's great. If that thing's getting me more

Cheetos more frequently, then I'm all for it. You know, it's

just again, it's around messaging. And to David's point,

I don't think the culture anymore. I think the industry is

mature enough now to get used to technology like this in the

store.

Yeah, I, I agree with all of that. And I'd add that

because now you have that digital information and you

become now you can actually put a put a number on what it costs

to have an out of stock or do a specific task. And we're working

with one retailer. And for every store that they implement with

our robots, they add three more headcount. So we're actually

going in the opposite direction. And, and that's because you can

actually, so if, if, before you knew you had to do out of stock,

you'd say, Okay, I'll put one guy to walk around the store and

figure it out. Now you realize that you have that you have all

these holes, and you can dimension you can you can

dimension, the size of the problem. And now you start to

actually do the math and see that three people actually pay

for more than pay for themselves, because they're

solving these problems that are that are highly measurable. So

you went from non measurable kind of thinking, you know, 10%

of my sales are going to be overhead or people in the store,

to now a more scientific approach where, where we're, you

know, exactly what every person every person's throughput is in

light of that, and that is turning out to see to turn into

more, more more jobs. And last. And then just recently, in, in

The Economist, an article came out that studies in a lot of

different industries have also shown that robots are increasing

the the productivity and the and the and people people being

employed than the other way around. So that's that's getting

quickly demystified.

Well, in all the projects that I've been involved

with, when I've been involved with a shell scanning robot,

through a couple of different suppliers. I've never heard an

associate complaint because all of the work that it's doing,

whether it's cleaning a floor, or RFID, scanning apparel, or

have literally scanning shells for outs, those aren't exactly

exciting things to do. They're very monotonous. They're various

tedious. I haven't seen any of the robots here that have arms

that are actually stock product. So I don't think anybody's

worried at the retail that the real people who are doing the

work, are worried about this. I just I think it's a perception

of customers that you're taking jobs away from people, which

also brings to a one more question and then we're going to

open it up for any other questions that we have, because

we're just about out of time. There's a customer perspective

that their robots are getting in the way of me trying to shop.

You got all these online pickers in the row and you got these

carts in the way and you got the displays in the pay and I gotta

navigate a robot. They Have you had any perspective from

shoppers or customers that robots are an unnecessary thing,

and they're getting in the way of people literally going

shopping at the store?

Yeah, so as we said, before, we try to look at when

there's less people in the, in the, in the in the store, these

robots are autonomous self navigating. So when there is a

customer shopping, at least our robot does not bother them. And,

and it just, it just takes longer to scan. So if you do do

it in peak hours, it'll just take, you know, two to three

times more to scan the whole thing, as opposed to when it's

downtime. So our recommendation, typically, I think it was bad,

or you were that BJ was saying that, that it's preferable to do

it early, early hours of the morning, late in the in when

when the customer accounts are low.

Okay, great. Folks, we got a couple of minutes left,

I'm just going to let you guys do any kind of closing comments

that you want. We'll start David, with you, you want to any

closing comments for the audience?

Yeah, thank you very much for hosting us, you know,

we're extremely excited about the journey that we're going on

with retailers, you know, starting with automating

something as basic as floor care, and then moving up the

stack into, you know, doing shelf analytics, RFID, all that

we talked about today, you know, creating delivery robots that

allow shelf stalkers to be more efficient. And so you know, our

strategy really is about what we call automate the aisle and

taking retailers on that incremental journey. We heard

from everyone here today. These are complex sales cycles. It is

a journey that we take retailers on together, we want to meet our

retail partners where they are, and work with them incrementally

to automate their operations over time. And we're just

delighted to be part of this industry. And thank you very

much.

Awesome. BJ?

Yeah, so you know, I echo their comments,

allowing us to be on this and sharing time with my colleagues.

So I just want to bring up one thing really quick that we

talked about, we were very customer centric here. But Brad

touched on a thing around dealing with the suppliers and

the fmcgs. I mean, this is becoming a very bi directional

information sharing so much that it's going beyond servicing the

retailers were involved in a lot of pick and pack operations. Now

on the on the side of that, because with the E curve, a

click orders now becoming you know, 60 to 70% of their

revenue. All of these autonomous robots are playing an important

role I know that badger does and enhancing their E click

operations, because I don't know if you guys my wife orders

groceries at home all the time. And she goes, Okay, this looks

nothing like a ribeye steak. Right, because they didn't know

what they were looking for. So but just for the general aisles,

I think that we're enhancing overall operations, not only

customer service, but just also their online procurement and

ecommerce operations as well.

Yeah, the robot knew your cholesteral levels BJ. So I

chose a sterile sirloin steak, which was a little bit leaner

pad of meat. And so that's really creepy. But we won't go

down that path of they actually know what your health records

are like. Brad, any closing comments?

Yeah, absolutely. Mike, you know, first and

foremost, thanks for bringing it bringing together such a

wonderful industry group, you know, not only the panelists,

but all the attendees here and, you know, creating this

conversation, you know, to echo David's comments, you know, we

couldn't be more excited about kind of where this industry, you

know, sort of is today as a company, we've grown, you know,

more than 10x, since the beginning of the pandemic. And I

think this is still the precipice, you know, we're

really at the beginning. And what I would encourage, you

know, those retailers or retail stakeholders on the phone to

really think about is, you know, this is really the only solution

that gives you an objective source of truth of what's

actually happening on the store shelves. And what most retailers

we've engaged with it, you know, often comes to such a surprise

to them, if they don't have prior familiarity with the

technology is how rapidly it can be deployed. And the extreme

strength of the overall business case, and how strong the pillars

are either on sales or margin contribution, labor efficiency,

effectiveness, as well as sort of pricing and promotion and,

and sort of the downstream sort of profit opportunities that we

sort of talked about. So, you know, definitely encourage

continued dialogue sort of across the industry. And look

forward to you know, future conversations with all of those

involved here today.

Awesome. And last, but not least, Luis.

So again, I echo the thanks for putting us together

here and helping us generate awareness, which I think is much

needed. I again, I echo the the fact that digital if retailers

want to move into the digital realm, they need to have this

part of their business digitized because it enables them to Use

compute power to be able to do a whole bunch of stuff, as you

know, the out of stocks, as we mentioned, the E commerce, as BJ

was saying, and then there's a whole bunch of other things that

you can do. So, you know, highly encourage retailers and industry

to, you know, take a look at this. And, and, and see if, and

you know, give it a try. I think it's, it's, once you understand

that it's really a no-brainer.

Awesome. Well, thank you very much all four of you

for taking time out of your busy schedule. I know you got lots of

things that you've got going on. But thank you for doing such a

great job of representing the shelf scanning robot industry.

We will be taking this and getting this out onto the

platform for conversations on retail and supply chain

organization at Walmart, you can see that via LinkedIn, we'll

have the recording of this. So if you have any colleagues who

you really want to have see this, but we're available, we'll

have that available. Last but not least, just a quick, quick

update, we've got several that we already done, we've actually

done one on algorithms, and we just completed this one

obviously on shelf scanning robots on September 16. We will

have one on store audits, whether it's field agender

tracks, etc. That is another data collection mechanism. So

put that in your calendar for a future event.

Well, I hope you enjoy that podcast on the idea of shelf

scanning robots in retail stores. In addition to cleaning

floors, looking for spills, scanning RFID tags. These

particular robots are doing a great job of scanning shelves at

a very detailed level to look for on shelf availability issues

like out of stocks, incorrect products, or pricing

discrepancies. Getting those alerts directly to the store

associate and the third party service providers to get those

corrected and rectified helps drive on shelf availability. I

hope you enjoyed that podcast as much as I had spending time with

these great leaders. Join us next week as we invite the zebra

technology company to come and share with us about the retail

smart lands platform, how it's used to read RFID tags inside of

retail stores and the benefits of that over a handheld scanning

operation. I look forward to seeing you then.