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"We’re using artificial intelligence technology to help our buildings optimize our energy use. Because our HVAC units are running less, we expect them to last longer and on top of this we’re already seeing energy and cost savings.”
—Mary de Guzman
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Episode 156 is a conversation with Mary de Guzman from Sleep Country and Omar Tabba from BrainBox AI.
Episode 156 features Mary de Guzman from Sleep Country and Omar Tabba from BrainBox AI and is our 6th episode in the Case Study series looking at real-life, large-scale deployments of smart building technologies. These are not marketing fluff stories, these are lessons from leaders that others can put into use in their smart buildings programs. Mary and Omar discuss the benefits of installing AI enabled thermostats in several hundred retail stores. Enjoy!
You can find Mary and Omar on LinkedIn.
Overview (1:30)
Introduction to Mary de Guzman (1:56)
Who’s on the vendor team (2:50)
About the project (3:12)
Introduction to Omar Tabba (5:34)
Results (6:23)
Achieving Net Zero (8:20)
How the AI thermostat works (12:23)
How many people run the buildings (18:00)
Operational Value (21:02)
Challenges (24:00)
Steps to follow (31:56)
Making the business case (35:50)
Music credits: There Is A Reality by Common Tiger—licensed under an Music Vine Limited Pro Standard License ID: S496305-15083.
Note: transcript was created using an imperfect machine learning tool and lightly edited by a human (so you can get the gist). Please forgive errors!
[00:00:00] Mary de Guzman: We're using artificial intelligence technology to help our buildings optimize our energy use. It's like we're, we're doing this without having to invest in new HVAC systems. Like, in fact, this solution just connects with our existing HVAC systems and autonomously sends real time optimized control commands based on weather data and utility data.
[00:00:24] To help minimize the energy that's required to maintain the best, like, most optimal temperatures in our buildings while reducing our emissions. And because our HVAC units are running less, um, we expect them to last longer. And on top of this, we're already seeing energy savings and cost savings.
[00:00:46] James Dice: Hey friends. If you like the Nexus podcast, the best way to continue the learning is to join our community. There are three ways to do that. First, you can join the Nexus ProMembership. It's our global community of smart building professionals. We have monthly events, paywall deep [00:01:00] dive content, and a private chat room, and it's just 35 a month.
[00:01:04] Second, you can upgrade from the Pro Membership to our courses offering. It's headlined by our flagship course, the Smart Building Strategist. And we're building a catalog of courses taught by world leading experts on each topic under the smart buildings umbrella. Third and finally, our marketplace is how we connect leading vendors with buyers looking for their solutions.
[00:01:23] The links are below in the show notes. And now let's go on to the podcast.
[00:01:30] Welcome to the Nexus podcast. This is the sixth episode in our new series, diving into case studies of real life and large scale deployments of smart building technologies. And today we have a story coming from SleepCountry's portfolio of several hundred retail stores that installed smart thermostats and overlaid BrainBox AI's advanced supervisory control software to optimize all of them in tandem.
[00:01:54] So I have Mary de Guzman, director of ESG at Sleep Country. Welcome, Mary. Can you [00:02:00] talk about a little bit about your background?
[00:02:02] Mary de Guzman: Sure. Thanks for hosting, James. Um, in my role here at Sleep Country, I lead and influence our ESG approach, including developing and implementing our comprehensive strategy for our company and delivering against our ESG goals.
[00:02:17] I play a major role in collaborating with the leaders of our organization and integrating our ESG strategy across the business. I'm essentially tasked to turn our strategy into action and lead the management and an implementation of our strategy, um, our environmental and philanthropy programs. And my background is in, uh, environmental management, um, and I also have, um, certifications and, um, lead.
[00:02:44] And also, um, uh, risk management.
[00:02:48] James Dice: Who's all on your vendor team, if you think about, um, putting together this team of experts, who, who's that team?
[00:02:55] Mary de Guzman: So we partnered with BrainBox AI out of Montreal, Quebec, and it's a team [00:03:00] of engineers, um, HVAC specialists, uh, project management, um, just my, my sustainability gurus when it comes to emissions reductions.
[00:03:11] James Dice: And how many buildings did you install this technology in?
[00:03:14] Mary de Guzman: We installed it in 214 of our retail locations across Canada.
[00:03:19] James Dice: And how, how many, how many square feet or square meters, depending on which one you want to use?
[00:03:25] Mary de Guzman: We are in 1. 1 million square feet of retail space.
[00:03:29] James Dice: All right. And when did you start this project?
[00:03:31] Mary de Guzman: So the project initially kicked off back in November of 2021, um, to plan a four store pilot. And the pilot started in January of 2022 and ended a month later in February 2022. Uh, then we planned for a greater rollout, um, that, um, you know, had installations start at in April of that same year, 2022. And it completed.
[00:03:58] November of last year. [00:04:00]
[00:04:00] James Dice: And what are the results you've seen since then?
[00:04:03] Mary de Guzman: So I'll start with our four store pilot. So the pilot, again, consisting of four stores located in the province of Quebec in Canada. Um, the square footage of our, um, you know, was about 20, 000 square feet, um, about 5, 000 square foot like per store.
[00:04:22] And, um, what we saw was a 15 percent reduction in the population. In HVAC equipment, electricity use. And about a 19 percent reduction in the HVAC equipment gas consumption. Um, they annualized the reduction in the HVAC equipment electricity cost to be about 4, 850 Canadian. Um, and an annualized reduction in carbon emissions by about 15 tons of CO2 equivalent.
[00:04:53] Then, for the first 49 stores that we, we had 49 stores installed and able to [00:05:00] get data from June until December, so we saw out of the first 49 stores, a reduction, an average reduction in HVAC equipment use, electricity use of about 24%, and an average reduction in HVAC Equipment gas use of 22 percent and overall about 11 percent reduction in our energy bills.
[00:05:25] Um, carbon emissions actually, so it was calculated to have been reduced by 23 percent from our HVAC carbon equivalent emissions.
[00:05:34] James Dice: Cool. We also have Omar Tabba here, chief product officer at BrainBox AI. Omar, thank you for coming and welcome. Can you talk about your background a little bit?
[00:05:43] Omar Tabba: Sure. Thanks for having us, James.
[00:05:45] Um, so I'm the product lead at BrainBox AI. I've been here for just under four years. It'll be four years in November. My background is in, uh, smart smart buildings. So HVAC control, lighting controls, system [00:06:00] integration, energy management. Um, I've worked at, uh, GE at Distech Controls amongst other places and happy to be with you here today.
[00:06:08] James Dice: Mary mentioned the results that they're seeing. Can you talk about your other customers and sort of, is that a typical set of results for, um, this type of technology being deployed at a pilot and then rolling out to
[00:06:23] Omar Tabba: Yeah, so we're seeing very similar results across different types of portfolio sizes and different sizes of buildings, which is interesting.
[00:06:32] One consistent note that we see is that the gas savings are consistently higher than the electricity savings, kind of like what Mary mentioned, which is an interesting thing to observe. Generally speaking, Um, The technology has been scaling well with square footage, so in a big, let's say, 1 million square foot building versus a smaller, you know, 5, 000 or 6, 000 square foot store like Sleep Country, we're, we're seeing kind of a linear relationship between the [00:07:00] performance of the technology and the energy saved.
[00:07:03] Um, it's, it's also interesting how the, the different. Kind of stores located in different places also, uh, react differently as, as, you know, based on not just the climate, but also from a decarbonization perspective, um, depending on the, the source fuel of the electricity or the energy being used. So in Quebec, for example, like Mary mentioned, we did, uh, the first few stores with Sleep Country, and there's hardly any, uh, carbon emissions associated with the electricity because it's mostly hydro Quebec.
[00:07:37] So it's kind of. Almost zero, uh, GHG, um, emissions factor, whereas in Alberta, uh, there are some stores there where it's mainly coal that is used to generate electricity, so much higher, um, emissions. So I think we're kind of seeing, uh, retailers kind of become, uh, and owners kind of become aware of this. And start to hone in on those locations that have [00:08:00] a much higher carbon intensity per square foot, not only energy intensity.
[00:08:06] James Dice: Awesome. That's a great segue. Mary, I wanted to have you ask about something you said at the beginning, which was Um, your net zero by 2040 target. Can you talk about sort of, um, that road map? What do you have to do to get to net zero by 2040? And then the role technology plays in that road map?
[00:08:24] Mary de Guzman: Yes. So our overall goal is to be net zero by 2040 and more broadly to play our part in battling our climates, our planet's climate crisis.
[00:08:34] Um, so having, you know, our collaboration with BrainBox AI to install the AI enabled thermostats is just one of our ways where. working to achieve this goal. We're also exploring electrification of our vehicles in our fleet, and we're also installing electric vehicle charging stations for our associates to use to also help, you know, um, the acceleration of the adoption of, [00:09:00] of EVs, um, in general, and looking also at other technologies within our warehouse and retail locations like LED lighting and sensors, um, so that.
[00:09:10] You know, equipment is, is only on when it, when there are people in the building, technology is playing a big part of, of our strategy.
[00:09:18] James Dice: Cool. And if we think about this HVAC controls project, can you talk about sort of the beginning of this? I know it sounds like you came in after the initial pilot, um, was already done, but maybe go back to the beginning.
[00:09:30] Why did sleep country sort of start down this path of. HVAC controls and then the carbon savings that could be had there.
[00:09:37] Mary de Guzman: So we have rooftop unit based heating, ventilation and air conditioning systems and our HVAC controls were not connected to a sophisticated building automation system. They're manually controlled and the problem was they were running essentially 24 7.
[00:09:53] Um, And, you know, buildings, in buildings, the HVAC units, I was educated by Brainbox, [00:10:00] Ionis, that are, uh, the HVAC systems account for about 40 percent of a building's electricity consumption or energy consumption, and of that, about 30 percent of it is just wasted. So again, this identified an opportunity for Sleep Country to, um, to tackle, um, and, and optimize our energy consumption, looking at putting in AI enabled thermostats on our HVAC systems.
[00:10:26] Um, so, you know, like you said, the phases that we went through, we started, um, the pilot just to make sure that, you know, proof of concept that we were actually going to see results, um, from our locations. Um, so we launched the pilot. Analyze the results. Expanded the project to our portfolio of retail locations across Canada.
[00:10:49] And again, just Initial results from the 49 stores I mentioned, we're already seeing positive results. And so now we're just waiting to collect a whole [00:11:00] year's worth of data from all sites and get an actual, you know, cost savings. energy reductions, you know, um, just, just like an accurate picture of it, just exactly how much this project has impacted, um, you know, the reduction of our greenhouse gas emissions, uh, for, for sleep country.
[00:11:21] Um, and sort of our next phase, we're looking at, um, rolling into the same technology into our distribution centers. So we have 20 distribution centers across Canada. Um, so that would be sort of the next. Um, also to catch up on the, um, now that, you know, the heat pump technology, um, can work with the AI enabled thermostats, that will also be something that will add on to the roadmap, um, going forward.
[00:11:52] James Dice: Omar, can you talk about the, sort of the... Technical side of this, like, it sounds like there are, um, thermostats, [00:12:00] they came and they're internet connected and then you're, as your software sits on top of that, can you talk about how that works and how it sort of improves upon just, you know, obviously there are different options from.
[00:12:12] You know, going from a building that runs their HVAC 24 7, you could add a schedule, you could add a programmable thermostat, you could add an internet connected thermostat, and then I'd say the cream of the crop, it sounds like, is to add your software onto that. So can you talk about the benefits that you guys provide versus those other Sort of more basic options.
[00:12:32] Omar Tabba: Sure thing. Um, so let's start with what we have in the store. So in the store, um, prior to the deployment, um, you have to imagine there are typically two to three rooftop units per store. Each store had a standalone thermostat, um, on the wall, non communicating thermostat. And the project was to remove the old one, Put the new one on the wall, the new thermostat connects, um, via the store Wi Fi and [00:13:00] dials out to the Brainbox cloud automatically.
[00:13:03] They're pre programmed kind of to auto connect. And, um, this checked one of the boxes of the IT team and the cybersecurity team at, um, at Sleep Country 2, as you can imagine. And the, uh, the goal then is to be able to dynamically read and write and release. To this thermostat from the cloud. So what does that actually mean?
[00:13:25] Right? So the new thermostat is connected to the same wires that are coming out of the wall, right? So you have Y1, Y2, W1, W2, and so on. So it's controlling the same rooftop unit, the same equipment like Mary indicated earlier, except now. Each of these points, whether it's the, the fan command, the cooling stages, the heating stages, the onboard temperature and humidity sensors are all visible from the cloud and they're also writable, uh, as needed.
[00:13:54] So what our system does is it not only brings this data up into the cloud, but then it. It [00:14:00] augments it with weather data coming from the closest weather station to the building, with utility tariff structure of each individual store, with the emissions factor for the energy being consumed by the store, and with the occupancy pattern of the store.
[00:14:17] And so the, the algorithms So for example, a good example of where AI really shines is the modeling of the thermal behavior of the store and being able to predict space temperature in every zone of the store two hours in advance with over 95 percent accuracy. So then you say, okay, cool, Omar, you can predict temperature.
[00:14:44] How does that save me energy? Right? So, we use the prediction, which is, let's say zone 2 in the store is going to be 76, or in Celsius, let's say 23 or 24 degrees Celsius, but my set point is [00:15:00] 21 or 70 degrees. I don't want it to get warmer and then cool back down. Uh, given today's weather, I just want to stay here.
[00:15:08] So what do I, what is the right time to start the unit in the morning so that I minimize energy consumption and at the same time I satisfy the comfort need of the space? So the, the algorithm will actually dynamically write to each rooftop unit in each zone in every store every day to figure out the exact right start time and the exact right stop time.
[00:15:33] To coast into the close. So that's an example of a kind of a real world AI use case.
[00:15:39] James Dice: And I would imagine that, you know, there are a bunch of different types of rooftop units in the portfolio, right? There's probably, um, very simple, like no ventilation style rooftop units. And then there's everything, like you mentioned, heat pumps, Mary, where there's probably Um,
[00:15:59] Omar, can you talk about [00:16:00] how the software sort of responds to the different complexity or lack of complexity in a, in a given unit across these hundreds of stores?
[00:16:08] Omar Tabba: We've established now that there's a connection, a live kind of link between the cloud platform, the Brainbox cloud and the individual rooftop unit.
[00:16:17] And to your point, James, each unit has different Capabilities. And so what our algorithms are good at is figuring out automatically. Well, for example, this rooftop unit has an economizer. This one doesn't. So when do you engage the economizer and benefit from free cooling? And when can't you? Right? So this Automatically happens in the background and the algorithms will then engage the right point and be able to write again back from the cloud down to the individual rooftop unit to engage that piece of hardware, uh, and kind of execute the energy conservation measure that is needed at that point in time.
[00:16:56] And this is done Um, without a human in the [00:17:00] loop, um, kind of one of our differentiating features as a technology is this ability to autonomously write back from the cloud to individual units and their subcomponents.
[00:17:11] James Dice: Okay, so something that's really striking me here is we've been talking about decarbonization a lot, and the value of controls and decarbonization is unquestionable.
[00:17:19] But what you're also, like, it's really striking me here too on the operations side of things. So, What we described here is 200 plus stores where there was very little data, very little digital systems, and very little way for the, these, these buildings to be managed remotely, right? Um, I think a lot of people take the building automation system for granted nowadays, where you can log in remotely, and mostly those are in the top.
[00:17:46] 10 percent or sometimes the top 2 percent of buildings. What we're talking about here is smaller buildings that don't have the ability to log in remotely, change anything. And so, Mary, can you talk about the, the people that are running these buildings? [00:18:00] I'm imagining it's not 214 of them or 200, one for each store.
[00:18:04] It's probably a few people and they're running around like crazy trying to keep these stores on, um, up and running. So can you talk about the value of this project in, in their eyes? Exactly.
[00:18:16] Mary de Guzman: So, so James, there are actually only two, um, amazing facilities management team members that we have running the country.
[00:18:24] So they're responsible for the facilities maintenance at our, now we have close to 300 stores in our portfolio and 20 distribution centers across the country. There was no data before, um, for the HVAC units. So we went from, from like zero to like, you know, 100 really quickly. And, um, so it's, it's been, um, great for them to now have, you know, dashboards that they can access remotely.
[00:18:55] Um, you know, when Um, So if there's ever issues at a location, [00:19:00] they're able to, to, to see the performance of that particular building all from the comfort of, of, um, wherever they are in, in the country. So absolutely, um, we, we don't have a building automation systems. And so in fact that having the AI enabled thermostats installed.
[00:19:18] And working with our HVAC systems, you know, essentially we're able to control, um, the temperature for the occupants of those buildings, um, at any time, but because of the system, the building is, it's, it's a smart building and, and now, you know, able to, um, adjust. You know, autonomously to the occupants comfort based on, you know, whatever the weather is supposed to be like and, and, um, you know, optimize based on, um, utilities, tariffs, information, all this information that's coming in externally, um, into the algorithms and then, and then it does it's, it's magic and, and it just, you know, creates for the optimal conditions in our customer.
[00:19:59] [00:20:00] Buildings and not only for the occupants of our buildings and our customers comfort when they come in to try our mattresses, for example, but the mattress has to perform optimally at, um, you know, at certain temperatures. So it can't be too cool. For example, otherwise, that full mattress is going to feel.
[00:20:17] Firmer than it normally would. So it's really, really important in, in our retail environment that the temperature is, is optimal at all times.
[00:20:27] James Dice: I once had a mattress like that, the got too cold. It was like a rock. Yeah, I know how that is. Omar, can you talk about, um, if you're looking across your customers and different asset types, this small building space where there isn't a whole lot of controls seems to be a real sweet spot for your technology for this exact reason.
[00:20:47] The ability to come in and do a, I don't want to say do a better job than an FM That would be, you know, changing the set points themselves, but do a job that they can't do across 200 [00:21:00] stores for two people, right? Um, so can you talk about the sort of the operational value? And it really seems like this sort of setup that you guys have here is a scalable one for this smaller building space that You know, people in our audience know we've been studying this for many years, where this is a huge opportunity from a climate change perspective for our society to actually start to control some of these units in these smaller buildings that don't have control systems.
[00:21:24] Omar Tabba: There's a very large number of buildings, to your point, James, um, We're talking about Canada and the US, for example, where, um, their rooftop unit or some sort of packaged unit type of, uh, conditioning unit, um, HVAC unit, and with standalone controls, typically not connected to anything, right? So it's a very large number of buildings, um, and a very significant opportunity from a decarbonization perspective.
[00:21:48] The solution that we have. Is very scalable. To your point, and maybe I can touch on an example, like a little mental exercise. So it, we all have a thermostat at home. We [00:22:00] all know the temperature that we like to set it at. Maybe some of us, when we're leaving for a few days, we'll kind of shift it to a different temperature set point to reduce energy consumption and so on.
[00:22:10] So this is, this is not that hard, let's be honest, right? So you can do it, you can think about it, you know, it's easy to do, but if you had three homes, or five homes, or a hundred homes, it starts to get kind of a bit much, right? And so something that is not so difficult in, in an individual unit becomes, Cumbersome or even cost prohibitive because we can't hire that many people.
[00:22:36] We don't have that many hours in a day to be able to do this manually. So it, it calls out for a scalable cloud based autonomous solution that's able to make the right decision, uh, at a, at a geographic scale, like Canada, where you have, you know, literally thousands of miles between the stores. Um, and being done autonomously 24 7 is something that is [00:23:00] necessary for this solution to scale.
[00:23:02] So what we're seeing with Sleep Country, what we're seeing with other retailers is, to your point, yeah, it is a really good fit and it does address these kind of low hanging fruits from an energy efficiency and decarbonization perspective. The unit is the unit. It's still there. It's still conditioning the space.
[00:23:18] It's just doing it with the benefit of the guidance of the AI's prediction and being enriched with. Weather data and tariff data and emissions factors data that we bring in in the cloud.
[00:23:29] James Dice: Okay, let's talk about lessons learned. So I'm curious that while this is like a really good fit, it seems like I think with any sort of rollout across hundreds of buildings like we're talking about, there's always some sort of challenge and it seems like you guys have probably overcome it, but I'd love to hear for future buyers of this sort of solution.
[00:23:49] What would you tell, Mary, what would you tell future buyers about The challenge is that, you know, they're going to run into and they just need to know about it up front to make the process smoother.
[00:23:59] Mary de Guzman: [00:24:00] One would be, um, regarding internet issues. So, um, some of our thermostats were not able to connect to the wifi at select locations.
[00:24:10] And that was. A challenge with Sleep Country. I mean, we just needed to work with the Wi Fi vendors to provide an updated Wi Fi solution to resolve the issue. So it was just, you know, us having to upgrade our Wi Fi systems at Sloan Stewart. Um, I think the second challenge would be, um, with, with people and, and behavioral, um, and, you know, education and, and making sure that, You know, when, when we rolled out this program, you know, we, we sold it as their, you know, artificial intelligent, you know, thermostats being attached to our HVAC system.
[00:24:45] So it's going to make the building smarter and it's, it's gonna, you know, autonomously, you know, automatically adjust the building temperatures to, you know, the optimal temperatures. And there were the odd times when the occupants of the [00:25:00] building would come in two hours earlier. And like Omar mentioned, you know, the.
[00:25:04] The building is, is able to predict, you know, two hours in advance, what the best temperature at the zone would be, what's the best time to, to turn on heating at the right time, um, for example, and then, but if we had our associates coming in to say, do inventory two hours earlier on a Saturday, um, the building needs, the occupants need to know that for that First time that's going to happen when you come into that building, it may not be at the optimal temperature at that moment because the building, you know, the algorithms have to kick in and the building has to learn your behavior that every, however many weeks, two hours early on that Saturday, you're going to be in that building or somebody will be occupying that space so that they know, you know, the building knows, okay, in this zone.
[00:25:49] On, you know, every, you know, fourth Saturday of a month, it's, it's going to have to have somebody in two hours early. So they know to kick in and optimize for that [00:26:00] zone, um, at that particular time. So once our people knew that this is how this is. You know, technology works, you know, then, then it was a piece of cake and it avoided, you know, those calls to our, our, you know, facilities management team, you know, that, you know, why is it too cold?
[00:26:16] And, you know, I came in and this is supposed to be a smart, you know, building. And, and so, you know, once people learn that, no, the building has to learn, it has to adjust. And, you know, once, once you get over that hurdle, it's, it's, uh, no problem.
[00:26:31] James Dice: Imagine there's a, there's a, like a, a dual. Education that needs to happen, not just the occupants, but also the FM.
[00:26:39] And we haven't talked about like service firms, right? So there's probably service contractors that come in and, you know, maintain compressors and change filters and stuff out as well. Um, I would imagine educating those folks to let them know that this is happening is important as well.
[00:26:56] Mary de Guzman: And that was actually related to one of our challenges that we [00:27:00] had.
[00:27:00] Um, so the contractors that we engaged, so they are also the same, um, you know, people who maintain our equipment. So they were, you know, um, communicated to, to help, um, install these units, um, with BrainBox and, and knew about the project. So Making sure that whoever you're using to help maintain your equipment, that they're brought on board.
[00:27:23] Um, and one of the challenges we had was, um, with scheduling or the commitments from our contractors for the actual installation of these units. Um, there, you know, when we were going through this project. There were some major heat waves going on here, um, in some of our locations across the country. And, and so some of the installation dates we had needed to be either postponed or moved, um, as those contractors needed to prioritize, you know, urgent maintenance visits over our planned installation schedules.
[00:27:52] So, um, that was the only other sort of, um, hurdle that we encountered throughout this, this whole project. Um, [00:28:00] so, but you raise a great point. Making sure that our contractors are brought on board, um, in the early stages so that, you know, they're aware of what we're, you know, what our project's all about, um, and that we're putting in these AI enabled thermostats and, um, you know, actually at the end of the day, um, these contractors are coming in less because, um, our units are running less.
[00:28:23] So it's, it's actually extending the life of our HVAC units and, and, um, uh, we're seeing, you know, less maintenance, uh, is being required on some of these units.
[00:28:32] James Dice: Go ahead, Omar, if you had something to add there.
[00:28:34] Omar Tabba: Yeah, so I just wanted to tag on to what Mary was saying is, is that indeed the, um, kind of one of the, the challenges of the project like this is always to kind of manage cost, right?
[00:28:44] So there were some, some decisions that we made, uh, together with the Sleep Country team, just to ensure that, that we did that. So, uh, one of these decisions is what Mary mentioned. So we leveraged the existing service, HVAC service contractors [00:29:00] that Sleep Country uses across the country. And, uh, we asked them to add a little bit of time on the next planned maintenance visit, thereby avoiding the cost of a new truck roll.
[00:29:12] And this time was then used to replace the thermostats like we discussed earlier. Another, um, item that was important is that That we're leveraging the Wi Fi of the store. So this is something that is, uh, the guest Wi Fi that's available already in the store. So, this avoided us the cost of having to install a gateway and some sort of, you know, telecommunications solution.
[00:29:34] Um, all of these things add up as you know. So, we were able to contain the cost as a result of that and then... Um, maybe last but not least, back to your, to your earlier question about the service contractors, they, they got wise to the fact that now there's data. So now our team gets calls from technicians randomly that are going to stores saying, Hey, I'm about to show up to the store in Victoria.
[00:29:57] Can you tell me what's going on with the rooftop? So it's [00:30:00] a, it's an interesting kind of development now where they're also benefiting from the availability of the data, the solution.
[00:30:07] James Dice: And Omar, we've been talking a little bit about occupancy. Mary mentioned the, somebody coming in early on a Saturday. How does the technology understand what's normal in terms of who's occupying the building and what schedule they're coming in at?
[00:30:21] Omar Tabba: When we onboard a building, we work with the owner and the managers of the facility to understand what the occupancy needs and patterns are of the facility. So it would be something like, well, You know, employees come in to do inventory, like Mary said, uh, you know, two hours before store openings, store openings are always from 9 a.
[00:30:40] m. And so on closing is at this hour. So that, that part is ingested into the platform and it acts as a constraint for the AI to, to govern with. Um, the, um, the other interesting thing that we do is that we also take. Um, uh, people density and occupancy data from different [00:31:00] sources. Uh, and we use that to inform how the algorithms govern or, or optimize the HVAC system during the day and at night.
[00:31:08] So this is also kind of an overlay that we add on in the cloud.
[00:31:12] James Dice: Got it. Got it. It's from things like people counters or other ways in which maybe the retail organization might have a way in which customers are being counted, that kind of thing.
[00:31:21] Omar Tabba: Exactly. So depending on the source, then each portfolio tends to have a different kind of data source, but we have different sources to be able to do that.
[00:31:31] James Dice: Okay, so let's pretend there's a imaginary retail organization out there that's listening to this and they want us to provide them a playbook that they can copy this approach. So Mary, can you start us off? What are the steps that other buyers can sort of copy to follow this approach? And then Omar, if she misses anything, you can...
[00:31:51] Mary de Guzman: Um, the first step would be to, um, engage with a, uh, a great vendor and work with the team that can, you know, work with the schedule. [00:32:00] Again, our facilities maintenance team, we, we do have a very, um, small, but mighty team. So to make sure that, you know, we can coordinate, um, with our, Um, Contractors, you know, um, right from the beginning, um, have a, a kickoff project meeting with all the stakeholders involved.
[00:32:17] You know, what, what the goal of, of this project is. Um, I recommend starting off with a pilot just to see to make sure that, you know, your technology. Will work with, with, um, the AI enabled thermostat solution, because that is one thing that it, it, we didn't catch that in our pilot, um, you know, the, the fact that the AI enabled thermostats were not compatible with the heat pump technology that was at some of our locations.
[00:32:42] So, um, yeah, if we caught that in a pilot, then we could have adjusted. Then the second stage would just be, you know, after going through a pilot. To, you know, plan, um, a, a rollout and that's where, you know, really having that schedule working with your [00:33:00] contractor, mechanical contractor is really important.
[00:33:02] And, um, you know, the team at BrainBox, we had regular meetings, uh, regular weekly updates. So we knew, um, if there were any challenges, any hurdles, any barriers that, you know, we needed to try to work together to, uh, overcome. And, you know, we, we worked the schedule, we completed the project. Relatively on time and on budget.
[00:33:24] Um, and I think essentially, um, it's as simple as that. And really that's where I want to try to encourage other retailers out there of any size that you don't have the sustainability. Subject matter expert on your team, you know, don't wait for them to come and, and, you know, map out a, a great sustainability roadmap to get you to net zero.
[00:33:47] Um, you know, before you start, you know, any initiatives, um, you know, to try to curb your, your carbon emissions. You know, I, I just feel as a sustainability, you know, expert in this field, I just, I just think it's so important to [00:34:00] act now. And, um, you know, all of the small actions that we take if, if we all took them.
[00:34:06] You know, we can get that much closer to, to tackling climate change together. I know, Omar, if there's more that you want to add to, to the, the steps in our playbook.
[00:34:16] Omar Tabba: I think you said it really well, Mary. I would just say maybe to the listeners that, um, Kind of having been around this industry that, that really, there is a lot of technology kind of coming into the market and it's not just BrainBox.
[00:34:30] There are a lot of vendors out there that are really interesting doing very cool things on the lighting side and on the heat pump side, on the equipment side. So there's, there's a lot going on and there are now resources. Actually, James, your website is nothing to sneeze at. You have some great educational material there.
[00:34:47] That I think can be super helpful for people that want to get up to speed as to kind of who's who in the market. What are they doing? What are the new trends and what technologies are available? So feel free to reach out to the different, uh, I guess, market [00:35:00] participants, companies, contractors, and talk to them and see what solutions are available again, uh, for each subsystem in your building that there's a lot going on and I think it's really encouraging to see how everybody's really rolling up their sleeves.
[00:35:12] It's really.
[00:35:13] James Dice: Yes, I would second that. Thank you for the shout out on my own show. I appreciate it there, Omar. One of the things that I would put into the playbook there, if I'm collaborating with you all is, is making the business case. So can you sort of both talk about how a future buyer of this sort of solution might think about making the business case to the higher ups?
[00:35:35] Um, and I'll start us off. They're going to need to think about, um, what are the energy savings estimates? So maybe you've got an estimate in your pilot project and then you extrapolate that out throughout the rest of the portfolio Um, and then there are the the costs, right? So understanding Okay. Before we just had a bunch of dumb units throughout our portfolio and now we have software costs, um, a little bit of hardware costs with a thermostat.[00:36:00]
[00:36:00] Um, how should Mary, how should this business case get made? And how, how did you guys make it to the, to the C suite to make them understand the business value of this, this sort of project?
[00:36:10] Mary de Guzman: Yeah, that's a great question. I think like, you know, what you start with data. And, um, a lot of companies struggle with this, um, so, you know, what you would need to collect first of all is, is your utility data.
[00:36:24] So your, your electricity, uh, consumption data, your like utility bills, uh, gas bills, um, and start with that. And you can use that data to calculate your scope, what they call scope two emissions, if you're not familiar with, with the different greenhouse gas emissions, either scope two, your purchased electricity use, right?
[00:36:46] Um, so, um, that would be, A starting point is, is kind of as a baseline and, and then, um, kind of narrow your scope as to, okay, if we're going to focus, like we focused on our retail network, for [00:37:00] example. So, um, the 200 plus stores, um, and then, and then isolating that data to look at that, um, on its own. Um, I think to build the business case, you know, once we use the pilot.
[00:37:12] To, um, kind of test, you know, what the proposal was from, from bearing box AI and, and what the projected reductions we would expect from electricity use of our HVAC systems. And then also from our, the gas consumption, um, and then the actual Cost savings. Um, so, so from that pilot, um, we were able to bring forward the case to our, our, um, senior leaders to say, okay, you know, we are seeing, um, the technology is working, you know, and, and the, in the month that we put it in our forest or pilot, um, so, you know, it, it matches what was presented to us in our proposal.
[00:37:53] And then based on that, if we extrapolate that against our, I'm happy. 200 plus stores, there could be really significant cost [00:38:00] savings here, um, and a lot of other environmental benefits as well. So building that case to your, your C suite, your board, I mean, that's obviously, you know, the, uh, a great starting point, um, in, in making sure that projects like this can get off the ground.
[00:38:17] Omar Tabba: Just from our side as a vendor, we, we kind of are aware that there are different stakeholders. Within the, let's say, the owner's environment or the retailer's environment. So, we try to address them with their needs and speak to them in their language. So, for example, we have financial metrics. What is the payback?
[00:38:35] What is the ROI? We have sustainability metrics. So, what kind of greenhouse gas emissions can be avoided or reduced? We have, um, The facility maintenance stakeholders, right? What kind of visibility, what kind of scheduling and alarming capabilities are available? So all of these stakeholders, I think it's important to understand, A, that they exist, and then B, that they need to be addressed within the sales [00:39:00] process and communicated with early on so that they understand, uh, kind of, uh, what their...
[00:39:06] outcomes are going to be when the project is concluded.
[00:39:08] James Dice: Totally. Okay. So any, are there any other things that we didn't cover that would be good for someone that is following along in your footsteps here, Mary, uh, as we kind of close out this conversation?
[00:39:19] Mary de Guzman: Right. I just think I can't emphasize enough the importance of educating, uh, the building occupants, um, you know, getting in touch with the, the landlords of the buildings that.
[00:39:29] You may be occupying space out of, um, and then also the, um, you know, other stakeholders like, like, um, Omar mentioned our facilities management team, our real estate team, um, connecting with finance and, um, uh, you know, and, and getting a hand, a handle of all the bills and, and finding out, you know, knowing exactly, you know, what this project is going to entail and, and what it means when we're, we're making our, Um, building smarter with, [00:40:00] with artificial intelligence and, and I think, um, you know, just having that, uh, open, uh, communication and dialogue early on in the process is, is really important.
[00:40:10] James Dice: Good old fashioned change management, I guess. Exactly. There you go. All right. Well, thank you to you both for, for coming on the show. It sounds like an awesome project and I agree with you, Mary. It seems like. More retail building owners should be thinking about copying this approach.
[00:40:29] Rosy Khalife: Okay, friends, thank you for listening to this episode. As we continue to grow our global community of changemakers, we need your help. For the next couple of months, we're challenging our listeners to share a link to their favorite Nexus episode on LinkedIn with a short post about why you'd listen. It would really, really help us out.
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"We’re using artificial intelligence technology to help our buildings optimize our energy use. Because our HVAC units are running less, we expect them to last longer and on top of this we’re already seeing energy and cost savings.”
—Mary de Guzman
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Episode 156 is a conversation with Mary de Guzman from Sleep Country and Omar Tabba from BrainBox AI.
Episode 156 features Mary de Guzman from Sleep Country and Omar Tabba from BrainBox AI and is our 6th episode in the Case Study series looking at real-life, large-scale deployments of smart building technologies. These are not marketing fluff stories, these are lessons from leaders that others can put into use in their smart buildings programs. Mary and Omar discuss the benefits of installing AI enabled thermostats in several hundred retail stores. Enjoy!
You can find Mary and Omar on LinkedIn.
Overview (1:30)
Introduction to Mary de Guzman (1:56)
Who’s on the vendor team (2:50)
About the project (3:12)
Introduction to Omar Tabba (5:34)
Results (6:23)
Achieving Net Zero (8:20)
How the AI thermostat works (12:23)
How many people run the buildings (18:00)
Operational Value (21:02)
Challenges (24:00)
Steps to follow (31:56)
Making the business case (35:50)
Music credits: There Is A Reality by Common Tiger—licensed under an Music Vine Limited Pro Standard License ID: S496305-15083.
Note: transcript was created using an imperfect machine learning tool and lightly edited by a human (so you can get the gist). Please forgive errors!
[00:00:00] Mary de Guzman: We're using artificial intelligence technology to help our buildings optimize our energy use. It's like we're, we're doing this without having to invest in new HVAC systems. Like, in fact, this solution just connects with our existing HVAC systems and autonomously sends real time optimized control commands based on weather data and utility data.
[00:00:24] To help minimize the energy that's required to maintain the best, like, most optimal temperatures in our buildings while reducing our emissions. And because our HVAC units are running less, um, we expect them to last longer. And on top of this, we're already seeing energy savings and cost savings.
[00:00:46] James Dice: Hey friends. If you like the Nexus podcast, the best way to continue the learning is to join our community. There are three ways to do that. First, you can join the Nexus ProMembership. It's our global community of smart building professionals. We have monthly events, paywall deep [00:01:00] dive content, and a private chat room, and it's just 35 a month.
[00:01:04] Second, you can upgrade from the Pro Membership to our courses offering. It's headlined by our flagship course, the Smart Building Strategist. And we're building a catalog of courses taught by world leading experts on each topic under the smart buildings umbrella. Third and finally, our marketplace is how we connect leading vendors with buyers looking for their solutions.
[00:01:23] The links are below in the show notes. And now let's go on to the podcast.
[00:01:30] Welcome to the Nexus podcast. This is the sixth episode in our new series, diving into case studies of real life and large scale deployments of smart building technologies. And today we have a story coming from SleepCountry's portfolio of several hundred retail stores that installed smart thermostats and overlaid BrainBox AI's advanced supervisory control software to optimize all of them in tandem.
[00:01:54] So I have Mary de Guzman, director of ESG at Sleep Country. Welcome, Mary. Can you [00:02:00] talk about a little bit about your background?
[00:02:02] Mary de Guzman: Sure. Thanks for hosting, James. Um, in my role here at Sleep Country, I lead and influence our ESG approach, including developing and implementing our comprehensive strategy for our company and delivering against our ESG goals.
[00:02:17] I play a major role in collaborating with the leaders of our organization and integrating our ESG strategy across the business. I'm essentially tasked to turn our strategy into action and lead the management and an implementation of our strategy, um, our environmental and philanthropy programs. And my background is in, uh, environmental management, um, and I also have, um, certifications and, um, lead.
[00:02:44] And also, um, uh, risk management.
[00:02:48] James Dice: Who's all on your vendor team, if you think about, um, putting together this team of experts, who, who's that team?
[00:02:55] Mary de Guzman: So we partnered with BrainBox AI out of Montreal, Quebec, and it's a team [00:03:00] of engineers, um, HVAC specialists, uh, project management, um, just my, my sustainability gurus when it comes to emissions reductions.
[00:03:11] James Dice: And how many buildings did you install this technology in?
[00:03:14] Mary de Guzman: We installed it in 214 of our retail locations across Canada.
[00:03:19] James Dice: And how, how many, how many square feet or square meters, depending on which one you want to use?
[00:03:25] Mary de Guzman: We are in 1. 1 million square feet of retail space.
[00:03:29] James Dice: All right. And when did you start this project?
[00:03:31] Mary de Guzman: So the project initially kicked off back in November of 2021, um, to plan a four store pilot. And the pilot started in January of 2022 and ended a month later in February 2022. Uh, then we planned for a greater rollout, um, that, um, you know, had installations start at in April of that same year, 2022. And it completed.
[00:03:58] November of last year. [00:04:00]
[00:04:00] James Dice: And what are the results you've seen since then?
[00:04:03] Mary de Guzman: So I'll start with our four store pilot. So the pilot, again, consisting of four stores located in the province of Quebec in Canada. Um, the square footage of our, um, you know, was about 20, 000 square feet, um, about 5, 000 square foot like per store.
[00:04:22] And, um, what we saw was a 15 percent reduction in the population. In HVAC equipment, electricity use. And about a 19 percent reduction in the HVAC equipment gas consumption. Um, they annualized the reduction in the HVAC equipment electricity cost to be about 4, 850 Canadian. Um, and an annualized reduction in carbon emissions by about 15 tons of CO2 equivalent.
[00:04:53] Then, for the first 49 stores that we, we had 49 stores installed and able to [00:05:00] get data from June until December, so we saw out of the first 49 stores, a reduction, an average reduction in HVAC equipment use, electricity use of about 24%, and an average reduction in HVAC Equipment gas use of 22 percent and overall about 11 percent reduction in our energy bills.
[00:05:25] Um, carbon emissions actually, so it was calculated to have been reduced by 23 percent from our HVAC carbon equivalent emissions.
[00:05:34] James Dice: Cool. We also have Omar Tabba here, chief product officer at BrainBox AI. Omar, thank you for coming and welcome. Can you talk about your background a little bit?
[00:05:43] Omar Tabba: Sure. Thanks for having us, James.
[00:05:45] Um, so I'm the product lead at BrainBox AI. I've been here for just under four years. It'll be four years in November. My background is in, uh, smart smart buildings. So HVAC control, lighting controls, system [00:06:00] integration, energy management. Um, I've worked at, uh, GE at Distech Controls amongst other places and happy to be with you here today.
[00:06:08] James Dice: Mary mentioned the results that they're seeing. Can you talk about your other customers and sort of, is that a typical set of results for, um, this type of technology being deployed at a pilot and then rolling out to
[00:06:23] Omar Tabba: Yeah, so we're seeing very similar results across different types of portfolio sizes and different sizes of buildings, which is interesting.
[00:06:32] One consistent note that we see is that the gas savings are consistently higher than the electricity savings, kind of like what Mary mentioned, which is an interesting thing to observe. Generally speaking, Um, The technology has been scaling well with square footage, so in a big, let's say, 1 million square foot building versus a smaller, you know, 5, 000 or 6, 000 square foot store like Sleep Country, we're, we're seeing kind of a linear relationship between the [00:07:00] performance of the technology and the energy saved.
[00:07:03] Um, it's, it's also interesting how the, the different. Kind of stores located in different places also, uh, react differently as, as, you know, based on not just the climate, but also from a decarbonization perspective, um, depending on the, the source fuel of the electricity or the energy being used. So in Quebec, for example, like Mary mentioned, we did, uh, the first few stores with Sleep Country, and there's hardly any, uh, carbon emissions associated with the electricity because it's mostly hydro Quebec.
[00:07:37] So it's kind of. Almost zero, uh, GHG, um, emissions factor, whereas in Alberta, uh, there are some stores there where it's mainly coal that is used to generate electricity, so much higher, um, emissions. So I think we're kind of seeing, uh, retailers kind of become, uh, and owners kind of become aware of this. And start to hone in on those locations that have [00:08:00] a much higher carbon intensity per square foot, not only energy intensity.
[00:08:06] James Dice: Awesome. That's a great segue. Mary, I wanted to have you ask about something you said at the beginning, which was Um, your net zero by 2040 target. Can you talk about sort of, um, that road map? What do you have to do to get to net zero by 2040? And then the role technology plays in that road map?
[00:08:24] Mary de Guzman: Yes. So our overall goal is to be net zero by 2040 and more broadly to play our part in battling our climates, our planet's climate crisis.
[00:08:34] Um, so having, you know, our collaboration with BrainBox AI to install the AI enabled thermostats is just one of our ways where. working to achieve this goal. We're also exploring electrification of our vehicles in our fleet, and we're also installing electric vehicle charging stations for our associates to use to also help, you know, um, the acceleration of the adoption of, [00:09:00] of EVs, um, in general, and looking also at other technologies within our warehouse and retail locations like LED lighting and sensors, um, so that.
[00:09:10] You know, equipment is, is only on when it, when there are people in the building, technology is playing a big part of, of our strategy.
[00:09:18] James Dice: Cool. And if we think about this HVAC controls project, can you talk about sort of the beginning of this? I know it sounds like you came in after the initial pilot, um, was already done, but maybe go back to the beginning.
[00:09:30] Why did sleep country sort of start down this path of. HVAC controls and then the carbon savings that could be had there.
[00:09:37] Mary de Guzman: So we have rooftop unit based heating, ventilation and air conditioning systems and our HVAC controls were not connected to a sophisticated building automation system. They're manually controlled and the problem was they were running essentially 24 7.
[00:09:53] Um, And, you know, buildings, in buildings, the HVAC units, I was educated by Brainbox, [00:10:00] Ionis, that are, uh, the HVAC systems account for about 40 percent of a building's electricity consumption or energy consumption, and of that, about 30 percent of it is just wasted. So again, this identified an opportunity for Sleep Country to, um, to tackle, um, and, and optimize our energy consumption, looking at putting in AI enabled thermostats on our HVAC systems.
[00:10:26] Um, so, you know, like you said, the phases that we went through, we started, um, the pilot just to make sure that, you know, proof of concept that we were actually going to see results, um, from our locations. Um, so we launched the pilot. Analyze the results. Expanded the project to our portfolio of retail locations across Canada.
[00:10:49] And again, just Initial results from the 49 stores I mentioned, we're already seeing positive results. And so now we're just waiting to collect a whole [00:11:00] year's worth of data from all sites and get an actual, you know, cost savings. energy reductions, you know, um, just, just like an accurate picture of it, just exactly how much this project has impacted, um, you know, the reduction of our greenhouse gas emissions, uh, for, for sleep country.
[00:11:21] Um, and sort of our next phase, we're looking at, um, rolling into the same technology into our distribution centers. So we have 20 distribution centers across Canada. Um, so that would be sort of the next. Um, also to catch up on the, um, now that, you know, the heat pump technology, um, can work with the AI enabled thermostats, that will also be something that will add on to the roadmap, um, going forward.
[00:11:52] James Dice: Omar, can you talk about the, sort of the... Technical side of this, like, it sounds like there are, um, thermostats, [00:12:00] they came and they're internet connected and then you're, as your software sits on top of that, can you talk about how that works and how it sort of improves upon just, you know, obviously there are different options from.
[00:12:12] You know, going from a building that runs their HVAC 24 7, you could add a schedule, you could add a programmable thermostat, you could add an internet connected thermostat, and then I'd say the cream of the crop, it sounds like, is to add your software onto that. So can you talk about the benefits that you guys provide versus those other Sort of more basic options.
[00:12:32] Omar Tabba: Sure thing. Um, so let's start with what we have in the store. So in the store, um, prior to the deployment, um, you have to imagine there are typically two to three rooftop units per store. Each store had a standalone thermostat, um, on the wall, non communicating thermostat. And the project was to remove the old one, Put the new one on the wall, the new thermostat connects, um, via the store Wi Fi and [00:13:00] dials out to the Brainbox cloud automatically.
[00:13:03] They're pre programmed kind of to auto connect. And, um, this checked one of the boxes of the IT team and the cybersecurity team at, um, at Sleep Country 2, as you can imagine. And the, uh, the goal then is to be able to dynamically read and write and release. To this thermostat from the cloud. So what does that actually mean?
[00:13:25] Right? So the new thermostat is connected to the same wires that are coming out of the wall, right? So you have Y1, Y2, W1, W2, and so on. So it's controlling the same rooftop unit, the same equipment like Mary indicated earlier, except now. Each of these points, whether it's the, the fan command, the cooling stages, the heating stages, the onboard temperature and humidity sensors are all visible from the cloud and they're also writable, uh, as needed.
[00:13:54] So what our system does is it not only brings this data up into the cloud, but then it. It [00:14:00] augments it with weather data coming from the closest weather station to the building, with utility tariff structure of each individual store, with the emissions factor for the energy being consumed by the store, and with the occupancy pattern of the store.
[00:14:17] And so the, the algorithms So for example, a good example of where AI really shines is the modeling of the thermal behavior of the store and being able to predict space temperature in every zone of the store two hours in advance with over 95 percent accuracy. So then you say, okay, cool, Omar, you can predict temperature.
[00:14:44] How does that save me energy? Right? So, we use the prediction, which is, let's say zone 2 in the store is going to be 76, or in Celsius, let's say 23 or 24 degrees Celsius, but my set point is [00:15:00] 21 or 70 degrees. I don't want it to get warmer and then cool back down. Uh, given today's weather, I just want to stay here.
[00:15:08] So what do I, what is the right time to start the unit in the morning so that I minimize energy consumption and at the same time I satisfy the comfort need of the space? So the, the algorithm will actually dynamically write to each rooftop unit in each zone in every store every day to figure out the exact right start time and the exact right stop time.
[00:15:33] To coast into the close. So that's an example of a kind of a real world AI use case.
[00:15:39] James Dice: And I would imagine that, you know, there are a bunch of different types of rooftop units in the portfolio, right? There's probably, um, very simple, like no ventilation style rooftop units. And then there's everything, like you mentioned, heat pumps, Mary, where there's probably Um,
[00:15:59] Omar, can you talk about [00:16:00] how the software sort of responds to the different complexity or lack of complexity in a, in a given unit across these hundreds of stores?
[00:16:08] Omar Tabba: We've established now that there's a connection, a live kind of link between the cloud platform, the Brainbox cloud and the individual rooftop unit.
[00:16:17] And to your point, James, each unit has different Capabilities. And so what our algorithms are good at is figuring out automatically. Well, for example, this rooftop unit has an economizer. This one doesn't. So when do you engage the economizer and benefit from free cooling? And when can't you? Right? So this Automatically happens in the background and the algorithms will then engage the right point and be able to write again back from the cloud down to the individual rooftop unit to engage that piece of hardware, uh, and kind of execute the energy conservation measure that is needed at that point in time.
[00:16:56] And this is done Um, without a human in the [00:17:00] loop, um, kind of one of our differentiating features as a technology is this ability to autonomously write back from the cloud to individual units and their subcomponents.
[00:17:11] James Dice: Okay, so something that's really striking me here is we've been talking about decarbonization a lot, and the value of controls and decarbonization is unquestionable.
[00:17:19] But what you're also, like, it's really striking me here too on the operations side of things. So, What we described here is 200 plus stores where there was very little data, very little digital systems, and very little way for the, these, these buildings to be managed remotely, right? Um, I think a lot of people take the building automation system for granted nowadays, where you can log in remotely, and mostly those are in the top.
[00:17:46] 10 percent or sometimes the top 2 percent of buildings. What we're talking about here is smaller buildings that don't have the ability to log in remotely, change anything. And so, Mary, can you talk about the, the people that are running these buildings? [00:18:00] I'm imagining it's not 214 of them or 200, one for each store.
[00:18:04] It's probably a few people and they're running around like crazy trying to keep these stores on, um, up and running. So can you talk about the value of this project in, in their eyes? Exactly.
[00:18:16] Mary de Guzman: So, so James, there are actually only two, um, amazing facilities management team members that we have running the country.
[00:18:24] So they're responsible for the facilities maintenance at our, now we have close to 300 stores in our portfolio and 20 distribution centers across the country. There was no data before, um, for the HVAC units. So we went from, from like zero to like, you know, 100 really quickly. And, um, so it's, it's been, um, great for them to now have, you know, dashboards that they can access remotely.
[00:18:55] Um, you know, when Um, So if there's ever issues at a location, [00:19:00] they're able to, to, to see the performance of that particular building all from the comfort of, of, um, wherever they are in, in the country. So absolutely, um, we, we don't have a building automation systems. And so in fact that having the AI enabled thermostats installed.
[00:19:18] And working with our HVAC systems, you know, essentially we're able to control, um, the temperature for the occupants of those buildings, um, at any time, but because of the system, the building is, it's, it's a smart building and, and now, you know, able to, um, adjust. You know, autonomously to the occupants comfort based on, you know, whatever the weather is supposed to be like and, and, um, you know, optimize based on, um, utilities, tariffs, information, all this information that's coming in externally, um, into the algorithms and then, and then it does it's, it's magic and, and it just, you know, creates for the optimal conditions in our customer.
[00:19:59] [00:20:00] Buildings and not only for the occupants of our buildings and our customers comfort when they come in to try our mattresses, for example, but the mattress has to perform optimally at, um, you know, at certain temperatures. So it can't be too cool. For example, otherwise, that full mattress is going to feel.
[00:20:17] Firmer than it normally would. So it's really, really important in, in our retail environment that the temperature is, is optimal at all times.
[00:20:27] James Dice: I once had a mattress like that, the got too cold. It was like a rock. Yeah, I know how that is. Omar, can you talk about, um, if you're looking across your customers and different asset types, this small building space where there isn't a whole lot of controls seems to be a real sweet spot for your technology for this exact reason.
[00:20:47] The ability to come in and do a, I don't want to say do a better job than an FM That would be, you know, changing the set points themselves, but do a job that they can't do across 200 [00:21:00] stores for two people, right? Um, so can you talk about the sort of the operational value? And it really seems like this sort of setup that you guys have here is a scalable one for this smaller building space that You know, people in our audience know we've been studying this for many years, where this is a huge opportunity from a climate change perspective for our society to actually start to control some of these units in these smaller buildings that don't have control systems.
[00:21:24] Omar Tabba: There's a very large number of buildings, to your point, James, um, We're talking about Canada and the US, for example, where, um, their rooftop unit or some sort of packaged unit type of, uh, conditioning unit, um, HVAC unit, and with standalone controls, typically not connected to anything, right? So it's a very large number of buildings, um, and a very significant opportunity from a decarbonization perspective.
[00:21:48] The solution that we have. Is very scalable. To your point, and maybe I can touch on an example, like a little mental exercise. So it, we all have a thermostat at home. We [00:22:00] all know the temperature that we like to set it at. Maybe some of us, when we're leaving for a few days, we'll kind of shift it to a different temperature set point to reduce energy consumption and so on.
[00:22:10] So this is, this is not that hard, let's be honest, right? So you can do it, you can think about it, you know, it's easy to do, but if you had three homes, or five homes, or a hundred homes, it starts to get kind of a bit much, right? And so something that is not so difficult in, in an individual unit becomes, Cumbersome or even cost prohibitive because we can't hire that many people.
[00:22:36] We don't have that many hours in a day to be able to do this manually. So it, it calls out for a scalable cloud based autonomous solution that's able to make the right decision, uh, at a, at a geographic scale, like Canada, where you have, you know, literally thousands of miles between the stores. Um, and being done autonomously 24 7 is something that is [00:23:00] necessary for this solution to scale.
[00:23:02] So what we're seeing with Sleep Country, what we're seeing with other retailers is, to your point, yeah, it is a really good fit and it does address these kind of low hanging fruits from an energy efficiency and decarbonization perspective. The unit is the unit. It's still there. It's still conditioning the space.
[00:23:18] It's just doing it with the benefit of the guidance of the AI's prediction and being enriched with. Weather data and tariff data and emissions factors data that we bring in in the cloud.
[00:23:29] James Dice: Okay, let's talk about lessons learned. So I'm curious that while this is like a really good fit, it seems like I think with any sort of rollout across hundreds of buildings like we're talking about, there's always some sort of challenge and it seems like you guys have probably overcome it, but I'd love to hear for future buyers of this sort of solution.
[00:23:49] What would you tell, Mary, what would you tell future buyers about The challenge is that, you know, they're going to run into and they just need to know about it up front to make the process smoother.
[00:23:59] Mary de Guzman: [00:24:00] One would be, um, regarding internet issues. So, um, some of our thermostats were not able to connect to the wifi at select locations.
[00:24:10] And that was. A challenge with Sleep Country. I mean, we just needed to work with the Wi Fi vendors to provide an updated Wi Fi solution to resolve the issue. So it was just, you know, us having to upgrade our Wi Fi systems at Sloan Stewart. Um, I think the second challenge would be, um, with, with people and, and behavioral, um, and, you know, education and, and making sure that, You know, when, when we rolled out this program, you know, we, we sold it as their, you know, artificial intelligent, you know, thermostats being attached to our HVAC system.
[00:24:45] So it's going to make the building smarter and it's, it's gonna, you know, autonomously, you know, automatically adjust the building temperatures to, you know, the optimal temperatures. And there were the odd times when the occupants of the [00:25:00] building would come in two hours earlier. And like Omar mentioned, you know, the.
[00:25:04] The building is, is able to predict, you know, two hours in advance, what the best temperature at the zone would be, what's the best time to, to turn on heating at the right time, um, for example, and then, but if we had our associates coming in to say, do inventory two hours earlier on a Saturday, um, the building needs, the occupants need to know that for that First time that's going to happen when you come into that building, it may not be at the optimal temperature at that moment because the building, you know, the algorithms have to kick in and the building has to learn your behavior that every, however many weeks, two hours early on that Saturday, you're going to be in that building or somebody will be occupying that space so that they know, you know, the building knows, okay, in this zone.
[00:25:49] On, you know, every, you know, fourth Saturday of a month, it's, it's going to have to have somebody in two hours early. So they know to kick in and optimize for that [00:26:00] zone, um, at that particular time. So once our people knew that this is how this is. You know, technology works, you know, then, then it was a piece of cake and it avoided, you know, those calls to our, our, you know, facilities management team, you know, that, you know, why is it too cold?
[00:26:16] And, you know, I came in and this is supposed to be a smart, you know, building. And, and so, you know, once people learn that, no, the building has to learn, it has to adjust. And, you know, once, once you get over that hurdle, it's, it's, uh, no problem.
[00:26:31] James Dice: Imagine there's a, there's a, like a, a dual. Education that needs to happen, not just the occupants, but also the FM.
[00:26:39] And we haven't talked about like service firms, right? So there's probably service contractors that come in and, you know, maintain compressors and change filters and stuff out as well. Um, I would imagine educating those folks to let them know that this is happening is important as well.
[00:26:56] Mary de Guzman: And that was actually related to one of our challenges that we [00:27:00] had.
[00:27:00] Um, so the contractors that we engaged, so they are also the same, um, you know, people who maintain our equipment. So they were, you know, um, communicated to, to help, um, install these units, um, with BrainBox and, and knew about the project. So Making sure that whoever you're using to help maintain your equipment, that they're brought on board.
[00:27:23] Um, and one of the challenges we had was, um, with scheduling or the commitments from our contractors for the actual installation of these units. Um, there, you know, when we were going through this project. There were some major heat waves going on here, um, in some of our locations across the country. And, and so some of the installation dates we had needed to be either postponed or moved, um, as those contractors needed to prioritize, you know, urgent maintenance visits over our planned installation schedules.
[00:27:52] So, um, that was the only other sort of, um, hurdle that we encountered throughout this, this whole project. Um, [00:28:00] so, but you raise a great point. Making sure that our contractors are brought on board, um, in the early stages so that, you know, they're aware of what we're, you know, what our project's all about, um, and that we're putting in these AI enabled thermostats and, um, you know, actually at the end of the day, um, these contractors are coming in less because, um, our units are running less.
[00:28:23] So it's, it's actually extending the life of our HVAC units and, and, um, uh, we're seeing, you know, less maintenance, uh, is being required on some of these units.
[00:28:32] James Dice: Go ahead, Omar, if you had something to add there.
[00:28:34] Omar Tabba: Yeah, so I just wanted to tag on to what Mary was saying is, is that indeed the, um, kind of one of the, the challenges of the project like this is always to kind of manage cost, right?
[00:28:44] So there were some, some decisions that we made, uh, together with the Sleep Country team, just to ensure that, that we did that. So, uh, one of these decisions is what Mary mentioned. So we leveraged the existing service, HVAC service contractors [00:29:00] that Sleep Country uses across the country. And, uh, we asked them to add a little bit of time on the next planned maintenance visit, thereby avoiding the cost of a new truck roll.
[00:29:12] And this time was then used to replace the thermostats like we discussed earlier. Another, um, item that was important is that That we're leveraging the Wi Fi of the store. So this is something that is, uh, the guest Wi Fi that's available already in the store. So, this avoided us the cost of having to install a gateway and some sort of, you know, telecommunications solution.
[00:29:34] Um, all of these things add up as you know. So, we were able to contain the cost as a result of that and then... Um, maybe last but not least, back to your, to your earlier question about the service contractors, they, they got wise to the fact that now there's data. So now our team gets calls from technicians randomly that are going to stores saying, Hey, I'm about to show up to the store in Victoria.
[00:29:57] Can you tell me what's going on with the rooftop? So it's [00:30:00] a, it's an interesting kind of development now where they're also benefiting from the availability of the data, the solution.
[00:30:07] James Dice: And Omar, we've been talking a little bit about occupancy. Mary mentioned the, somebody coming in early on a Saturday. How does the technology understand what's normal in terms of who's occupying the building and what schedule they're coming in at?
[00:30:21] Omar Tabba: When we onboard a building, we work with the owner and the managers of the facility to understand what the occupancy needs and patterns are of the facility. So it would be something like, well, You know, employees come in to do inventory, like Mary said, uh, you know, two hours before store openings, store openings are always from 9 a.
[00:30:40] m. And so on closing is at this hour. So that, that part is ingested into the platform and it acts as a constraint for the AI to, to govern with. Um, the, um, the other interesting thing that we do is that we also take. Um, uh, people density and occupancy data from different [00:31:00] sources. Uh, and we use that to inform how the algorithms govern or, or optimize the HVAC system during the day and at night.
[00:31:08] So this is also kind of an overlay that we add on in the cloud.
[00:31:12] James Dice: Got it. Got it. It's from things like people counters or other ways in which maybe the retail organization might have a way in which customers are being counted, that kind of thing.
[00:31:21] Omar Tabba: Exactly. So depending on the source, then each portfolio tends to have a different kind of data source, but we have different sources to be able to do that.
[00:31:31] James Dice: Okay, so let's pretend there's a imaginary retail organization out there that's listening to this and they want us to provide them a playbook that they can copy this approach. So Mary, can you start us off? What are the steps that other buyers can sort of copy to follow this approach? And then Omar, if she misses anything, you can...
[00:31:51] Mary de Guzman: Um, the first step would be to, um, engage with a, uh, a great vendor and work with the team that can, you know, work with the schedule. [00:32:00] Again, our facilities maintenance team, we, we do have a very, um, small, but mighty team. So to make sure that, you know, we can coordinate, um, with our, Um, Contractors, you know, um, right from the beginning, um, have a, a kickoff project meeting with all the stakeholders involved.
[00:32:17] You know, what, what the goal of, of this project is. Um, I recommend starting off with a pilot just to see to make sure that, you know, your technology. Will work with, with, um, the AI enabled thermostat solution, because that is one thing that it, it, we didn't catch that in our pilot, um, you know, the, the fact that the AI enabled thermostats were not compatible with the heat pump technology that was at some of our locations.
[00:32:42] So, um, yeah, if we caught that in a pilot, then we could have adjusted. Then the second stage would just be, you know, after going through a pilot. To, you know, plan, um, a, a rollout and that's where, you know, really having that schedule working with your [00:33:00] contractor, mechanical contractor is really important.
[00:33:02] And, um, you know, the team at BrainBox, we had regular meetings, uh, regular weekly updates. So we knew, um, if there were any challenges, any hurdles, any barriers that, you know, we needed to try to work together to, uh, overcome. And, you know, we, we worked the schedule, we completed the project. Relatively on time and on budget.
[00:33:24] Um, and I think essentially, um, it's as simple as that. And really that's where I want to try to encourage other retailers out there of any size that you don't have the sustainability. Subject matter expert on your team, you know, don't wait for them to come and, and, you know, map out a, a great sustainability roadmap to get you to net zero.
[00:33:47] Um, you know, before you start, you know, any initiatives, um, you know, to try to curb your, your carbon emissions. You know, I, I just feel as a sustainability, you know, expert in this field, I just, I just think it's so important to [00:34:00] act now. And, um, you know, all of the small actions that we take if, if we all took them.
[00:34:06] You know, we can get that much closer to, to tackling climate change together. I know, Omar, if there's more that you want to add to, to the, the steps in our playbook.
[00:34:16] Omar Tabba: I think you said it really well, Mary. I would just say maybe to the listeners that, um, Kind of having been around this industry that, that really, there is a lot of technology kind of coming into the market and it's not just BrainBox.
[00:34:30] There are a lot of vendors out there that are really interesting doing very cool things on the lighting side and on the heat pump side, on the equipment side. So there's, there's a lot going on and there are now resources. Actually, James, your website is nothing to sneeze at. You have some great educational material there.
[00:34:47] That I think can be super helpful for people that want to get up to speed as to kind of who's who in the market. What are they doing? What are the new trends and what technologies are available? So feel free to reach out to the different, uh, I guess, market [00:35:00] participants, companies, contractors, and talk to them and see what solutions are available again, uh, for each subsystem in your building that there's a lot going on and I think it's really encouraging to see how everybody's really rolling up their sleeves.
[00:35:12] It's really.
[00:35:13] James Dice: Yes, I would second that. Thank you for the shout out on my own show. I appreciate it there, Omar. One of the things that I would put into the playbook there, if I'm collaborating with you all is, is making the business case. So can you sort of both talk about how a future buyer of this sort of solution might think about making the business case to the higher ups?
[00:35:35] Um, and I'll start us off. They're going to need to think about, um, what are the energy savings estimates? So maybe you've got an estimate in your pilot project and then you extrapolate that out throughout the rest of the portfolio Um, and then there are the the costs, right? So understanding Okay. Before we just had a bunch of dumb units throughout our portfolio and now we have software costs, um, a little bit of hardware costs with a thermostat.[00:36:00]
[00:36:00] Um, how should Mary, how should this business case get made? And how, how did you guys make it to the, to the C suite to make them understand the business value of this, this sort of project?
[00:36:10] Mary de Guzman: Yeah, that's a great question. I think like, you know, what you start with data. And, um, a lot of companies struggle with this, um, so, you know, what you would need to collect first of all is, is your utility data.
[00:36:24] So your, your electricity, uh, consumption data, your like utility bills, uh, gas bills, um, and start with that. And you can use that data to calculate your scope, what they call scope two emissions, if you're not familiar with, with the different greenhouse gas emissions, either scope two, your purchased electricity use, right?
[00:36:46] Um, so, um, that would be, A starting point is, is kind of as a baseline and, and then, um, kind of narrow your scope as to, okay, if we're going to focus, like we focused on our retail network, for [00:37:00] example. So, um, the 200 plus stores, um, and then, and then isolating that data to look at that, um, on its own. Um, I think to build the business case, you know, once we use the pilot.
[00:37:12] To, um, kind of test, you know, what the proposal was from, from bearing box AI and, and what the projected reductions we would expect from electricity use of our HVAC systems. And then also from our, the gas consumption, um, and then the actual Cost savings. Um, so, so from that pilot, um, we were able to bring forward the case to our, our, um, senior leaders to say, okay, you know, we are seeing, um, the technology is working, you know, and, and the, in the month that we put it in our forest or pilot, um, so, you know, it, it matches what was presented to us in our proposal.
[00:37:53] And then based on that, if we extrapolate that against our, I'm happy. 200 plus stores, there could be really significant cost [00:38:00] savings here, um, and a lot of other environmental benefits as well. So building that case to your, your C suite, your board, I mean, that's obviously, you know, the, uh, a great starting point, um, in, in making sure that projects like this can get off the ground.
[00:38:17] Omar Tabba: Just from our side as a vendor, we, we kind of are aware that there are different stakeholders. Within the, let's say, the owner's environment or the retailer's environment. So, we try to address them with their needs and speak to them in their language. So, for example, we have financial metrics. What is the payback?
[00:38:35] What is the ROI? We have sustainability metrics. So, what kind of greenhouse gas emissions can be avoided or reduced? We have, um, The facility maintenance stakeholders, right? What kind of visibility, what kind of scheduling and alarming capabilities are available? So all of these stakeholders, I think it's important to understand, A, that they exist, and then B, that they need to be addressed within the sales [00:39:00] process and communicated with early on so that they understand, uh, kind of, uh, what their...
[00:39:06] outcomes are going to be when the project is concluded.
[00:39:08] James Dice: Totally. Okay. So any, are there any other things that we didn't cover that would be good for someone that is following along in your footsteps here, Mary, uh, as we kind of close out this conversation?
[00:39:19] Mary de Guzman: Right. I just think I can't emphasize enough the importance of educating, uh, the building occupants, um, you know, getting in touch with the, the landlords of the buildings that.
[00:39:29] You may be occupying space out of, um, and then also the, um, you know, other stakeholders like, like, um, Omar mentioned our facilities management team, our real estate team, um, connecting with finance and, um, uh, you know, and, and getting a hand, a handle of all the bills and, and finding out, you know, knowing exactly, you know, what this project is going to entail and, and what it means when we're, we're making our, Um, building smarter with, [00:40:00] with artificial intelligence and, and I think, um, you know, just having that, uh, open, uh, communication and dialogue early on in the process is, is really important.
[00:40:10] James Dice: Good old fashioned change management, I guess. Exactly. There you go. All right. Well, thank you to you both for, for coming on the show. It sounds like an awesome project and I agree with you, Mary. It seems like. More retail building owners should be thinking about copying this approach.
[00:40:29] Rosy Khalife: Okay, friends, thank you for listening to this episode. As we continue to grow our global community of changemakers, we need your help. For the next couple of months, we're challenging our listeners to share a link to their favorite Nexus episode on LinkedIn with a short post about why you'd listen. It would really, really help us out.
[00:40:46] Make sure to tag us in the post so we can see it. Have a good one.
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