“We've solved so many things where the engineering staff has been like, ‘I bet you this was going on for 10 years.’ It's been happening forever… whether that's a damper, whether the fans are running 24/7, whatever's broken. It's just really good at identifying things that would not otherwise be identified.”
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Episode 39 is a conversation with Adam Kaufman, energy engineer at Wendel Companies, and my favorite type of energy engineer at that - he’s what I call an analytics super-user.
Adam is supporting the rollout of analytics software across two campuses for a large university in the US. We talked about:
- the university's analytics deployment,
- how the software fits into the workflows of the staff there,
- the role of the analytics super-user,
- the results they've seen, and much, much more.
This is the vital role of the famed human in the loop: the stark truth that in order for this potentially game-changing technology to make a real impact, there must be humans like Adam that use and drive actions.
Mentions and Links
- Wendel Companies (0:56)
- Episode 37 with Shannon Smith (8:10)
- U.S. DOE Better Buildings Initiative (20:22)
- Clockworks Analytics (26:32)
- Episode 3 with Nick Gayeski on mass customization (30:42)
- Whitepaper from Nexus and Clockworks Analytics (31:48)
You can find Adam Kaufman on LinkedIn.
Thoughts, comments, reactions? Let us know in the comments.
- Why this university decided to pursue analytics (5:30)
- A history of software collecting dust on a virtual shelf (12:12)
- Universities’ place on the analytics adoption curve (22:31)
- Adams’s role as the super user Pt 1 (25:10)
- Drivers of success (36:35)
- Adams’s role as the super user Pt 2: the three workflow processes (39:18)
- The importance of the verification process (48:59)
- Baby stepping to other use cases (58:05)
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!
James Dice: [00:00:03] hello friends, welcome to the nexus podcast. I'm your host James dice each week. I fire questions that the leaders of the smart buildings industry to try to figure out where we're headed and how we can get there faster without all the marketing fluff. I'm pushing my learning to the limit. And I'm so glad to have you here following along.
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All right. Episode 39 is a conversation with Adam Kaufman energy engineer at window companies. And my favorite type of energy engineer at that, he's what I call an analytic super-user. And he's supporting the rollout of the analytics software across two campuses for a large university in the U S We talked about the university's analytics deployment, how the software fits into the workflows of the staff there, the role of the analytics, super-user the results they've seen and much, much more. This is the vital role of the famed human in the loop. The stark truth that an order for this potentially game-changing technology to make a real impact, there must be humans like Adam that use and drive actions without further ado, please enjoy nexus podcast, episode 39.
Adam Kaufman: [00:01:37] All right,
James Dice: [00:01:38] Adam. Thanks for coming on the show. Can
Adam Kaufman: [00:01:40] you introduce yourself for us? Yeah, I appreciate being here. so I'm Adam. I am an energy engineer. I worked for Wendell. We are an architecture and engineering firm. We also do energy services and construction management. Um, my background is, uh, physics undergraduate, uh, mechanical engineering master's degree.
Then I worked at Johnson controls in their building efficiency, energy solutions department while I was in school. And I started working at Wendell right after I graduated with my masters. So cool. And how many years you've been out now? So I've been out four years working in industry for four years.
Feel like, uh, it's gone quick.
James Dice: [00:02:21] For sure. Yeah, It does go quick. Cool. So, uh, this is a little bit of a change of pace for our normal guests. A couple of years out of school, you were just telling me you took the FEA exam today. so, I want to talk about this, this main project that you've been working on.
Adam Kaufman: [00:02:36] So can you kind of introduce this project and all kinds of then add, you know,
James Dice: [00:02:41] why I wanted to have you on and why I wanted to talk,
Adam Kaufman: [00:02:43] about this to you. Yeah, so. The project that I've been working on is a energy services role, where we are implementing fault detection and diagnostics analytics for a large university a little bit.
Before I get there, I worked implementing traditional energy conservation measures for the first two years, uh, when I started working for Wendell and that's, that's our core business model and in energy services, we implement energy projects. We've had a relationship with this university for six years now, implementing more standard measures where we do retrocommissioning heat, recovery, chillers, high efficiency, lighting, steam traps installation, and the university has been aware of the different techniques that you can use to improve efficiency.
You know, analytics being one of them. And they've tried to implement analytics previously over previous years, probably five years ago, 10 years ago. They've, tried this before. Um, so they engaged Wendell to try and brainstorm what the best way to actually have a successful implementation of their analytics program, which they've been interested in for a really long time.
So Wendell brought me in. To help with the implementation and we'll get to the process of them engaging Wendell and, the back and forth and pros and cons of the different vendors and software providers or tools, how I think is a good way to refer to them. So. they engaged me and I, you know, my role for the university was to successfully implement the analytics program and workflow surrounding it.
So their main goal was probably around energy efficiency and sustainability. But it's a really cool, innovative new program that the university has contracted, you know, and an actual energy services company to help them where they've, had trouble, you know, building successful programs in the past.
James Dice: [00:04:51] Yeah. And this is just. For people that aren't wondering why Adam saying the university, we can't say who it is. Uh, it's a very storied institution that has a lot of buildings. They're doing this at scale. Um, it's in the U S that's about all we can say. and the reason that we wanted to talk about this today is because of that history of trying things out and sort of hitting obstacles, hitting roadblocks that I believe.
A lot of other people in the, in the marketplace, a lot of other building owners are hitting those same obstacles. And so the reason I wanted to have you on is because you guys have recently sort of started to get around some of those obstacles and started to prove the real value of analytics. So we're going to unpack all that.
What's the context of like, why did the university get into and want to start down the road of, of building analytics?
Adam Kaufman: [00:05:40] Yes. So I think that the university and the, directors of engineering are definitely in touch with the direction that This industry is moving. And when I say the industry, I mean, operations, engineering, facility, management, construction.
So they definitely have their finger on the pulse and they understand. The changing times it's 2020, you know, data is a big buzz word and leveraging data is really important. And I think this industry in particular is a step behind compared to a lot of others, but they understand the value there and the gap that exists and being able to effectively use that to improve how they operate, how they maintain, how they manage their buildings.
So they've been aware of this, but it's not an easy thing to do. So one, I think that this industry moves slow. Just the nature of it. I think the nature of construction and operations, making changes, people are very sensitive to them and making the wrong step. You can actually take two steps backwards.
So I think that they've been aware that there's a large opportunity here and. That they should be investigating and pursuing how to use these tools properly and how to improve efficiency, improve operations, but they just haven't, gotten there. and to be honest, the path isn't clear how to get there is not this tried and true path.
That's been done a million times that let's just do the same thing. Everyone else has done. People are figuring it out on their own. And like you said, this is a large university, there's multiple campuses. There's dozens of buildings on each campus. And. The way to get that done in the way to, you know, build something that's successful with longevity, which is not just one year and one project in one building that uses the tools.
Right. but actually a program, workflow that is in place for years that they can build off of is tough. So I think why is. They want to improve efficiency. They want to improve productivity. They want to improve understanding of their facilities. And that comes from making decisions around energy and sustainability, as well as business decisions on where they should be allocating money and, what they should be upgrading and what they're in danger of risk.
So, and I know Shannon references some of these in previous podcasts, and I think he did a great job of really tying. You know, he comes from a different lens than I work in, which is, you know, he comes from more of the software lens, but he actually is in touch with reality of what you need to do to successfully, you know, make this work.
These programs work, these tools efficient and add value to facilities, operations, engineering's teams. So the why is there's a whole lot of them and they've been aware of it, but it's not an easy thing to do.
James Dice: [00:08:43] Totally. And I think, I think if you just took like analytics out of your answer right there, or took energy out of the answer there, you could also apply what you just said to so many different types of technologies in the built environment.
It's like. Analytics is kind of the leader. And I think a lot of people that are trying to implement other technologies that are going to find they're going to kind of hit a lot of these roadblocks and learn a lot of these same lessons. So basically what you're saying is it's a sustainability thing that drives the need for energy management reduction, but then it also, the way that this university sees it is it's that plus.
making the O and M team more efficient. It's that plus making better capital replacement, budgeting decisions. It's that plus probably more around indoor air quality and getting students back to campus and, and all that as well. So it just hits all these different number one departments, uh, number two levels of the organization.
Number three budgets. It's a very complex
Adam Kaufman: [00:09:44] value proposition. Yeah. Oh, but you really did touch on that. There's a lot of different things there. And I would highlight that the main reason that this first step was taken is a sustainability is a building efficiency and the productivity productivity is huge.
So the way that we've been able to use this tool to keep everyone working effectively and productively is great, much less. Digging through the dirt to actually make it, make a correction, fix something or much less waiting for something to go on fire, which is probably how majority of the facilities teams work, where, Hey, someone's complaining or something's broken, let's go fix it.
Um, so it touches a lot of different aspects of those teams and that's why it's so complicated to effectively implement it because a lot of people need to get on board with this. Totally.
James Dice: [00:10:38] And there's also with that, making the team more efficient, the way that I've been thinking about this, and a lot of this is through some of my consulting work is like the way that I'm thinking about it is.
it's not just making them more efficient, it's helping them get to things that they wouldn't otherwise have gotten to. So in other words, if you can put analytics in place and that can take your to-do list from, you know, 20,000 things that you should be doing and give you the top five things that are the highest impact, right.
now you can like basically say, well, I'm never going to get to all of these things. So. with the team we have, what's the highest value thing that we can get to is
Adam Kaufman: [00:11:16] are you guys seeing that on campus as well? A hundred percent. And I think that speaks to this time that we're living through right now.
So we're in we're in COVID times, unfortunately. but what that's done to the operational staff is reduced it significantly. So right now they're running on 70% or less, 50% or less to this day. So, and that has to do with the concerns due to different reasons, health concerns, age, and stuff like that. Um, of the staff.
Exactly. You're on campus and, and who's around. And it's interesting that they really need to focus on the things that matter right now, more than they usually would. Totally. So I think that what you said is perfect there, where it allows them to focus on the highest priority issues. Very effectively.
Love it. Okay.
James Dice: [00:12:10] All right. Cool. So let's go back. So you mentioned, the university has been attempting analytics for awhile. Let's go back to all the different attempts before you guys kind of got to this current, way that we'll talk
Adam Kaufman: [00:12:22] about in a minute.
Yeah. So I don't have the full history of every detail of why wasn't completely a success and why it stopped and, and. What exactly was the trigger that made that happen? I have had conversations with people who were involved with those attempts. And I think if we go back to the first attempt was I'm not even going to use the vendor names, but the first attempt was.
A couple buildings with one or two specific engineering staff who were vaguely involved in using the tools. And I think they got an onboarding and a training and they would go in every now and then to try and find issues. And I think the first months, six months were good where they found issues And they were using it more. and they were able to delegate and not, they had relationships with the techs at those times where they really just made a phone call or submitted a work order directly to a specific text bench and said, Hey, can you take a look at this? But over time, if you're not using it really, really you know, in depth, You start to see the same errors come up and you start to say, well, that's not real.
And you start to say, all right, I'm not really going to use this. And I don't think this is right. And you start just kind of drifting away from it. And then by the end of it, you have this software product tool, which is sitting there doing nothing. So by the end of it, you had people telling upper level management.
I don't think this is working right now. So at that point management says, okay, it's not working. I'm not going to pay for something that we're not using and isn't doing anything for us.
James Dice: [00:14:18] Yep. And every, every person that like has done analytics or is out there doing analytics has. Projects like this and I call it collecting dust on a virtual shelf.
The software is eventually just sitting there and the vendor can go in and see who's logging in and everybody's done it. And they've seen, they've had sites where it's like, why isn't this person logged again?
Adam Kaufman: [00:14:41] Yeah, right. Yeah. So the second attempt was another analytics platform that was implemented during construction.
On one specific building and the person leading the implementation of analytics was somewhat involved in the project, on the project team. And I, I believe they engaged someone from the facility as well. And as that project closed, the person involved with the project went on to the next project they were working on.
And the person from the facility. Whose job was not to be an engineer and to look through tasks and, and really track, you know, the savings and all the other metrics that are associated with this. Again, drifted away from using it and ended up collecting dust. So, I mean, that's what it is.
You really have to be engaged with these tools to find value. They do not work. They are not a standalone solution. You can't. Buy it and say, Hey, look at this great bell, you can ring it and it makes noise. And then we don't ring it anymore. It stops making noise. It doesn't do anything collage dust, like you said.
James Dice: [00:15:53] Yeah. And there's another, there's another theme that I've noticed that you're hinting at here, which is, this is not a tool that is just like, we use it sometimes. Um, this is not a tool that is like on the periphery
Adam Kaufman: [00:16:06] of the organization.
James Dice: [00:16:07] And if it is, then it needs to be, there needs to be some sort of path to integrating it.
Like Shannon talked about two weeks ago. He has some clients where he's just kind of spoonfeeding them false and that's totally fine as a way to onboard and a way to sort of scale up and prove the value. but where we're headed is not some like, you know, periphery tool. This is an integral tool for all the reasons that we
Adam Kaufman: [00:16:31] talked about earlier.
So I totally agree with that. I think that the best way to really show the value and, use it properly and effectively is to be in it every day. Checking what's going on because what happens is this, you, you learn it, you understand what it's picking up on? You, you learn what the fallbacks are, which ones are starting to be a little bit more.
You know, real verse versus not so much. And you're able to direct the, the vendors and providers to help you change the diagnostics to improve it. And every single day, I'll tell you as someone who uses it a lot and we'll get into it. My role as a super user champion, I am communicating with the vendor every single day.
Four times a week, let's say, and I'm telling them, Hey, I'm seeing this error, this issue. And I feel that it should be changed in this manner. And I see this a lot. And how can we address this? This is something I caught that wasn't picked up by the analytics, but I found because you sparked. You know, uh, an error or a deficiency in the system.
And I picked up something else. How can we change this at mass scale? Which I think is really important with the vendor that we use, to be able to see this all throughout the buildings that we have. Hey, how can we improve this on an everyday basis? You are, Adding your 2 cents of the fallbacks and the things that you want to improve.
And so there's a lot of great things that start there, just, you know, boiler plate that come with it that are great, but it's something that should be improved upon every single day. And that's why it kind of ties into what you said where it doesn't work as effectively. The value proposition isn't as high when you're using it once a week.
Once every other week, once a month, because you're not improving it at that point. This is something leveraging data is something that allows you to continuously improve how you're learning, verging that data. So the data is there. Okay. Not all of it's great. Some of the sensors are broken. Some of the data is not telling you anything.
So there is also that, that fear of that overload, there's so much there that you can shape it and form it into. What you'll use most effectively. And that's part of it where, Hey, one person, one facility, one site, one building doesn't like that it's presenting tasks, or it's picking up on these issues in this manner.
Another building loves that and they want you to do that a step further. So it really is something that, yes, it's a tool that can be applied at scale where it's. a software product and, and millions of people well can use it, but each user is going to use it in a different manner. Each user should adjust it to the site that they're working on.
The space use, that they have the people that they're engaging. So that's why I say that the more you use it, the better value you get out of it, the more effective it is, the higher productivity around everyone around you. Totally.
James Dice: [00:19:28] So that was the second attempt
Adam Kaufman: [00:19:31] they made and I was a second one
James Dice: [00:19:33] and that one didn't work out.
So is this current rendition the third attempt or how did we get from two to
Adam Kaufman: [00:19:39] three? So I think there was a breathtaking after the second. And then that's where Wendell comes in. So we've been working with this. University for about six years and we've been doing standard, uh, energy conservation measures and projects, and they, they value our opinion.
We have a good understanding of the industry. We work with a lot of different clients. I mean, we've done energy services before and more typical energy management role. but we are aware of the different techniques. We've, we've worked with fault detection, diagnostic, then analytics providers before.
And so they engaged us and said, Hey, we're going to take another swing at this. We really liked this. we see what's happening in the industry. We're paying attention. We see the success stories. I think they follow the better buildings initiative. So they were following that program and they saw some of the success stories that were there and they said, Hey, we can do this.
We want to do this. We've taken a couple of swings before it hasn't really stuck. Let's engage. the. Energy services provider that we're actually using right now onsite and see what they think. So they bounce some ideas off of us. And I wasn't engaged at that point. I wasn't involved in those discussions yet, but my understanding is they wanted us to give them pros and cons of why it's a valuable tool and you know, there's so many different tools.
So we were able to give advice on is what they really should be looking for in our opinion. And the decision they made was they understood the labor that was going to be required to have a successful implementation. And. the technical expertise, which they have as well, but we have from a different lens we have from a different, Whatever you want to call it. We have different technical expertise and we were able to give the labor that they needed and the manpower they needed with different, layers.
You know, I I'm, I'm the person working on it every single day, but I have, you know, more experienced engineers with 30 years of experience. If I needed to engage them in something. And they have that as well, but they're very busy people. Their whole team is busy. Everyone's working on different things. So they really needed to dedicate.
Manpower towards this. Totally. and manpower that was, you know, wanted to learn and wanted be involved in this. And, and that's something else where it is not like. The most engaging intuitive. It's not there yet. there's a lot more work on the software side to make it more.
Hey, it's really easy to use. Bing bang, boom. I picked it up in two seconds. It's something where there's a learning curve to it. So they engaged us to, to bring me on board to work on this in a full-time role and a full-time capacity. So you can take another swing at analytics, knowing that there's value there, knowing that this is something that they should be doing and that can help them meet their energy efficiency goals and improve sustainability and increase their understanding of their buildings.
Can I pause you right there?
James Dice: [00:22:32] Let's do this. Um, Everything you just said kind of reinforces it, this growing opinion of mine, that analytics is still kind of in, and I'm not sure if you're familiar with the technology adoption curve, it's still kind of in this early adopters zone where we're not quite ready for us to really go mainstream yet.
and what you just said there as the university is really. They have this vision and they're willing to figure it out. They're willing to go through multiple phases and multiple obstacles and multiple failed projects to get to that vision. Right. And what I've been making the point of is like the general public, you know, Just take universities.
I think universities are a specific type of early adopter. I think they're early because they kind of have to be that we could talk about it in a minute, but if you take a look at all the other universities besides this, you know, one storied institution in the United States, um, We need to get to a higher level of product performance at a higher level of case studies, like you were saying successful projects before those other, those mainstreams, late adopters, those laggards are going to be able to onboard this technology into their universities.
And then if we're assuming the universities are early adopters themselves, the rest of the building types out there, we still have a long way to go. And in terms of creating what I call a whole product that can then go to scale. Um, and so I just wrote an essay about that. it's just while I was
Adam Kaufman: [00:24:02] on my mind, I wanted to kind of reinforce it right about that, where.
It is a slow process. So one, I mean, this industry in general moves slow slower than we'd like, of course. But yeah, I agree to get that whole product approach. There is different stages of implementation at different levels that are going to be needed and we're paving the path right now. Yeah. I think you
James Dice: [00:24:24] guys are helping provide the whole, product, right?
Adam Kaufman: [00:24:28] that the
James Dice: [00:24:29] university brought you in for kind of help augment areas where their staff couldn't make up for it or the product wasn't quite
Adam Kaufman: [00:24:37] ready in certain case a hundred percent. And that's, why I feel that this role is. Essential and super, super, super important for the full adoption of this.
Because without that help, it's tough to build successful programs, processes that help is really needed because. They don't recognize the value without it. Totally,
James Dice: [00:25:03] totally. So what's the current analytics, I guess, process right now and status of the
Adam Kaufman: [00:25:08] deployment and everything.
Uh, current analytics process is, so that's kind of where my role. Is now is developing that process and that workflow. So I guess I'll start talking about what I do and what my role was start to finish. So I came in and a lot of my role to start was learning the software, learning the, the work, the existing workflow in the, facility and in the campus, on campus and between the different departments to be, to be Frank, you know, there's different relationships between the operations team.
Programming team, the controls, technicians. These are all, they're not siloed. They're all part of one organization, but they're different teams and they have different relationships. And I think that's another thing where this isn't something you throw at someone and every relationship, everyone knows how to proper communication.
And who's going to talk to who and who likes who, and the right way to do something. And you know, These are all different dynamics that come into play. so a lot of what I was doing in the beginning was learning the software, getting to know, and getting engaged with the different engineering staff and, and the programming staff and the different technicians.
So, you know, my role from there evolved to establishing workflow. So right now, if I find an issue or. A fault on the analytics, which we're using, by the way we ended up using clockworks analytics was the, was the provider that the university ended up going forward with and what they really wanted.
And what they really liked about clockworks was that it was a diagnostics as a software product. And what do you mean by that? So I think there's different approaches. And this is, this is someone who has been learning about this industry for the last two years. I won't pretend to say that I know everything about it, but my understanding is it kind of gets broken up into at least two different groups.
And one is more of a tool could approach. And one is more of a diagnostics as a software approach. And the toolkit approach is more of I'm going to build the rules that I want to use, and they may come with pre-built rules. you know, using whatever black logic and you can even see each individual blocks and pull each control point and make your own rules greater than equal to if this, then that rules and that's, the tool could approach it and you can add to them.
And you're really, really engaged in that. And you can make your own and you can make them as complicated or as simple as you want, versus the diagnostics as a software, which is, Billed differently. So some of the toolkit approaches can be one-time fee and you make your own, rules from there.
And you manage that. Or you can hire someone to manage that the diagnostics as a software is, more of a annual build, and they give you support to maintain that system. But more so I like the hierarchy of rules that they have. So basically The most important part that the university felt about this was if someone changes, if their champion, if their super user changes, someone else doesn't need to go back in and look at all the rules that were built and say, do I like these?
And am I going to change this? And I have to learn this and why isn't this working? And why is this set up this way? This isn't right. And there's a lot more. Learning curve when that role changes, then there is with the, the clockworks product where the next person comes in. Someone from that company has been supporting those rules and has written them.
And there. There is a whole chain of why things were done. There is, you know, I send in a message saying, Hey, can you change this? Can you enhance this? Can you add that? Because this is what's going on and they have that chain. So when they make a change, they have a record of, I made it because this, so the next person doesn't need to learn a whole thing of rules.
If they don't like something. They can say, Hey, I don't like this. Please change this. And if it's a reversion and back, they can even bring up a ticket or a message that says, Hey, this was made for this reason. So long story short, the reason that they went forward is they liked the support, the ongoing support, and they like the ability to change that super-user role.
So someone leaves, or if something happens, the next person can jump in. You know, on the ground running. So th there's much less clutter in that transition time. So that's one reason that they want it to go forward with that. Okay. And you know, as that hasn't happened, we haven't transitioned that role, but I can see that I can see that if I had built 50 rules, a hundred rules, 500 rules, the next person coming in might not like those rules that were built or the way they were built might want to change.
I mean, that's a lot of work to go through all 500 rules. And modify and change and enhance, and it helps them learn, but it's a lot. Yeah.
James Dice: [00:29:57] And, and this is one of the themes that I like to point out to everyone in the, in the audience is like for this university to write their own rules. Right. That means that everybody university then needs to write their own rules.
And then now we have all these people like Adam here that are out there just worrying about their rules library when really rules are. I think someone else said in the podcast rules are just physics, right? And I think the software, and this is clockwork's approach, which I like is a lot of the rules are shared between all the other universities that have the same types of air handlers and the same types of mechanical systems.
Right. And. There's other software out there that like, lets you share rules. But what we're talking about here is the, I think Nick calls it Nick, the CEO of clockworks calls it mass customization. So if we have the same type of thing, their handler throughout the entire world, we don't need all the data, different practitioners of every analytics, software, writing a rule set for that same type of air handler.
We could probably just. As he says, mass produced the rules for everyone that has that type of their handler. And then like you're saying customize where we need to customize, but it's coming off of a mass produced rule
Adam Kaufman: [00:31:12] set. Essentially. I agree with that. And that helps them effectively and efficiently build and maintain a productive and valuable software product on their side.
It's a lot harder if they were to have to. You know, make enhancements one by one. Yeah. It's a lot easier to say, Hey, I'm helping the other hundred universities by helping this one university, it makes them want to help you more because it's going to help everyone. Yeah. So I totally agree with that.
And for anyone that
James Dice: [00:31:45] wants to go deeper into this, what is
Adam Kaufman: [00:31:47] diagnostics topic?
James Dice: [00:31:48] We have a white paper that we're putting in the show notes, about exactly what diagnostics
Adam Kaufman: [00:31:53] means.
James Dice: [00:31:54] Um, I did want to ask you, like, because the software can do this diagnostics, it's basically like another step over. What the other software that the university tried.
Right. Because it can do the diagnostics. What does that mean for what you can then spend your time on versus writing rules or versus doing other things, right. Yeah. And, and same thing with the other people that are on the university staff. Right. They don't have to do certain tasks because. The software is taking some of that off their plate.
And that's a huge thing for scalability because we only have so many, like Adam's
Adam Kaufman: [00:32:30] out there. Right. So how has that shown up for you guys? So that's a good point. And I think that goes back to productivity. So I'm able to use my time to actually identify real issues instead of. Building diagnostics to then identify issues to then delegate the workout to then turn the wrenches.
We skip a whole large step there where it goes, Hey, I have the issues that are being reported to me. So I'm able to then review the issues. And, and identify root cause perform root cause analysis and propose solutions. And then we have workflow in place of where those solutions are going to go and the right people that those proposed solutions need to get to in order to actually make changes at the facility level, or like Shannon Smith said, turn the wrenches.
I like that term. It doesn't work if you're not turning wrenches and maybe that's too literal. Where, Hey, you can make a ranch. It's not always a rent. You know, it's not even someone always out there physically making a change. It can be a programmer improving the control scheme or something like that, but changes need to be made.
Someone needs to implement a change for you actually to recognize the value. Cool.
James Dice: [00:33:46] So where are you guys at in your deployment? So we talked about the three waves of the three different types of software, and now you're, you're lenient on clockworks.
And so where are you guys at and sort of,
Adam Kaufman: [00:33:56] uh, rolling that out across campus or both campuses? So we have, we started with one campus, one of the main campuses at the university. And it was 33 buildings, 300 air handling units, so, okay. That's interesting. We focused, or the university focus specifically on the air handling units.
They felt that that was the biggest bang for their buck and. That's not to say they don't want to expand it. I'm not sure exactly which direction they're going to go with that I know we've been super successful looking at just the air handling unit. And I think that they, they involve a lot of energy using systems in a specific area.
So it's less control points to monitor, and it's less areas to make changes, to get a lot of value out of it. From an energy perspective, it makes sense, but it's 33 buildings, 300 air handling units. And. We started in the one campus and we were extremely successful and that was the first year. So we signed up for one year.
That's what the agreement was, the agreement with Wendell and the agreement with clockworks was for one year each. And as we got to the end of that, that year, our results were really amazing. And I think I was, happily surprised and I think the university was really, really surprised as well with the results that we got.
So they made goals and we blew the goals out the water. So it felt good to be involved in, in this program that was new, that people were unsure of. They didn't know if they were going to get half their goal or if they were going to get a third of their goal, they just were so unsure of what was really going on.
And I think we had great results and the results are proven with data, which is also a great part of it. So. You know, another part of my, role, which I'll get to is in reporting. So in part of the reporting, I do write ups. you know, bi-weekly summaries of progress and, and the write offs, we have to actually export the raw data before and after the issue was resolved and, use graphical representation and work order details, and write ups about what we did and what the effect was and, and all data behind it.
So it's not just me presenting a number at them, or we did a calculation. It was. we can show you the before and after of what this change did to the system, which is really cool. So we were really successful in the first year and in the second year, which is where we're at right now. We were, we expanded to another one of their campuses, another large campus where they think there's a lot of inefficiencies and they have high hopes for this one as well.
And I think that we're going to be really successful there we've started on that second campus, but we're still in the early stages. Got it.
James Dice: [00:36:29] So what are the, some of the things that are driving the success that are coming out of the software and getting
Adam Kaufman: [00:36:34] implemented? So, I mean, it's basic changes that are being made.
I mean, it's not crazy engineering projects, redesigns tons of capital budgeting being allocated to this stuff. It's really. Bad dampers, bad valves. you know, bad sensors fans that are ramped up to a hundred percent logic. That's old, you know, controllers that are bad, you know, static pressure and VAV box levels that are, that are stuck or broken or whatever it is.
these aren't rocket science, crazy engineered design solutions. they take intuition to understand what's happening. There's a lot of, Hey clockwork sees this issue, but. It's actually stemming from something else, but that's something else could be a bad sensor. That's something else could be a feedback that's broken that that's something else could be a return fan that's idling or something.
That's, that's stuck at, you know, a CO2 sensor that's reading too high all the time and error. So, you know, these issues are very common issues and there's hundreds and thousands of them throughout probably every campus out there. But if they have significant effects on the system and, there are a lot of issues that would never be solved because they don't necessarily, you know, nothing's on fire people, aren't complaining about it.
It's something that it's just would go on for years. We've solved so many things where the engineering staff has been like, I bet you, this was going on for 10 years. Just like no idea what was going on, but it's been happening forever. Whether that's a damper, whether the fans are running 24 seven, whether whatever's broken, it's just really good at identifying things that would not otherwise be identified.
So, you know, there's standard things. you know, I wish I could say we did a whole redesign of duct work and I actually don't wish we said that because then we would be like, all right, who's paying for this. Yeah. But it's things that are, they're rectifiable, they're easily fixable.
If you know what you're doing, if you know what you need to focus on and you have someone telling you, Hey, it's broken. Totally.
James Dice: [00:38:33] All right. Well, you've used a great. This word that I love to use you said workflow a few times. And I feel like you're after my heart with that word, because I feel like one of the things that I want to get into is like how the university actually uses the software.
And one of the reasons I wanted to dig into this with you specifically is because you are the analytics, what I call the analytics super user, and this role is extremely important, right? And so let's start with like
Adam Kaufman: [00:39:00] your role and
James Dice: [00:39:01] your workflow, and then use that to jump into ad the university uses it and what their workflow is because This lesson of how you guys have done this is I think, applicable to a lot of people that are trying to figure out the same. It's the same question.
Adam Kaufman: [00:39:17] Yeah. So I totally agree that this role is an essential aspect of successful implementation. So I know that a lot of people that are on your podcast talk from the software side of things.
And so I think it's completely different lens that we're going to discuss. And we've been discussing today. But it's also super important and it might not be as you know, people aren't as excited to talk about this super, super important role, because it's not some software startup, which is also super important.
And I love hearing about them and they're doing amazing things, but it's a completely different lens. So my workflow like you referred to is starting with. Identifying the tests or looking at the fall, looking at the issue that's identified by clockworks. Okay. So to be honest, there's thousands of them, thousands of them.
and I'm working on this, we've worked on this every day for a year and there's still a thousands of them. And that that's not to say there's thousands of issues, although there's a lot of issues. But there's always faults in all different ways and they even run analyses just to tell you, Hey, I ran this analysis, even if there's no problem there, it shows you run an analysis, but there's always tons of pages.
I mean, 10 pages of, issues and errors. So I start with each day I go on and I look at the issues. If there's any new issues there, I perform root cause analysis. So I go through the issue and like I said, this is not a standalone solution. The issue is not always 100% correct. And that's just the reality of it.
It's the nature of the work. Not all data is, built the same. Some that is bad. Some data isn't flowing. Some, some that is giving you a, what they call a false positive. So, first is, is this a real issue? I look at it. Is this a real issue, then I perform root cause analysis on it. Then I propose solutions.
And from there, I send it to, one of three different paths. And that's why I'm going to kind of integrate the discussion about the facilities and the university's workflow with my workflow, because they're really, really closely related. Okay. So I went back to, I have relationships with different technicians, with different programmers, with different engineering staff, with.
Different directors of engineering, if it needs to go that route. So from there, identify, we've really grouped it into three buckets. We've grouped it into, is this a physical issue? it's a physical issue, a stuck damper, a bad valve, a leaking valve, a bad fan, something along the lines of that, it gets distributed via work order to a control technicians bench, who I have working relationships with with details about what the issue is, why I think that's the issue.
And kind of reference points of data of, Hey, check this, this, this, and this. This is what I'm seeing. This is why I think it's the issue, you know, investigate.
James Dice: [00:42:08] So let me pause you real quick. So this is why this is a rare job, and this is why this is difficult to hire someone like you, Adam, because there are people out there that are wanting people like you, because what you just described was operating analytics software.
Identifying the root cause and then going develop a relationship with the technician. There's not a whole lot of people that like have that little Venn diagram and I'm not trying to like, pump up your ego here, but I'm trying to point out that as an industry. It's an extremely important role, but we just don't have a lot of those people just like lying around, waiting for jobs.
And so it's this like kind of catch 22 that we have to figure out.
Adam Kaufman: [00:42:50] So, sorry. very much agree with you there. That that part is actually a fallback of, of a lot of people in my role don't necessarily want to do that. Yeah, that's really important because that relationship there makes them believe what's going on.
Makes them easy communication. Those lines of communication are extremely important. So physical issue goes through work order system control mechanic investigates and, comments or whatever else is going on. And it's able to communicate with me if necessary. and most of the time we'll go through this route.
that's that work order path. Um, the other path that it would go through as programming, I would send it to the engineering team or the programming team. And I would reference again, This is what I'm seeing.
These are some reference points. This is what I propose. Can we investigate this programming? Let's say it's not exposed to programming, which a lot of time, it's not Some of the Xs have more programming. That's exposed that I can look at, or I can pull the controllers and see what's going on. I can pull sequences, but a lot of times those sequences aren't right.
This is a DX
James Dice: [00:43:52] 9,100, like a supervisor
Adam Kaufman: [00:43:54] controller. Uh, well, it's a unit level control of the DX is the cases or the, yeah, the Jason's are the supervisors. So right now they have JCI in their campus and they have a mix between DX 91 hundreds, and FEC controllers. And then they have their NAS or their Jace controllers there.
But if it's a DX 9,100, it's easier to pull the, old programming logic. And see what's actually going on in the controller. I can pull sequences the sequences aren't always right. Which I'm sure is the case. You know, I often do a lot of places. Yeah. Yeah. Um, Or I need to even engage the programming team on, Hey, can we investigate what this controller's doing?
I read the sequence. It's not this it's not functioning as intended. The sequence says this. It's actually doing that. Here are some reference points. Here's the date and time that it was occurring. Here's what I think the issue is. Here's what I think we should do. And I would send that over via email, via zoom meeting, via call to the programming team or the engineering team.
They would take a look at it. They would call me up. Hey, let's go over this together. Do you have an hour? Okay. Let's schedule some time. All right. Are you free at three? Great. Let's get on a call. I bring up the raw data from the analytics providers. Here's what I'm seeing. I pull in the control points. You know, explicitly show them what's happening.
They say, Oh, okay. I see what's going on. I'm going to go in the controller, went bad. I'm going to reload it. Or I'm going to change this, programming here. Oh, how about let's add this sequence in. Okay. Maybe they can make that change. Maybe they have to engaged JCI. Uh, they have medicines, so sometimes they need to engage JCI.
Cause they, they keep that tightly knit, uh, making changes to their programming. But. I'm saying there's a dialogue there in that flow. There's a back and forth of here's the issue. Here's what I think we should do. This is what I'm seeing. Okay. Let's bounce things back off each other and see what the right solution is.
So most of the time it goes through one of those two paths, the third path would be, Hey, there's an issue that I don't think is a quick solve. I think this should go to triage basically. And be reviewed by your team of engineers and decide what you'd like to do. Is this something where you'd like to implement a project?
Is this the next energy project? Is this the next place that the capital budget is allocated? And that's definitely the rarest of the three. And it's the rarest partially because it's not what I find is efficient. When it goes that route is when things start to slow down because just the nature of doing work and getting funding is a much slower process than submitting a work order and getting someone out there right now, work orders aren't being done next week, but whenever they get out there, maybe two weeks, so that's the slowest of the three paths, but those are the three workflow processes that we've established as a group, as a program to try and address issues use.
The, automated fault detection, diagnostics clockworks to the best of its ability. So there's three, three, those
James Dice: [00:46:52] three workflows were physical software and basically let's figure out how to develop a project. Okay. Around review is
Adam Kaufman: [00:46:59] what we, we consider it. And definitely, yeah. So, you know, those are the three workflows, so that's how we distribute the work to get, done, to get fixed, to get rectified, to turn wrenches.
How did the work
James Dice: [00:47:12] orders work? Because I know clockworks can integrate and
Adam Kaufman: [00:47:15] create work orders directly. It's a good point. Um, we're not there yet. the work order system that's in place in the university is managed by their it and their security departments. And they didn't want to go that route yet.
This is year two. They're not saying, Hey, we're, we're using this as our holistic. university-wide solution to implement workflow efficiently. Totally. So they didn't really want to tie that in yet. And, and I know that I'm pretty sure that clockworks has the ability to just supplement it where, Hey, you still have your work order system.
You can still go through and use that work order system. But. We can also distribute work through that process. Okay. But anyway, either way, we're not there yet. So what we have to do is I have to take that issue and submit it via their work order system. And they basically copy paste the details into the work order details and assign them to the right person.
And. I actually have a really strong relationship with someone on the engineering team who, does that aspect of it. So I have basically daily meetings with this person who, who actually helps me triage and troubleshoot bounce things off. And, you know, it goes through that, that work order process.
But again, I talk with this person on this, this engineering staff every day, every other day, multiple times a week. And, and we have a really good relationship where we're able to get this workout effectively. And I keep going back to effective productivity value. These are things that make this worthwhile, you know, that, that relationship, if I had to wait a week or that that was a bad relationship where he didn't like what I was submitting and didn't submit it all, the success of this would be a lot less, it would be, you know, Half or whatever that, factor is.
So, I go through him and those are the three processes we have. And what's really important is after the work gets done, the work order gets completed. The programming team completes the change. I then go through a process. we make an engineering design change. No, they go through a project, which is actually something that I wouldn't verify afterwards.
The project team would verify afterwards, but verification is an extremely important aspect of this. So we made a change. Uh, the software picked up on an issue. We delegated work out to the appropriate teams. The appropriate teams made a change. Now, before I close this issue, we have to make sure that the, actual root cause and, uh, symptoms all.
Are you know, addressed and rectified. So I go through, I look at the data weeks, months after it to make sure, Hey, this is now completely completed and there's no more issues and what we did to resolve the issue worked. so that process is really important. I spend a lot of time there and I say that because a lot of the time, 30% of the time, the issue is still there and that's not to say, and there's different reasons what we did.
Wasn't the root cause. There's different things affecting this as well. Um, the, change didn't hold it reverted back to old settings. So something happens every day. It reverts back something like that, and they have to really say, okay, what's going on? And so this is just part of that verification part of the follow through that's required to actually get success.
So that verification process is super important. And then the last things that I do in my role is a reporting biweekly reporting to directors of engineering. Um, and the entire project team, I do annual reports to the utilities where we go through every single issue. We saw the associated savings and with raw data work, order data, et cetera, et cetera.
And I do. energy modeling for issues that are not completely accurately modeled via the automated fall detection and diagnostics. So there are a lot of times where, you know, I'll use two examples here. One is the sensor is broken and treating 200 degrees.
Okay. But it's not actually 200 degrees. So we fix that and it says there's a hundred thousand dollars in savings there. Okay. It's not 200, it's not a hundred thousand dollars in savings. Okay. So, so there, there is some. you know, where I need to intervene and go through and do a custom calc.
And that's just the reality of it, whether that's needed where, Hey, let's not chase those where you need to do customs. That's a different discussion, but I'm not going to present numbers that aren't real. And that's why you really need to be engaged. You need to understand what's going on and you need to understand what the solution was, what the issue was because
James Dice: [00:51:40] that's part of continuing to prove the value, right?
So the reporting piece. Needing to be accurate. That's what gets your three to happen
Adam Kaufman: [00:51:48] and your four to happen? You're fine. Yeah, exactly. Yeah. And that's what gets the buy-in from the utilities. It may see how you reported a hundred thousand dollars and it's actually a $2,000 error or issue. They're not going to pay you for savings on the rest of the hundred because they don't believe you anymore.
Right. So, so that's a really important aspect from the reporting and from the getting rebates and from the utility. So, The verification process is extremely important. The second issue I would have is there's not a control point. So let's say if there's not inter coil temperature sensors, and there's simultaneous heating, cooling load, it's hard for the analytics program to say, Hey, there's this much energy wasted because we don't know the temperatures on each side of those quarters.
Yeah. So then I'm able to use different analyses. See when one coil is closed, see what the temperature rises, et cetera, to be able to do custom calculation. So. You know, that's my role as a whole. And I kind of did a little streamlined explanation of it right there. And, um, you know, going through this little quick, but those work orders and those workflow processes are what makes this, this work.
And it's what makes everything, the one that's part of the team buy into it. When you have those relationships, it's funny, you said, Hey, that last part people don't like to talk to the work orders. That that's what makes them say to their boss. Wow. This is great. Right. And their boss says to the next person.
Yeah, I really liked this. When they check in the director of engineering says, what do you think about this? And they say, well, you know, we like it a lot. We're keeping my guys busy. They like it. They're fixing issues. You know, we're, we're a fan as opposed to, we're wasting our time using this it's nonsense.
Right. You know, those are two different, results. Those are two different explanations of how this program is working. And there's not that much that needs to change to go from one to the other, totally someone hating you tells you, they hate it. Someone that likes you and says, yeah, this is good, it works well for sure.
Love it. I love it. So
James Dice: [00:53:42] one of the things, as I listened to I'm picturing, if we were to like, create a movie about Adam's job here, like a cartoon, I'm picturing you like chasing these technicians around campus, Stuffing work orders in their pockets. Like,
Adam Kaufman: [00:53:54] you're basically insert image. I like it.
I like it.
James Dice: [00:53:58] And they're running from thing to thing. And you're like, by the way that valve's broken. Right. and so you're just chasing them around and COVID might be a little different. You're probably just in your room, just
Adam Kaufman: [00:54:10] so that's a good point. I don't need to be there physically, but yes, but I think
James Dice: [00:54:15] what I have is this, like, I'm always.
Thinking about the future. And I think where we need to get to is, and you guys are early days, right? Everybody's early days where we need to get to is like, instead of. You running around chasing and inserting stuff into people's workflows, the analytics becomes part of their workflow. And part of this is like age of people who are never going to get it right.
Part of it is how can we. get there eventually, right. Where it's part of their daily flow. Right. Without Adam. And that's the difficult part. to two-part question? How far do you guys think you are away from that? And do you think it's possible to get
Adam Kaufman: [00:54:55] there? Um, that's a good question.
James Dice: [00:54:59] We would need you, I'm saying
Adam Kaufman: [00:55:01] there would be a limited role. Yeah, I agree. And I understand, and I think you know, at the end of the day, the facility's thinking like that too. The university is thinking like that too. I think someone needs to be in that role with technical expertise.
Now, what degree of technical expertise and, what. degree of physical expertise and fixing issues is I think we're far away from it being intuitive enough to having the texts themselves on here saying, Hey, I see this issue. I'm gonna go do it right now. I think we're really far away.
If that's possible, it's possible. It's possible, but that comes with our adoption of technology and we're moving fast on there, but. It's just, the triaging is an important factor. You don't want them saying here's an issue. I'm going to go chase this issue, but it's not actually an issue. That's not productive, right.
That's time wasteful because they spend three hours getting to that space, crawling through wherever they need to crawl through it and look at it and say, damn, there's there's, you know, I got a pie in the face or something like that. Yeah. Different part of the movie. Yeah. I, I think it's possible. I think that comes with the improvement in the confidence in each fault that's identified.
but to be honest with you, that goes back to the engagement in the tool. So if you have someone that logs on at the beginning for an hour and only looks at their stuff, and you have 10 people that picks one and goes the tracking, the verification, it gets lost. So that's super user and champion role.
I really think needs to be separated. I truly think that role needs to be separated, You know, whoever you delegate that to, I truly feel that they're almost like if you have control technicians, they have a boss who's delegating the work orders to them. Yeah. You know, even if he's that person, which I still think is a stretch, because that person has a lot of responsibilities that they have to do.
Dustin, the delegation and management of their people and a different expertise. Then then someone, you know, with a mechanical, you know, whether they are in mechanical engineering, but a different expertise than someone who is in the trenches going through issues on a daily basis. So I really feel that that super user champion role should be someone dedicated to that.
Separate from listen, feeding, work orders into their pockets while they're on the run. It's not an efficient, it works really well. Right. You know what I mean? And I know that you say, Hey, maybe they can grab them themselves and put them on their pocket. But the whole processes that are associated with the completion and validity of this work, that's deteriorated degraded, you know?
So I'm not going to say never because I never say never, but I think we're, I think we're a decent amount away from that night. I almost feel that it shouldn't be that way. Yeah. Got it. Got it.
James Dice: [00:58:04] Cool. All right, let's talk about, so we've talked about the energy as a result of the projects, the energy savings that have been driven.
What about, like, let's talk about those other reasons that the university started doing this. Um, and this is kind of closing things out here. so you talked about earlier, you talked about energy. You talked about making people's workflows, more efficient, What about like indoor air quality and bringing students back to campus?
I can't remember what the other one was. Oh, capital planning and any results that have, come up from those other,
Adam Kaufman: [00:58:37] uh, sort of motivators. Yeah. So, and I think there's a lot more than I don't even know. And I know, you know, Nick, the CEO of clockworks was, was on one of your first podcasts, less than like 10 things of the future of what this can be used for.
Yeah. But I think some, ones that we've used slightly or could be around the corner are. switching from reactive to preventative maintenance. So that is one where it just comes along with the territory. Hey, we're no longer chasing fires. There's we're addressing them before they ignite.
You know? Hmm. Okay. So we're, preventatively fixing issues. So that's one that switched from, it's a mindset. It's a maintenance mindset. Hey, I'm putting out fires too. I'm fixing things before they break, before someone complains about it before I have to get yelled at it's the character, the stick type thing.
And where's the
James Dice: [00:59:28] university like on that transition from reactive to
Adam Kaufman: [00:59:31] proactive or preventative? I think that it's in progress. I, that is one that I think has started now. you know, they still get calls all the time and this person's cold, but, it just comes. Naturally with doing work in this manner.
It's not something that needs to be pursued in a different path. It's something that is associated with it. It's just part of it. It comes with the territory. So that's something that they've started on and they notice it. The engineering staff notices it already. And. To put numbers to it. We're not there yet where we say, Hey, we were getting this many work orders that were hot, cold calls that were people upset versus this is how many we're getting now.
And, Oh, there's a 20% reduction. But. It's noticed by the team, by the staff. Cool. So another one that you mentioned is capital budget allocation. I, don't think we're there yet. So that's actually somewhere one point where I wanted to make, where I know everyone loves to say, Hey, there's so many uses of, of all this great software and it's amazing.
And you can use the data in this way and you can use the data as this way, and you can use the data in that way, but it is. Like I'm referencing your previous podcast again, Shannon mentioned data overload where, Hey, there's just too much, you know, and. That's not to say that the capital budget, isn't what you should be focusing on.
I'm saying we're not there yet. So that's not something that we've stepped into yet to understand the data in that manner. I think as people get comfortable with it, as we use this more, as we start addressing more of the issues, we don't get a thousand gets 700. We can actually say all right, I have a little bit more time to use this in a different way.
Okay. But you guys are in year
James Dice: [01:01:07] two. So, so that's the thing that I think people, if they might, if they're listening to this, they're going, man. They're not that far along, but that's not how the analytics on a big campus and a big portfolio works. You take baby steps. You, create wins. You report on those wins and then you expand the next year.
And that's how I've heard a lot of campuses, a lot of portfolios approach it. You guys are ahead of the curve ahead of the curve based on what I'm hearing. Um, and that's just kind of how,
Adam Kaufman: [01:01:36] yep. I, I completely agree with that. Where. It's baby steps, you know, one at a time, but that's in their head, that's in our head as a use case.
So, so it's something that I think could be around the corner, whether that's next years or two years or whatever, but it's there where it will be used in that manner. And that's really important because that speaks to the business case of this now that we get into, Hey, how can this be used from a business perspective?
Where am I spending my money, the best, you know? Right. And so this is the
James Dice: [01:02:08] concept I call traversing through the organization because the person that's after the energy savings is different than the person that's after the ONM efficiency improvements is the person that's like different than the person that's doing.
Capital budget allocation is different than the person that's making the decision on which projects. To pursue
Adam Kaufman: [01:02:31] out of what's already budgeted, right? So
James Dice: [01:02:33] it's just inserting them into different workflows and it takes time to get to
Adam Kaufman: [01:02:37] those different people. you that is completely true.
And that's even true with, Hey, just around the engineering and solving the issues. And you're expanding that to saying, Hey, think about all the groups that were involved just in engineering and operations and facilities. And then you expand that to all these other different. Groups or people involved in the university.
I mean, it's very true, uh, where you traverse the facility or the university in baby steps, you know, from one to the next and you get other people involved and the other get other people to buy in. And part of that is your success stories. So the last one, which I think we should, I actually been able to make progress on and I think COVID has helped that is indoor air quality.
Okay. So clockworks was quick on their feet and they were able, but to tag issues that were associated with indoor air quality pretty effectively. Cool. So that was good. But another thing that we were able to do was use the data. So I get data reports and I can request them when I want to get them on a monthly basis of all the points we're monitoring and.
Basically values that are good values that are bad values that are not sending data mins and maxes different ways to use the data associated with each of the points. And so what we were able to do was we focused on CO2. We focused on. relative humidity and we focused a little bit on outdoor air ratio.
So we needed design days to do our outdoor air ratios. Yeah. Now we can use flow stations and stuff like that, but we don't always trust their calibration flow stations go out of Cal a lot. Um, but undesigned days we were able to do outdoor our ratios to make sure we had comfortable amounts of outside air introduced.
We were able to say, Hey, what are I'm in max values of our relative humidity? Are we getting out of bounds of what our suggested relative humidities are flagged those spaces let's investigate. And another one that's really big is we had a lot of CO2 sensors that were out of Cal. I mean, just reading one, you know, reading zero reading, whatever it is, and not really getting the full capacity now.
the university did kind of make an initiative where, Hey, we're not going to do demand control ventilation as much. Let's, let's disable this on a lot of stuff because of COVID. But we were able to really focus in before they did that on, Hey, here's a list of items we should get to immediately because these are out of Cal and we want to make sure that our indoor air quality is where it needs to be up to standard.
So listen, we were able to think on our feet and use this stuff in a matter. That helped for the obstacles that we had with COVID. So, you know, indoor air quality was a big one that I think we were actually able to act on. And the only reason we were able to act on it is because we had this, analytics software tool.
James Dice: [01:05:05] question for you. What about new buildings? So is the university building new buildings and then what's the role of analytics there?
Adam Kaufman: [01:05:13] Yeah. They are constantly building buildings and, and doing new projects. So I'll, I'm going to group those together. So if there's a building going through a whole retrofit, I'm going to group that in and they're trying to, and I say trying, because like you said, there's different silos.
There's the capital budgets team. There's the engineering operations team. So the engineering operations team is kind of pushing this, they're advocating for it. And so they have to sell that basically to the other group. But what they'd like to do is do what's called connected commissioning, where they engage these analytics providers during the commissioning process.
So they're able to use the analytics with the brand new sequences that are supposed to be perfect. And up-to-date, and you know, how, how often are they actually perfect. You know, commissioning is essential role, but it's done basically via spot checking certain. Things. I mean, you can commission air handlers fully, but if you have a thousand VAV boxes, you were paying a ton to have someone go through every sequence and every operation of every VAV box.
So what ends up happening is you say, Hey, I'm gonna spot check 10%, 20%. so what they want to do is implement this connected commissioning process where they use the automated fault detection and diagnostics over the new brand new BMS with new controllers and. Basically use it as part of the budget in the project for that first year warranty period.
And the commissioning agent would kind of lead this, be in that champion super user role for that first year. Yeah. The idea is it shouldn't need you know, 24, seven attention. It's a brand new building. Yeah. But they're going to get reports on a weekly basis of all the issues they have. And if there are issues that are relevant to, Hey, this was built improperly, or this was designed improperly, or this was, you know, not commissioned, right.
They're calling those contractors backed up to have them fix it. Totally. I think that's really cool. I think that's very different. I think that's very new. I think that. In a new building, you always have things that didn't work. Right. And it's brand new. Like why does it not work? Right. We just bought this, we just built this.
So I think that's something that we haven't really had proven results. Yet. We have the first project you know, it's being implemented right now. I think the commissioning process started like this week. I have to kind of check in on that, but I know it was delayed like two months. It was supposed to be December, but that's not unusual either.
but yeah, so, you know, I don't think contractors are gonna love this process. No,
James Dice: [01:07:43] no, no. I've done it before. They don't, I don't like it. Yeah.
Adam Kaufman: [01:07:47] But that's, the standard that they'd like to implement, you know, all new buildings, all new projects like this, and then work that into the engineering operations budget.
After that first year, after that warranty project interests. Ties right into easy transfer right into. Hey, where were the super users? It's looking at everything. Now look at these buildings as well. Let's maintain them. Totally.
James Dice: [01:08:08] Yeah. All right, Adam, this has been fun. we'll uh, have to check in with you and maybe it's future years of the program, but it's something awesome here and about the history of the university.
And then also, you know, these first couple of years of the deployment. So thanks for, thanks for coming on the show.
Adam Kaufman: [01:08:22] Yeah, I appreciate you having me, James. I'll be checking in with you. I'll be watching the podcast too. Appreciate it.
James Dice: [01:08:30] All right, friends. Thanks for listening to this episode of the nexus podcast for more episodes like this, and to get the weekly nexus newsletter, which by the way, readers have said is the best way to stay up to date on the future of the smart building industry. Please email@example.com. You can find the show notes for this conversation there as well. Have a great day.