- Podcast
The Quiet Innovation of Infrastructure in the Lab with CBRE’s Mark Trueman
When you picture a laboratory, chances are your mind conjures a clean, clinical environment — white coats, centrifuges, the hum of fluorescent lights. But beneath that surface hum lies a different kind of engine. It’s invisible, often overlooked, and yet essential to the success of every experiment: the infrastructure of operational support. The pipes and platforms that quietly keep the science moving.
Mark Trueman has made a career out of helping others do their best work — not by stepping into the spotlight, but by refining everything behind it. As Global Technical Director for CBRE’s Full Spectrum Lab Services, Mark works at the nexus of real estate, technology, and science. His job is deceptively simple: keep labs running. But the reality is far more complex. Lab equipment today is more expensive, more fragile, and more interdependent than ever. A broken centrifuge no longer just means a pause in workflow — it might derail a critical study, or compromise months of irreplaceable data. What once could be managed with a clipboard and a yearly checkup now demands predictive analytics, real-time monitoring, and nuanced human collaboration.
In this conversation, Mark walks us through the transformation of lab management from a reactive, hands-on discipline to a proactive, data-visualized environment. He shares how labs have evolved into high-stakes shared spaces, where understanding the difference between “in use” and “almost in use” can save thousands in misallocated resources. And he introduces a powerful metaphor: imagine your lab like an airport — every machine, every process, flagged in real time for risk, readiness, and repair. With modern visualization tools, scientists can now “see” their lab the way pilots see the runway: with full confidence that the systems behind them are working exactly as they should.
This is a conversation about trust. It’s about sitting down with scientists and saying: we’re not here to take your tools — we’re here to make sure they work when you need them. Mark shares stories about the art of change management exemplified by doing one radical thing: listening. Because at the end of the day, innovation isn’t just about the equipment. It’s about people. And the best technology is the kind that frees people up to do what they do best.
Links & Notes
Transcript
Pete Wright:
Imagine you are a scientist poised at the edge of discovery. You’ve designed your experiments, prepared your samples, and then the centrifuge won’t start. The microscope blinks out. The lab equipment, silent and inert. This is the enemy of progress.
Today on Seeing Beyond the Dashboard, I’m talking with Mark Truman, global technical director at CBRE, whose work is transforming the hidden world behind the lab bench. Not just by fixing equipment, but by helping scientists actually see it, its usage, its health, and its future needs all in real time. It’s a quiet revolution powered by visualization, turning the invisible chaos of modern labs into something that can be understood, managed, and optimized. I’m Pete Wright, and this is Seeing Beyond the Dashboard. Mark Truman, welcome to Seeing Beyond the Dashboard. I’m honored that you have taken the time to chat with me today.
Mark Trueman:
No problem. I’m looking forward to the conversation.
Pete Wright:
Well, let’s start with a little bit about you and the organization. CBRE is one of those companies that is deceptively diverse. And your role in it, I have to say, just in researching what CBRE does, is also deceptively sort of hidden in the marching orders of the organization. So can we start with a little bit of background about what you do and how you landed there?
Mark Trueman:
Yeah, yeah, sure, sure. And it’s funny, because I think anybody, especially in my sort of line of work, anybody who came to CBRE started off with that same, exactly as you described, that same kind of, “Huh, that’s kind of interesting. Why are they interested in me? That’s sad.”
Pete Wright:
Yeah.
Mark Trueman:
So yeah, CBRE is the world’s biggest real estate company. And so of course the second you go looking for CBRE, real estate is predominantly what you will find. And it’s kind of interesting, the story is that effectively you could almost cut the company in two and say one side is what you would think of as traditional real estate, which is leasing, renting, buying, selling buildings and real estate and managing those portfolios. Half of the business pretty much, and it’s more than half the business in terms of people, is actually in facilities management. So it’s managing the buildings and that’s everything. So that’s hard technical services. It’s the catering, the cleaning, the security, the whole works. And so within that, you’ve then got this niche business, which is supporting laboratories. And that came about organically from, I’m going to say 1990, when one of CBRE’s clients in facilities who was a global pharmaceutical company said, “You’re doing a really nice job of managing our buildings. The real issue we’ve got is they’re filled with all this stuff and we don’t really understand it. Can you help?”
And so, that’s how the business was born. It’s predominantly still around the laboratory equipment in particular. But I would say it’s grown and evolved into more of a sort of end-to-end laboratory services business. So the idea is that we come in and we help our clients to manage their laboratories.
Pete Wright:
I think that’s hysterical, that it evolved organically into, “This is an expertise that we’re going to develop internally about managing your stuff.” That the lab managers say, “We don’t know how to manage this stuff. You’ve got to do it for us.” And I think there’s this Venn diagram of bench scientists in life sciences and IT and facilities managers, and you’re right in the middle.
Mark Trueman:
Absolutely. And that’s one of the great things I love about the business stacking. And it’s one of the things that differentiates our little bit of the business from, I would say, the broader business. Because generally in facilities management, the idea is that you want to be invisible. You kind of want, everything just works. And that’s true of what we do as well. But the key difference is that everybody in our organization is interacting directly with scientists on a day-to-day basis. We’re in the labs. That’s the whole point of our services. We’re there in the labs. We’re live and present. And that’s quite different to your sort traditional facilities management where it’s you, you’re invisible in the background somewhere. My background prior to CBRE, I spent pretty much my entire career working in and around the laboratory equipment for equipment manufacturers. So I’m a physicist by qualification.
Pete Wright:
No kidding. I was wondering what sort of young lad dreams of managing laboratories, and I think we’ve just found it.
Mark Trueman:
Yeah. I forget the term for this, but I came across it a few years ago. I’d always thought that my kind of career planning was what I would term haphazard. So I have no idea what it is that I want to do. I’m just going to do things. And if I enjoy them, that’s great. And when I don’t enjoy them anymore, I’ll see what else is out there. And I actually found out a few years ago, that’s a real term. Yeah, that’s a, not going to say it’s a good or a bad strategy, but that’s a specific strategy for how to approach a career.
Pete Wright:
The absolute value of strategy is what it is.
Mark Trueman:
Yeah. So that’s kind of been my approach. So yeah, I mean, worked for instrument manufacturers for 20 years in R and D, in final test and installation of complex systems, and then probably spent the latter half of that time and moved into customer service, service organizations, technical support, application support, the whole kind of supporting functions. And so the move to CBRE, whilst I was one of those people who went, “CBRE, who on earth are they? Oh, they’re a real estate company. Why are they interested in me?” And then of course within days saw about 25 CBRE vans from people who work for the company within a mile of my house.
Pete Wright:
As soon as you know to look for them.
Mark Trueman:
Absolutely. So yeah, that’s kind of how I ended up in this organization. And one of the things that I was really interested about, and I still love today is, there’s two things about the organization. One is, so I deal with all of our clients, so I’m not dedicated to any specific one.
Pete Wright:
You’re not in one lab, in other words.
Mark Trueman:
Yeah, so I deal with all of our clients. And as a physicist who’s always been working on equipment that is supporting different types of science, which I absolutely have no understanding of that stuff, because that’s a whole separate discipline which is way beyond my capabilities. I was always fascinated in how the scientists used the equipment that I was involved in to do this fantastic science. And I get to see that across the board, which is just amazing, and deal with the actual scientists. So we get to really get embedded and understand, and the whole point of our service is, how do we enable the scientists to be productive? To be as efficient and productive as they can be? Although what that means in itself is probably a 40-page PhD thesis.
Pete Wright:
Okay, well, let’s not go quite that far. But I would like to talk a little bit about the complexity of the lab environments, and this takes us to the context of equipment and facilities, data visualization, the kinds of technology that we’re talking about. So set the table for us around the complexity of the lab right now. What’s the scope of data that you’re trying to help these labs track?
Mark Trueman:
So I think that’s been one of the sort of paradigm shifts in the lab in the last 10 years. And it’s accelerating, so there’s probably been more change in the last two years than in the previous eight. And it’s a great opportunity, but it brings its challenges. And that is we’ve gone from a situation where you’ve got departments, departments have got equipment, and probably way more equipment than they ever needed.
Pete Wright:
How many of them are wrapped on a track, a cart with a cord wrapped around it?
Mark Trueman:
Absolutely. And a lot of what I would say traditional lab equipment, so the things that somebody who isn’t close to it would walk into and expect to see in a lab… Microscopes, balances, centrifuges, just very standard routine equipment. That’s changed and that’s continuing to change. And so there’s a lot more, the departmental thing, those kind of departmental barriers typically are already broken or are in the process of being broken. And the mix of equipment is moving from lots of the standard core equipment, which is inexpensive, pretty reliable, and not that critical. Because okay, my balance breaks, well that’s fine. I’ve got another 25 lined up on the bench next to me.
The mix is shifting towards more complex equipment, more automated equipment, and which inherently is more expensive, and it’s more expensive to purchase, and it’s more expensive to maintain, and so there’s less of it. And so it becomes more critical and there’s more sharing of equipment. And so suddenly you move from this situation where you’ve got, everybody knows what they’re doing, everybody has almost their own equipment, to a situation where you’ve now got more complex equipment, which is shared. More people are trying to use it. The equipment itself is being used more than it was before.
And so that then moves you from a position of the age-old, “Well, what maintenance do we do? What maintenance does the equipment need to keep running? It gets a service once a year. Great.” Well, it got a service once a year when it was being used once a week. It’s now being used 10 hours a day by 20 different operators. Is once a year still okay? Maybe not. The other bit that I think has changed, and this is the bit that’s really accelerating is, those systems, traditional systems, there’s no information. They’re working or they’re not working.
Pete Wright:
That was going to be my principal question here. There was a time when what we were tracking on this equipment was uptime. Does it work or does it not work? Is it on or off? And the complexity of these systems now, my hunch is any one piece of equipment, you are now tracking multiple variables of its performance.
Mark Trueman:
Absolutely. And tracking how is it being used, not just how much is it being used. And that can be really important. Because there are, any given piece of equipment can run multiple hundreds of different types of method using different types of samples, some of which are really easy for that equipment to run and some of which are right on the edge of that equipment’s performance in terms of what you’re trying to do. And are extremely aggressive chemicals, et cetera, that require a different level of maintenance. So over time, we’ve gone from a situation where there was very little information other than that manual record that you had of, “Well, they got a service once a year,” to a situation now where every piece of equipment has a bunch of data that it’s inherently generating that can give you useful information on what you should do with it. What’s the best way to operate it? What’s the best way to maintain it? And moving you from that time-based maintenance into something like predictive maintenance, where ideally we’re intervening before things fail rather than waiting until after they fail.
Pete Wright:
So we’re talking here about moving toward real-time equipment monitoring, or let’s say data-driven lab services. How does that impact the bench scientists? To what degree are they walking up to a piece of equipment and still having to knock on it to make sure it works? Or does this liberate them in some way? I’m looking for, what are our advantages for the kind of monitoring you’re doing?
Mark Trueman:
The advantage in principle is that the scientist gets to a point where they can walk up to the piece of equipment confident that it’s going to do what they need it to do. And that takes time. We can’t just do that by collecting some data and saying, “Hey, this is all good now.” It takes time to build that confidence that, “Hey, do you know what? Every time I come to use this piece of equipment, it does just do what I’m expecting.” And realistically, I mean the goal for us in the end is we, yeah, and I touched on it earlier, that I don’t even pretend to understand some of the amazing science that’s being done on the equipment. But what I do know is that what we can influence is the availability of that equipment, and that equipment when it’s available is working as it should. So that’s really our focus. The trick for us then is, in a world where we’ve now got all of this data that we never had before, there’s different personas.
So we talked a lot about the scientists, the scientists. In the end it’s, “Is my piece of kit there and does it do what I need it to do? Can I get on and do my science?” That’s part of it. Of course, there’s a bunch of people who are managing the lab who are looking at it and going, “This is just a huge expense.” Lab space is really expensive. Sustainability increasingly is important to people, and lab space is about the most inefficient, from a sustainability perspective is not great. So how can I do more with less? How can I reduce the footprint? How can I reduce the amount of redundant equipment that I’ve got?
And so part of our services is also to look at that. So to look at a total fleet level and say, “Here are the risks in your asset fleet. Here are the things that, here’s the equipment that potentially is going to cause you a problem.” Because we think it’s going to fall over in two days from now, and it’s going to stop a huge study. That kind of extra layer of information. And the good bit is we’ve got all of this data that we never had before. Internet of things, sensors have added an extra bonus of environmental measuring that we can do, vibration measuring that we can do. There’s a huge amount of data we can now take in.
Our job is to take, and I use this fairly frequently, a comment from Kenneth Nolan, which is around context. His comment was, “Context is the key. From that comes the understanding of everything.” For me, the data is the context to our program. Let’s imagine a piece of equipment’s failed and the context we have is, “Well, it’s not on a service contract. The repair is going to cost us $5,000 and we’re in the middle of an urgent study, therefore we really need this thing running.” Logical conclusion from all of that is, well, we’d better get a service visit ordered and we’d better get somebody in to come and repair it. You could then look at it and go, “Well, what if we knew that was the third time it’s failed in the last two months for the same problem? And what if we knew that it’s 12 years old? And what if we know that the repair is going to cost us $5,000, but we could buy a brand new one for $20,000?”
Maybe then the decision changes. So maybe then you go, “Well actually, we probably want to look at purchasing a replacement. That seems the logical thing to do.” And then you can say, “Well, yeah, but what if we said there’s 12 identical pieces of equipment in the same lab and the average utilization of that equipment is 10%? Now you’re not purchasing a replacement. You are probably keeping the one you’ve got, turning it off, maybe cannibalizing it for spare parts to keep the others going. And so, what we’re trying to do constantly is take all of this data and use that to give objective decisions about what to do with the equipment fleet to optimize it as much as we can. Using that data as the context, but to make it in the end really simple, i.e. go replace that piece of equipment. Or you could redeploy that piece of equipment from that lab to that lab, and you’ll get way more usage out of it. That’s what we’re trying to build.
Pete Wright:
I think this entire sort of path we’re on implies another part of this conversation, which is demand for change or the change management process. I have this picture of a team of scientists going to a conference somewhere and they learn about some brilliant new piece of technology. And they come back and they say, “You know, we’re adapting. We’re going to do some new things and we need some new fancy piece of equipment.” What have you learned about how to introduce change into these environments? To enable change for bench scientists and lab managers without causing disruption or potentially redundancy?
Mark Trueman:
In some ways it’s common sense, I would say. It’s kind of obvious, and it’s just fundamentals of change management, which are regardless of environment. For us, the key to that is the scientists. Because if the people who are ultimately the recipients of the program, if they are not seeing the benefits, or even worse they’re suspicious of the motivations of the program and they don’t buy into it, it doesn’t matter how good the program is or how good the opportunity is. It will not fly. It will not happen.
And if you take something like utilization measurement, so I’ve yet to do or see a utilization study on lab equipment that came back and said, “Oh, you’ve really not got enough. You need to go out and buy loads more of this equipment.” Pretty much without fail, you might find, and this is important, you might find pockets of equipment where that’s the case. And that’s an important point for a utilization study. It’s also an important point to sell a utilization study to a scientist, because inherently a scientist will see a utilization study as, “Well, you’re just trying to cut down the fleet. You’re trying to remove the equipment. That means I’m going to have less access to the equipment or I’m not going to be able to go to my favorite one that I’ve always used.”
Pete Wright:
“Don’t make me fight for resources, Mark.”
Mark Trueman:
Absolutely. And that’s a natural view of a lab equipment utilization program. And so, the only way to manage that change is to spend the time sitting down with scientists to understand and to explain, “Well, no, the point of this is… Yes, there may be some equipment where we say you really don’t need everything that you’ve got, but that’s data driven.” So there’ll be some fundamental backing to that. And we’re not doing that in isolation. We’re doing that in the context of the total fleet, the whole organization. So we’re not just looking at your one lab and saying, “Oh, you’ve got too many of those.” We’re looking at the bigger picture to make sure that in the end you’ve got the right thing.
I used to be responsible for the customer experience program for a large global instrument manufacturer, and I inherited a part of the business. There was a big sort of transactional survey mechanism in place, and fundamentally it was a net promoter score program. And I inherited a business with a net promoter score of minus eight. It was the second-worst performing division in the entire company.
Pete Wright:
Mark, that’s an inheritance that just screams failing up. What is happening right now?
Mark Trueman:
That was fun. That was fun. I’ll be honest, that was great. But when I left that business, the net promoter score was at plus 76 and was number one in the company. And so as you can imagine, I ended up on lots of conversations with some very senior people going, “How did you do it? What’s the magic dust that you’ve done here?” And so I had to have that conversation with them and be very honest about it and say, “Well, the bit where you ask for feedback on what we can improve? Well, we read it, we actioned it and we fixed it.” That’s pretty much it. It was no more complicated than that. The point being that in that change management program, I think that’s just an example that speaks to… You need to sit with the scientists and you need to listen to what they’re saying, and then you need to do something about it. And if you do that, you’ll get their engagement and you’ll get there. Yeah, it’s really not very complicated, but it’s surprising how often it’s not done.
Pete Wright:
But Mark, that’s just so hard. Yeah, it’s absolutely brilliant.
Mark Trueman:
So the change management bit is consistent with any change management. It’s engaging with the right people, it’s engaging with them really early, to have very honest and open conversations about what the objectives are, why we might be thinking about going in a certain direction or why we think a certain dataset might be useful. And it’s having that continuous conversation. It’s the ongoing conversation to demonstrate.
And then the other bit, and I think especially with new programs, new technologies, it’s small pilots. It’s being able to engage with a small group that maybe are early adopters. They’re interested in the tech or whatever it might be for whatever reason. Engage with them, do a pilot, get the proof of concept, show that it isn’t just about “We’re going to remove stuff.” It’s actually about, “We’re going to optimize everything that’s there.” That’s the goal.
Pete Wright:
It seems like from a lab or facilities management perspective, you are able to come to the table with the science operators and say, “Look, we have data we’ve never had before.” Before, equipment utilization was based on your reports. You would say, “Here’s how we used it and here’s how often we use it now.” We get to tell you how often it’s being used, how often do those things line up, and how does that influence conversation? Because I can imagine at an individual lab level, that could make for a contentious kind of operation.
Mark Trueman:
Rarely do those things line up. We have these open conversations very frequently, which is, and again, utilization is one parameter. But it’s an important one, where I can stand here and say very confidently that I’ve yet to do a utilization study where the average utilization was more than 25%. People will be genuinely, jaws will drop at that statement and then we’ll show the data that we collect and go, “Well, there it is.” It’s not that people are deliberately overstating things in the first place. It’s that genuinely, they’re like, “No, no, no. We use that one all the time.” And it’s only when you’ve got the data that you go… Well, again, this is important in terms of the context. If you’re just looking at the lab equipment utilization, what you see is the lab equipment working. What you don’t see is the five hours of prep that went on to prepare everything before the lab equipment is involved. And you don’t see the 10 hours of data crunching at the backend once the data’s come out.
So again, it’s important to have some context around when we look at lab utilization. And again, this is where integrating the data becomes really helpful. So you can have a lab booking tool. So, in the same way as you can book an office space and actually smart office spaces will, you’ll book the space for two hours and the smart ones will know when somebody entered the room because that’s when they turn the lights on. And when somebody left the room and they’ll go, “Well, it was booked for two hours and it used for 20 minutes in the middle.”
The same thing applies to lab equipment. So you can look at lab equipment booking tools and say, “Yep, somebody booked this from 8:00 AM to 12.” And you can see the operating window of the instrument for 30 minutes in the middle. It doesn’t mean the other time isn’t valid and wasn’t necessary for them to be there physically in front of the instrument, setting up, prepping, getting things working. And again, understanding that time is helpful. Because then we can say, “Well, if you’ve got all of that time to prep and set up, actually, that’s the kind of tasks we can do.” So then, your scientist is truly only focusing on their bit.
Pete Wright:
I think so much of this goes to how fundamentally terrible human organisms are at estimating these kinds of behaviors. You asked me how often I pick up my phone every day, I’d say, “I don’t know, 20 times.” And I am genuinely gobsmacked when I see “200,” right? It’s an order of magnitude larger. That’s where we’re going here is just that being able to rationalize assumptions.
Can you talk about the visualization part of the data management? Because I mean, we’ve been able to get data tables for a long time, but how does the visualization part change the way you do your jobs?
Mark Trueman:
Yeah. I think for me, there’s a couple of angles to it. So one is, people ingest data and ingest cues in different ways. So you won’t be surprised to know that if you put a table in front of me, I’m happy. This is my world. And that wouldn’t be a surprise, I’m sure. But that’s not true for everybody. And I think the one thing that is true for everybody, because it’s part of everybody’s daily life, is that visual cues are hugely powerful. So you think about driving to work and stop signs, traffic lights, road signs. I occasionally do drive across Europe, multiple countries, multiple different languages, et cetera, et cetera. I can understand 95% of the information that’s displayed as I’m driving the car.
As humans, we’re instinctively good at responding to visual cues, and that’s kind of backed up in everyday life. And so, one of the things that I saw a few years ago. So I went through a lean implementation at a manufacturing site, and one of the topics in there was visual controls because lean relies very heavily on visual controls. And there was a beautiful example that was given to us. It was a Russian visual control example, but it was essentially, you were given a table of product performance and it was just a blank, was just a table. And the question was, “Where do you want to focus your efforts?” And the starting point was, “Oh, God.” Okay, you’re trying to digest this table and you’re trying to take all the information and see what’s in it.
Pete Wright:
It’s all in Cyrillic, right? That’s the example we’re looking at.
Mark Trueman:
Yeah. So then it’s like, okay, 15 seconds I think you had. So it’s like 15 seconds later, anybody know where to start? And a few people had maybe picked up one thing or whatever was going on. The next slide is, “Okay, so we’re going to change it up slightly, exactly the same table with very simple red, amber, green status indicators on some of the relevant cells. This time you have three seconds. And in three seconds everybody went, “Well, we need to start there.” And it was a really powerful example of how visual indicators can really help understand complex data. It was a relatively simple data set, but even then, 15 seconds, nobody in the room was doing anything, three seconds with the visual cues, everybody was in on it.
And so when we’re talking about how do we take all of this data and make it easy to understand whatever the recommendation is or whatever the observation is? And that could be as simple as for a scientist in the lab, “All my equipment’s available.” If I’m looking at an overview of my lab and I can see all my equipment is green, I know I’m good to go. Similarly, if I can see one’s red, I know straight away-
Pete Wright:
Know where to start.
Mark Trueman:
I’ve got a problem here. I need to go solve that before I can get started. So it’s as simple as that. Then you can take that to the next step, which is, “Okay, I’ve got a study to do. I need these 10 pieces of equipment. Nine of them are green, this one’s got a problem. Find me the nearest one that I can use that’s an alternative.” And there’s something about that information visually that I think we just naturally interpret very quickly and is far more compelling than, “I’ve got a table of stuff I can show you.”
Pete Wright:
I know we’re leaning toward the end of our time together today, but I have a couple of more questions. And I think they’re important for those who are looking to build high functioning lab environments. And the first one that I don’t think we’ve talked about yet is, the onboarding of equipment on this process. When you are transitioning to a highly visual data flow, recognizing the number of sensors you need to bring into play, from hydration sensors, vibration sensors, temperature… How would you characterize that process?
Mark Trueman:
Yeah, I think from an equipment perspective, it’s become easier and easier. And some of that again is just technology, internet things… And more, not good, but more openness about sharing of data and the ease of which you can access data and integrate data.
Pete Wright:
Between sensor providers and information.
Mark Trueman:
Between us, between our clients, between the sensor providers. We’ve already got, we’re generally responsible for tracking all of the equipment related to the… All of the data related to the equipment. So you know what it is, where it is, what level of service it gets, all of its service history, records, all of that stuff. Age of the equipment, warranty status. We’re already tracking that. So we’ve got a lot of information there that we’re keeping. Then there’s financial cost information. There’s downtime information. There’s all the sensor information. It’s becoming increasingly open about how that data is able to be shared, and that helps.
Pete Wright:
Very positive, yeah.
Mark Trueman:
It is. And then if I think about some of the, even just some of the sensors. So when we first did a utilization study, it was using power monitors. And to do that, you had to disconnect the equipment. You had to get a special in-line lead, et cetera, et cetera. Now you can just screw a clamp on and 30 seconds later, it’s up and running. And you’ve got the data. And actually there’s, with the learnings over the last 10 years for those companies that have been doing that stuff, not only is it as simple as you just take the sensor, you bolt it onto the cable, and 30 seconds later you’ve got data coming through, but they’re able to interpret that data and go, “Oh, yeah, that’s a Panasonic incubator.” Which still blows my mind to this day.
So I would say some of those things previously were quite a heavy lift. They’re increasingly becoming easier and easier, to the point where we can deploy those things and we can get meaningful data back, really quite quickly and with minimal disruption to the lab, because that’s always… I mean, yeah, talk about onboarding of instruments. Just something as simple as labeling of an instrument and verifying the instruments are where we think they are. I’ve lost count of the amount of times I’ve been physically removed from a lab when trying to pursue that activity. So, “I’m here to just do a standard.” “What are you doing in my lab?” “Well, I’m just checking that everything is where it should be.” “Well, nobody told me you were coming. Get out.” That’s happened on numerous occasions, which comes back a little bit to change management and communication, but…
Pete Wright:
You’re quite a rogue, Mark.
Mark Trueman:
But yeah… Any disruption to the lab is a problem. So minimizing that and being able to launch these kinds of programs, and to be able to give those kind of visual overviews really quickly, again is very powerful.
Pete Wright:
To what level of detail are you applying this sort of tracking technology? When you look at individual sort of tabletop microscopes, do they get some sort of tracker on them or is it just the big equipment that you’re finding?
Mark Trueman:
It’s a real mix, and I guess I always take a very pragmatic approach to these things. But not everybody does. So there are different approaches that they’re taking. And I’ll use just an asset labeling kind of example. So it’s kind of calmed down a little bit now, but there was a period of time where everybody wanted to throw RFID tags onto lab equipment.
Pete Wright:
On everything.
Mark Trueman:
Everything needs an RFID tag. And so you say, “Okay, why?” “Well, because we want to know where it is. And then you look at the 300 kilo mass spec that’s hard plumbed into the gas and electrics and say, “Are you telling me that somebody’s going to move that and you’re not going to know?” Yeah. It’s like, “I’m not sure you need an RFID tag on that piece.” And the funny thing is in those days, a few years back, but the things that you did need RFID tags on the things, that really do go missing, pipettes and small pieces of portable lab equipment, there weren’t really solutions for those at the time. So the things you really need a solution for, you can’t track. The thing that you are now putting an expensive label on is not moving anywhere without somebody knowing about it.
So I kind of take the same approach to how we look at the lab equipment in general. What’s the value in tracking it? What’s the value in monitoring it? If it is a critical piece of equipment that’s critical to the process, that has a history of, it’s higher failure rate type equipment. It’s heavily utilized. It’s used by multi, then absolutely we want to track that. Is it an optical microscope on a bench somewhere? Do we need to know where it is? Yes. Do we need to put a live sensor on it to track where it is? Probably not.
Pete Wright:
Can we generally assume it’s not moving from that desk? Yeah.
Mark Trueman:
And nobody’s really that interested in moving it either. But there are flips of that where, and this was an inventory example again… So I was once doing an inventory, and we couldn’t find two chart recorders. And these were in a quality-controlled environment, and turned out they were backup, more manual chart recorders to an existing building management system. Because cost controlled environment, these things were calibrated every year. And we couldn’t find them. And we eventually found them in the bottom of a filing cabinet drawer stacked on top of each other. And these things were sent away once a year for a calibration that was probably $2,000 to be available as backups. And it’s like, well, the second you threw them in the drawer, that calibration is gone. I mean, it’s out the window.
And so there are, something like a balance for example, might be an example where you do want to put a sensor on it, that actually has got an accelerometer or whatever. So that you can see it’s carefully, somebody’s carefully precision calibrated it in situ. And then somebody comes along and bumps into it and nudges it across the desk and then goes, “Oops,” and puts it back. Well, you might want to know about that. Again, it’s understanding where you get the value out of tracking these things and then applying that.
Pete Wright:
You said that the pace of change is only accelerating. More change in the last two years than in the eight prior. So if you were to sit down with future Mark and raise a glass to the change that you see ahead in the next say five to 10 years, what are you looking forward to? What do you find you’d still love to have now that sort of more of what you’re capable of getting?
Mark Trueman:
Yeah. I think there are two things, two key things from just a general lab operating environment that I think are in our grasp, but we’re not quite there yet. One is, and it’s a bit around the data integration piece. So one of the things that you can do with the visualization tools, which is great, is create a custom workflow. So this week, the thing that I’m going to be doing requires these 10 pieces of equipment. What I want to see is those 10. And I want to see how they’re performing and where they’re at in a custom view. That we can do. And I think that’s really powerful, because next week it might be 10 different pieces of equipment. So you’re moving from a static view of what’s important and critical to a much more dynamic way of managing it. Being able to do that is great.
So that’s just the equipment. What if all the equipment’s available, but you haven’t got the right consumables to run your method? What if all the equipment’s available, but the air conditioning’s gone in the building and the labs are running at 40 degrees and nothing that’s coming out of that equipment is valid anymore?
So I think there’s a piece about integrating that oversight of the lab process more than the lab equipment or the lab consumables or the lab. And I think where it’s coming together is there are lots of digitization going on in the labs, but a lot of it is kind of independent. So we still use a system for tracking the lab equipment and its records. There are equipment booking tools, which typically are separate. There are LIMS data systems that are the foundation of all the quality control spaces, which typically are separate. It’s the coming together of all of those, integrating that with all of those other things that aren’t the lab equipment, but actually are fundamental to the lab process working, and then bringing that together in a view that allows you to go, “I can look at my entire global portfolio,” down to, “I can look at that one instrument in that one lab over there, and I can see exactly what’s going on.”
I’ll give you an example from Microsoft HoloLens. So very early days of Microsoft HoloLens, we went to Microsoft Office in London and we had a demo. And one of the things, the most compelling thing that they showed me, which has kind of stuck with me since was an airport. And the idea was, you could visualize the entire airport. And then you had flags everywhere. So there’s a plane on the runway over here, it’s 20 minutes delayed. That’s a flag. There’s a plane over here that’s not going anywhere for three hours because there’s a problem on the plane. There’s one over here that’s delayed because there’s some baggage handling problem, and it went right through down to there’s a pump in that air conditioning unit that is running over temperature, and it needs an intervention, and here’s the service manual.
And it was that complete overview that you went, “Wow, I can see how powerful this would be in that kind of lab environment to be able to go, yeah, I can see everything from complete global fleet overview.” I can understand all my risks, I can understand where I can optimize, I can see everything, and I can break it right the way down to, That thing needs a service visit tomorrow. It’s going to fail the day after, and we’re going to stop our process.”
Pete Wright:
Everything you’re describing… Right now feels like we’re on the precipice of that.
Mark Trueman:
Yes.
Pete Wright:
That feels like the next thing. And famously, HoloLens a canceled project. We don’t need a HoloLens to do it. We’re already about there, and that’s really delightful.
Mark Trueman:
Absolutely. On the edge.
Pete Wright:
Mark, this has been terrific. Thank you so much for your time today.
Mark Trueman:
No problem at all. It’s been a great conversation. Good to talk. And yeah, I’m really interested, and I think you can probably see that I think we are on the cusp of it. I think a lot of the, some of the tools that are out there now, that weren’t there 18 months ago even, are moving us in that direction that I think will be really powerful and will make a significant difference.
Pete Wright:
Laboratories today are facing a paradox. As technology becomes more sophisticated, the systems needed to manage it grow even more complex. But thanks to new tools in real time visualization and predictive insight, scientists and laboratory managers are beginning to see, literally, the forces shaping their work. What Mark reminds us is that innovation doesn’t happen in a vacuum. It needs infrastructure, foresight, and increasingly, the ability to turn invisible data into visible action.
Thanks for listening to Seeing Beyond the Dashboard. I’m Pete Wright, and we’ll see you next time.
