OPERATIONAL EFFICIENCY

Christina Cardoza: Hello and welcome to “insight.tech Talk,” where we explore the latest IoT, AI, edge, and network-technology trends and innovations. As always, I’m your host, Christina Cardoza, Editorial Director of insight.tech, and today we’re exploring the world of smart retail with Silvia Kuo from ASUS. Hey Silvia, thanks for joining us.

Silvia Kuo: Hello. Nice to be here. Thank you, Christina.

Christina Cardoza: Before we jump into the conversation, what can you tell us about yourself and what you do at ASUS?

Silvia Kuo: Yeah. So, I’m the Business Development Director at ASUS for the EMEA region, and I basically—my job, what I do is I look for new projects to engage in. I also look into what technology we should include in those projects. And the other thing I do is also I run the partner program, which is essentially looking for different partners to create solutions together and offer them to the market.

Christina Cardoza: Great. Excited to dive into some of that. I know ASUS has a lot of solutions and a lot of ways you can help retailers or partners in this space. But I wanted to start off the conversation, because we’re talking about smart retail, and I feel like this is a key word that has been floating around for a couple of years now. So, when we say smart retail and when we talk about smart retail, what do we mean? Are retailers there yet? Or, what are the challenges that they’re still facing to get to smart retail?

Silvia Kuo: I think that smart retail has been around for a while and everyone’s talking about it, but I think that there is this—of course, as any change, right? there’s a bit of resistance, but there is also this imminent change that you can feel. And I think that the industry has been probably trying to look for a direction—that’s my feeling—but there is a lot of innovation that we see, and they are adopting it. And I think that a few years from now retail might look a bit different from what we know it, how we know it, today.

Christina Cardoza: One thing I’ve noticed with smart retail, and I’ve noticed ASUS stands out a little bit in this space, is that when retailers do their transformation, it often happens in silos or one at a time. They do self-checkout or they do inventory. What I love about ASUS is the company has a range of products from end-to-end to really go from inventory to behavioral analytics to space optimization.

So, what can you tell us about some of the smart retail solutions out there and how that can help really transform a business from front end to back end rather than just one area at a time?

Silvia Kuo: Right. And I think that’s the reason why we have this more comprehensive, if you will, or holistic type of approach is really because of the nature of a computer company, right? So, we are the brain, let’s say, behind all of this innovation, is where we, it, runs. So, because of that, we have had to look into the different solution as a whole. So when we offer something, it’s more of an end-to-end. It’s, we are looking into a problem and then seeing what components to put into a solution to solve this problem. And that is why it’s this holistic approach.

And I think that in retail, some of the things that we are doing today fall—I think it always falls into two categories: Either it is gathering data in order to allow to analyze this data and further make data-driven decisions, or is automating process. And I think that in retail it is very important, because we’re seeing that one of the main challenges is that there’s a lack of personnel all around the world, right? And people not wanting to do this kind of operational, routine type of job.

So what was happening is that the retailers are quite desperate in trying to find a solution to make this less costly and also to make people be able to do a job that is more meaningful for them. So, for example, instead of filling up the shelves, they’ll be managing systems that will fill up the shelves for them. Or in instead of looking and completing Excel forms to say, “Okay, what do I need to buy for the next quarter?” it’ll be analyzing this through a computer and making a decision, a final decision, or reviewing that. So this kind of management of systems is how we see where the industry is going.

Christina Cardoza: And I love how you put that, because I feel like a lot of times when technology like this comes into play, a lot of people are worried that it’s going to replace their jobs or what the technology is going to do, but it sounds like it’s really taking away the mundane tasks and making their role more valuable and making their position within a retail store more valuable.

Silvia Kuo: Of course, of course. And I—and we see that overall. Like we see that now we want people to be more engaged and to have more meaningful—because I think it comes along with the introspection that we as human beings have, we are becoming more and more aware of our psychology and things like meaningfulness in life. And I think the technology allows us to explore that side even more.

So, like you say, it’s—a lot of people feel that it’s a threat, especially AI; it’s going to be a threat, replace all of all of our jobs. I think that it’s a shift. It is like everything in technology, and even the industrial revolution time a long time ago—people were scared, but actually what happened is that it improved production, it improved people’s lives.

So, for example, when I see logistics, there’s a lot that we can do still in logistics. Sometimes it’s still today the process is very manual. So, things like tracking the stock when it comes in: How much do I have? Instead of sending someone to count it, then I have a system that can just naturally do it quickly for me. And then I can also have an alerting system that lets me restock the shop very quickly without me having to send someone to see if the shop is empty. So that’s just for logistics.

If we go into, for example, the space optimization, now with computer vision we can look at the whole store, and we can have a heat map of which are the areas that people visit the most, because of the layout or maybe because of the type of brand that I put there. And with all this knowledge then I can—for example, during Q4, when it’s the high season for certain stores, I can put the best brands. Or I can adjust the rent—let’s say as a retailer—the rent or the fees or what I offer to my vendor according to this.

So there’s lots of things. Or, for example, in when we see queues, long queues, and that is really something that we want to avoid, right? We can look at this, and instead of having three checkout points, we can say, “Okay, now there’s more people, so let’s open three more.” So these kind of solutions are what we are trying to help out with.

Christina Cardoza: Absolutely. And I just think about some of the more advanced technology or solutions in my everyday life, it just becomes norm. I don’t even think about things that I’m using. So I can’t wait to see until that becomes more mainstream and more adoptive across retail stores. I don’t think workers will really even think of it; it is just going to become a new way of working.

But of course there’s a lot of complex technology and things that go into making that happen. Some of the other keywords out there: “AI,” “computer vision,” and “edge computing.” You touched on computer vision a little bit, but I’m curious, what are the roles of these advanced technologies? How are they playing in these solutions and making retail really smart retail?

Silvia Kuo: Yeah, I think these are all technologies—so, AI, computer vision, edge computing, they’re all technologies behind something, so it’s more of a horizontal. So when I say this is, for example, AI can help the engagement of customers, because nowadays we see that stores are not really a place just to purchase but more of a customer-experience, a brand-experience space.

In that aspect we have seen things like digital signage that is targeting the audience. When I see that, for example, there’s a group of people that is around this age, they’re male or female, I can instantly show them something that is targeted to them or even do interactive—kind of ask them questions. This is what AI is doing now, is analyzing the situation real time and giving feedback and interacting with customers.

And something more behind the scenes that AI is doing, it’s, for example, analyzing data. So when we—AI works based on data, so if we have more data—sometimes in the past we had data, but we didn’t know what to do with it. Now what AI is doing is analyzing this so that over time, over years and months, I can understand what the behavior of my audience is in this area as opposed to another district in the country, and adjust the, say, the stock according to this.

Computer vision is very interesting, because also it’s a horizontal technology where I can apply it, for example, for recycling. Now we see a lot of retail grocery stores that have the recycling machines, and they’re determining what kind of empty product we are putting into the machine and giving, for example, in some cases, in Europe, they give you money back, right? So you can spend it in the store.

Another thing is security. We’re doing security with this or doing checkout—a checkout when someone doesn’t have a barcode or it’s a product without barcode is not walking back to the produce section or having to weigh it. It can use that, use computer vision, and recognize what the product is.

And each computer I think is—it’s something that allows all this technology to have less latency, because if we had to move all of this analysis back to the cloud or back to a data center—first, it’ll take a long time and consumes a lot of power and also data. So, without having to go back to the central network, we are doing this compute in a distributed way, even when there’s no internet—for example, it’s down for a few hours. I can even do this, I continue to use it, and then when I’m back on the network I can also update new features, etcetera.

Christina Cardoza: It’s amazing when you think about the data aspect that you just mentioned. I feel like, a couple of years ago, all that data that was being collected, you’d have to wait every quarter or every at the end of the year to really analyze that data and to be able to make changes. And by that point a lot of opportunity came and went. And so with this technology—AI helping with the data, computer vision, edge computing—you’re able to now make these changes in real time when it actually matters. And that has just been improving the business even more. So that’s great to see.

But I’m curious, how can businesses successfully integrate some of these technologies we’ve been talking about into their infrastructure, when we’re talking about inventory and end-to-end self-checkouts, smart queues—what type of infrastructure or investments are necessary to start implementing some of this technology and these solutions?

Silvia Kuo: Right. Of course there is some investment involved, because it’s a technology that wasn’t there before. But as much as we can we always try to use what is there. So, for example, when you mentioned the cameras, computer vision uses cameras a lot. We always try to use the cameras that they already have doing security, but we adjust them to at the same time that they’re doing security, we’re using the same video stream to analyze it behind on the on edge computer, for example, and do some analysis afterwards. So we are trying to reuse whatever infrastructure is there already.

The compute—usually there is, if it’s a new technology such as AI or computer vision, it needs a lot of computers. So there might be a need of putting in a computer. But many times many stores have their own little data centers, if you will. And what we can do is collect the data on the edge, let’s say, in the stores, pull it back into these little data centers, so all of the big data analysis is happening there or is happening—whatever decision-making or statistics is happening afterwards—there.

So we reuse these kinds of things. We don’t really have to put a lot of hardware into it. But yes, a lot of the newer technology needs, for example, some sensors or some cameras that have certain angles that were not there, these—or some signage, for example, in order to communicate with the customer—these are investments that are, that have to be done, but as much as we can, we try to reuse.

Christina Cardoza: That’s great. I always love the camera example, because so many businesses, they want to make sure they’re future-proofing any investments that they do make. And when they were purchasing these security cameras decades ago, I don’t know if they imagined that they would be being used in these capacities. So it’s amazing to see just how much technology has involved and how much we can leverage some of that existing infrastructure to really make these changes across the store.

I wanted to shift over now. We’ve been talking a lot about retail stores in general and different solutions that can be applied, but do you have any customer examples or use cases you can share of businesses that have actually leveraged these solutions we’ve been talking about? What problems they were experiencing and how the company came in and helped them.

Silvia Kuo: Yes, yes. I think there’s one that is more of problem-solving, like you mentioned, and the other one is more of an enhancement: I have two examples. So the first one was a—this retailer, this grocery retailer—was looking for an automated way to alert them of empty shelves, especially in the fresh produce area, because it was very manual, and they also wanted to combine that with the pricing. So they wanted to adjust the pricing throughout the day depending on the performance of that product that day.

So what we did was we used computer vision to first identify what produce was on the shelf, and throughout the day it would take different images and analysis at a certain interval in order to determine the level of stock. So this would be, you’ll create an alert in their system, the central system, but also for all of the operators, so they will see, “Oh, okay, I have to refill the apples and the oranges now.” And without having to do this walking by.

And another thing that we did based on the same AI technology of recognition of the product, we also automated their pricing. So now, for example, the apples were not selling very well that day, and we at four o’clock we wanted to start clearing them. So we would automatically change the e-tags below the apples and change the information and the price. So, and we did this already in several of their stores, and they are planning to roll it out. And so that’s a good example.

The second example is more about enhancement and improving understanding of the customer. This was a technology that we developed together with a software vendor of ours in France. What they did is they put a sensor and a camera in order to understand how people interact with the products. A lot of brands were interested in this. They also featured in one of the biggest trade shows for retail in NRF New York.

What they were doing is they were looking at how long do customers stand in front of the brand, which product they pick up, whether they look at it, how long they interact with the product, whether they take it, did they buy it, or whether they put it back. So there was a lot of data that was accumulated there, and a lot of—they got a lot of interest from brands themselves, because brands want to understand, when we launch a new product, how do people like it? Or, it’s not such a big deal, right? So the brands themselves were trying to contact these people to use the technology, but also the furniture makers for retail stores were very interested in offering this to the brands.

Christina Cardoza: I love those examples, especially the one about the fresh produce and apples, because it showcases how this is not only helping businesses and their efficiency and their operations, but then also the customer experience is improving as well: They’re making sure that their produce is always fresh and that items are always there when they’re going to look for it. So that’s great to see.

You mentioned in the beginning how ASUS works as a brain and works with partners to get some of these solutions in stores and to make some of these things happen. So I’m curious about those types of partnerships, especially I should mention insight.tech and the “insight.tech Talk” are sponsored by Intel. But I’m curious what the value of your Intel partnership and technology is in making some of this happen, and additionally any other partners that you’re working with to make this happen.

Silvia Kuo: Right. I think that Intel has been a long-term partner to ASUS. Even right at the beginning, when we were just doing consumer computers, laptops, and because of this long-term relationship it has really been crucial when we—this department, the IoT department—was created. Because I think one of the advantages that ASUS has, and even throughout difficult periods where stock or supply chain was an issue for ASUS, it wasn’t so much an of an issue because of this relationship and partnership. In many cases we are one of the first people that try out the new technology from Intel, so we’re able to implement them in a lot of the new products that we launch.

But at the same time I think that Intel is a very good partner in terms of when they’re developing something like AI, they are doing OpenVINO^™ to implement these new features, they ask ASUS to be sort of the testers of this. And we also market it out there, not just because of the partnership. I think it’s because we see across the board there’s lots of choices of technology and we offer different kinds, but we are also able to—when we see something like Intel coming into this space and trying to optimize and democratize it, because it’s not just about selling more computers, it’s about how do you make this accessible to people?

We see Intel doing that a lot, and they’re also very supportive to partners. They will, for example, engage us with the end customers. Many of the examples I gave you just now were actually people from Intel that introduced us and said, “Look, they tried this out, why don’t you see if that works for you?” So there’s a lot of good rapport with Intel in this aspect, and I think that’s what makes a good relationship.

And I think regarding the other question, the other part of the question that you made regarding the partner program that we run, I always say that in IoT it’s very difficult to do something on your own, because how you have so many components, right? Even with the camera makers, we see camera makers, they are optimizing their software in order to make AI more possible, or trying to put the chip on their camera so that it’s easier for the edge computer to analyze more data.

So we’re seeing that there’s a very good collaboration, and everybody understands this—that without a partnership it’s hard to do everything alone. So we are, we created this partner core program as a way to—we do a lot of marketing and a lot of validation, but apart from this it really is a space where you can exchange projects and you introduce each other to different customers and projects.

Christina Cardoza: Yeah, that’s great. That’s an ongoing theme at insight.tech, this idea of better together and working with other partners, that no one company can do it all themselves. I think that would be very difficult, and the solutions that retailers or end users would get would be very expensive and take a lot longer time to update or to really work through or do more advancements. So it’s great to hear companies like Intel and ASUS working together with other partners to make this possible.

This sounds like even though we’ve been talking about smart retail for a couple of years now, we still have a long way to go, and we’re only at the beginning of it, and not all of these things are being implemented yet. So I’m curious, how do you anticipate this space to continue to evolve? Technology gets more advanced, you make more partnerships—where do you think smart retail is going?

Silvia Kuo: Right. It’s a broad question and, yeah, I wish I knew more, but I will try to guess. I think that one of the things is that we will see a lot more automation of the operations, like we mentioned first when we started. Second is that, as I said, we will see people having more meaningful roles, more interesting jobs. Let’s say that it will be managing systems, right? And also I think that a lot of the brands and a lot of the retail spaces will be become showrooms really; it won’t be just for, you know, to buy things. And I would even dare to say that to some of the spaces you would just place orders and it’ll ship to your home; you don’t even have to wait for the clerk to go and get this right size for you; it’ll be more of a showroom, a tryout room.

Other things we are seeing is a lot of interactive devices and kiosks, and AI will help with this. It will help with the problem of having enough staff to attend to all of the guests, so you will be able to interact with the screens, with devices, in an easier way. We are seeing a lot of voice AI, for example, that is very accurate, even has accents and slang. So, a lot of that coming up. Also I think the—what you see sometimes in Asia, there’s this obsession for making things faster and more seamless, right? And I think that will be something that will expand across the world. It will be making the experience more seamless and waiting for less time and having a nice experience instead of this, you know, waiting time.

Christina Cardoza: Yeah, that’s one thing that customers with technology, if implemented, don’t take well to is when the technology doesn’t work or when they have to wait for the technology. But we’ll have to come back in a couple of years and re-listen to this podcast to see if any of these predictions were right. But I can definitely, definitely guarantee more meaningful roles for workers is definitely going to be something that comes out of this, and more meaningful customer interactions and customer experiences because of this. So that’s great to hear.

We are running out of time, so before we go I just wanted to throw it back to you one last time, if there’s any key takeaways or final thoughts you want to leave our listeners with today.

Silvia Kuo: Yeah, I think I want to do a sort of a call to anyone that is a solution provider that thinks that they would benefit from partnering with a ASUS—feel free to reach out.

Christina Cardoza: Absolutely. And for those who want to learn more about what ASUS is doing in this space or how smart retail is going to continue to evolve, I encourage you to visit insight.tech, where we continue to cover ASUS and other partners in this space.

So thank you, Silvia, for joining. It’s been a great conversation. Thanks to our listeners for tuning in today. Until next time, this has been “insight.tech Talk.”

The preceding transcript is provided to ensure accessibility and is intended to accurately capture an informal conversation. The transcript may contain improper uses of trademarked terms and as such should not be used for any other purposes. For more information, please see the Intel^® trademark information.

This transcript was edited by Erin Noble, copy editor.