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Welcome to Nexus, a newsletter, podcast, and membership community for smart people applying smart building technology—written by James Dice. If you’re new to Nexus, you might want to start here. And if you’re interested in digital twins, you’ll love the free weekly Nexus newsletter. Sign up here:
I remember it vividly…
I was a senior in high school, the year was 2005. My soccer team had just won the state championship. As a result, I’d been invited to join a prestigious club team in a nearby city. I was giddy and nervous for the first practice with my new teammates and coaches.
As I gathered my gear preparing to make the 75-minute drive, I realized I had no idea where I was going. So I did what any tech-savvy teenager would do in those days: I went to the computer, typed in MapQuest.com, and printed myself the step-by-step driving directions to the soccer facility.
MapQuest helped me get to practice just fine. But today, it would be silly to print out directions on paper before leaving the house. Today, I use Google Maps, a digital twin for transport. And Google Maps doesn’t just do MapQuest’s old job for me—its scope has expanded far beyond that.
We’re at a similar place today with digital twins for buildings. Ask your average building owner and they’ll say their tools are doing just fine. They get the job done. They need digital twins the same way I needed Google Maps back in 2005: I didn’t.
And yet, as we’ll unpack below, building owners need digital twins for the same reasons I need Google Maps today:
If this talk of jobs being done sounds a bit weird, let me explain. It comes from a theory developed by the late Harvard Business School professor Clayton Christensen, who unfortunately passed away last week at the age of 67. Clay, as those who knew him called him, is an absolute legend in the business world. I, my friends, am not—but I’ll try to summarize Clay’s theories for our purposes.
Put simply, the “job to be done” is what the customer hopes to accomplish. If I want to get a question answered, that’s a good job for Google. If I want to buy stuff online, that’s a good job for Amazon. Clay’s innovation was that the job is the causal driver behind a purchase. If you understand the causal driver, you can create products and services that customers want to buy.
Innovation, according to Clay’s theories, starts with doing the job better. Then the job broadens in scope. Take Amazon for instance. It didn’t start as The Everything Store. It started as an online bookseller. If a customer wanted to buy a book online, Amazon did that job better than anyone else and it expanded from there. They carried more books than anyone, which attracted customers, which allowed them to offer more products. Then it attracted independent merchants, which again attracted more customers. Eventually, it evolved into the hybrid store and platform that it is today.
Let’s walk through the parallel progression made by Google Maps:
Old jobs done better
New jobs done
Expansion into adjacent jobs
Maps is integrated with Google’s database of local businesses (with 1-5 star reviews, descriptions, website links, etc)
Two-way third-party app integration is added via the Maps API.
E.g. The Lyft app sits on top of the Maps infrastructure, feeding it more data. In turn, Maps pulls prices from Lyft and Uber and scooter companies to help users find the best way to get to their destination.
Maps acquires Waze, picking up new features like checking nearby gas prices, speed trap reporting, traffic slowdown alerts, and adding stops along your route while navigating.
The “job” started as directions for driving and ended up doing that very well, then expanding into a slew of other similar jobs. It’s now a digital twin platform for anytime the user isn’t inside a building.
Which is a great segue for any time we are inside buildings.
Now let’s look at commercial buildings through the lens of the Jobs To Be Done framework. Building owners and operators hire people, things, or software to perform the following jobs:
Maintain, Update, Upgrade, Optimize/Minimize Energy/Water, Secure, Plan, Comply, Manage Tenants, Manage Vendors, Manage O&M Staff, Automate Systems, Collect/Store/Access/Transmit Information, Transport, Navigate, Audit, Model, Schedule, Engage
Whew, I’m tired just writing that. Much respect is due to all of the overwhelmed facility operators out there.
But let’s be honest with ourselves: the results aren’t cutting it. In many buildings, it’s the equivalent of printing the map out using Mapquest.com. Building performance has fallen far behind what technology is capable of. Even state of the art buildings are still pretty dumb. As my colleague Cory has said, Facebook knows every contour on my face, but the building where I work doesn’t even know I exist. The jobs can be done much, much better.
Also, we should note that the list is quite a bit longer than it used to be. And it’s still growing. There are cities (NYC, Boston, LA, etc) setting climate targets forcing buildings to comply. There are growing occupant needs and expectations. There is competition continuously raising the bar. And there are new technologies allowing new possibilities, which allow ever higher targets, expectations, and competition.
Finally, if we perform the 5 Whys exercise on each of those jobs, we realize those are just supporting jobs. They’re secondary. It all comes down to two primary jobs:
If the secondary jobs were being done well, it would be in service of these primary jobs. In my view, improving primary jobs requires the integration of secondary jobs. And most tools doing secondary jobs are not integrated together.
As we covered in part one of this series, a digital twin improves performance by combining the built environment technologies that came before it. Using a building’s digital twin, we can:
Let’s walk through some examples, broken down into the same progression we used for Google Maps. Because I haven’t worked with a modern digital twin myself, I think it would be silly for me to make up examples. So these are plucked from a report provided by global consultancy Arup. They cite real examples from twin implementations around the world.
Note: I intend to keep updating this list as new examples are documented. It’s barely scratching the surface of my imagination.
Surveys and walkthroughs
With augmented reality we can merge the digital twin with its corresponding physical building, in order to look at the walls of a building and instantly visualise the infrastructure behind them. We can look at building components such as HVAC and see exactly how they are performing — and where problems may be developing.
We can put data in front of users in contextually realistic ways. We can put city managers at the actual intersection, where they can then analyse the data. They can look at different aspects that are occurring around that intersection while they pull out different bits of data and do visual comparisons and correlations.
Maintenance and Equipment Service
On a typical system, a technician may receive an alert which reads “Fan broken, Office 14, Building 3”. What seems like a clear message is actually very ambiguous. What type of fan is it? What parts are required? Where exactly is the fan located?
Using the digital twin for the building, a vast amount of information can be accessed by the technician before they reach the job, allowing them to prepare fully for the task in hand. A virtual inspection can be carried out showing the exact type of fan, the most recent maintenance report, a video tutorial and an exact nomenclature.
The correct tools can then be prepared to carry out the job. The technician is given an accurate photo of the location as well as the most efficient route to take to reach the job. All of this combined reduces the time spent working out what is required and keeping the actual maintenance time to a min
Modeling and Simulation
By running simulations on the model, the digital twin can generate 100,000 times more data than can be provided by the sensors alone. In this way, the digital twin becomes smarter much more quickly than the unaided physical building ever would.
Furthermore, a digital twin simulation can interpolate many more virtual sensors than would ever exist in reality, thereby filling the information gaps which could not be gleaned from the sensor data alone. Beyond even this advantage, the digital twin will already understand the relation between these points, whereas discovering this relation solely from real-time building data would be extremely challenging, due to the many non-linear and dynamic physical responses characteristic of the built environment.
But, as with any other building, energy meters do not at present indicate whether the current building is over-consuming. And as with any other building, the Province House is a unique building. There is no other building of exactly the same shape, at the same location, experiencing the same weather conditions — and no building is used in exactly the same way as the Province House. There is no reference building against which to compare it. But by building a virtual twin of the building, we obtain an exact copy of the building against which to compare real-time data.
Automation
As the technology matures, so does the terminology, hence the need to include autonomy, learning and reasoning within the definition. The proposed definition acknowledges that the digital twin is still a model, but it has the potential to evolve into an autonomous system with less human intervention, through AI-enabled design and control.
It can start performing critical tasks on it’s own. It can transition away from preprogrammed controls. The twin starts to learn. It can use predictions in control sequences. It can simulate a control sequence, and then download it to the real world. (My addition: Like downloading an algorithm for “how to fly a helicopter” in the Matrix!).
Design
I believe that the traditional jobs of structural and M&E engineers will disappear — AI can do them very easily on a digital twin model, at a fraction of today’s time and cost. I believe that the only engineering that will survive in the medium term will be the one with a creative angle (…) —PROFESSOR CARLO RATTI, Director, MIT Senseable City Labs
Wayfinding
Current workplace designs rely on workers’ familiarity with office fitout to find their way around. Using data from 3D as-built BIM models and occupant geolocation, a digital twin can be used with an augmented reality app to guide users to free rooms, quieter or under-occupied spaces, or specific facilities.
Prediction
The revolution in software development and the democratisation of high- performance, rapid development computing power. Digital twins are getting attention because they also integrate things like artificial intelligence (AI) and machine learning (ML) to bring data, algorithms, and context together, enabling organizations to test new ideas, uncover problems before they happen, get new answers to new questions, and monitor items remotely.
Wayfinding + Space Management + Safety
The introduction of two-way interaction systems in conjunction with wayfinding can also be used to provide adaptive and real-time wayfinding in a space. This type of digital twin provides facilities managers with the ability to have real-time influence over the usage of a space.
This digital twin study quantifies the benefits of wayfinding tools and demonstrating the extreme value in it’s potential to affect time reduction and adaptive management applications. Additionally, wayfinding technology can benefit the safety of the space by using it for the identification of evacuation paths in emergencies.
Automation + Occupant Engagement
The Melbourne team has developed an app to gather information on worker comfort and perceived productivity. This qualitative information helps to feed a digital twin to yield intelligent suggestions for alternate working locations and to provide user control over personalised environments.
The result projected an increase of up to 11% in productivity. Additionally, this functionality establishes the methodology of using AI derived from empirical evidence to inform future designs.
Spatial Optimization
With the aid of a camera-and-sensor IoT network, a digital twin could ascertain current space usage. Pattern recognition could yield intelligent suggestions on how a space is optimised (e.g. through retrofitting or subleasing rooms or zones). (Or, as was discussed on a recent episode of Next Story Up, determine exactly where to put the coffee shop to maximize customers.)
In the next part, we’ll walk through the major questions to be answered and challenges to be solved before digital twins go mainstream.
Subscribe to make sure you get part 3 in your inbox:
Welcome to Nexus, a newsletter, podcast, and membership community for smart people applying smart building technology—written by James Dice. If you’re new to Nexus, you might want to start here. And if you’re interested in digital twins, you’ll love the free weekly Nexus newsletter. Sign up here:
I remember it vividly…
I was a senior in high school, the year was 2005. My soccer team had just won the state championship. As a result, I’d been invited to join a prestigious club team in a nearby city. I was giddy and nervous for the first practice with my new teammates and coaches.
As I gathered my gear preparing to make the 75-minute drive, I realized I had no idea where I was going. So I did what any tech-savvy teenager would do in those days: I went to the computer, typed in MapQuest.com, and printed myself the step-by-step driving directions to the soccer facility.
MapQuest helped me get to practice just fine. But today, it would be silly to print out directions on paper before leaving the house. Today, I use Google Maps, a digital twin for transport. And Google Maps doesn’t just do MapQuest’s old job for me—its scope has expanded far beyond that.
We’re at a similar place today with digital twins for buildings. Ask your average building owner and they’ll say their tools are doing just fine. They get the job done. They need digital twins the same way I needed Google Maps back in 2005: I didn’t.
And yet, as we’ll unpack below, building owners need digital twins for the same reasons I need Google Maps today:
If this talk of jobs being done sounds a bit weird, let me explain. It comes from a theory developed by the late Harvard Business School professor Clayton Christensen, who unfortunately passed away last week at the age of 67. Clay, as those who knew him called him, is an absolute legend in the business world. I, my friends, am not—but I’ll try to summarize Clay’s theories for our purposes.
Put simply, the “job to be done” is what the customer hopes to accomplish. If I want to get a question answered, that’s a good job for Google. If I want to buy stuff online, that’s a good job for Amazon. Clay’s innovation was that the job is the causal driver behind a purchase. If you understand the causal driver, you can create products and services that customers want to buy.
Innovation, according to Clay’s theories, starts with doing the job better. Then the job broadens in scope. Take Amazon for instance. It didn’t start as The Everything Store. It started as an online bookseller. If a customer wanted to buy a book online, Amazon did that job better than anyone else and it expanded from there. They carried more books than anyone, which attracted customers, which allowed them to offer more products. Then it attracted independent merchants, which again attracted more customers. Eventually, it evolved into the hybrid store and platform that it is today.
Let’s walk through the parallel progression made by Google Maps:
Old jobs done better
New jobs done
Expansion into adjacent jobs
Maps is integrated with Google’s database of local businesses (with 1-5 star reviews, descriptions, website links, etc)
Two-way third-party app integration is added via the Maps API.
E.g. The Lyft app sits on top of the Maps infrastructure, feeding it more data. In turn, Maps pulls prices from Lyft and Uber and scooter companies to help users find the best way to get to their destination.
Maps acquires Waze, picking up new features like checking nearby gas prices, speed trap reporting, traffic slowdown alerts, and adding stops along your route while navigating.
The “job” started as directions for driving and ended up doing that very well, then expanding into a slew of other similar jobs. It’s now a digital twin platform for anytime the user isn’t inside a building.
Which is a great segue for any time we are inside buildings.
Now let’s look at commercial buildings through the lens of the Jobs To Be Done framework. Building owners and operators hire people, things, or software to perform the following jobs:
Maintain, Update, Upgrade, Optimize/Minimize Energy/Water, Secure, Plan, Comply, Manage Tenants, Manage Vendors, Manage O&M Staff, Automate Systems, Collect/Store/Access/Transmit Information, Transport, Navigate, Audit, Model, Schedule, Engage
Whew, I’m tired just writing that. Much respect is due to all of the overwhelmed facility operators out there.
But let’s be honest with ourselves: the results aren’t cutting it. In many buildings, it’s the equivalent of printing the map out using Mapquest.com. Building performance has fallen far behind what technology is capable of. Even state of the art buildings are still pretty dumb. As my colleague Cory has said, Facebook knows every contour on my face, but the building where I work doesn’t even know I exist. The jobs can be done much, much better.
Also, we should note that the list is quite a bit longer than it used to be. And it’s still growing. There are cities (NYC, Boston, LA, etc) setting climate targets forcing buildings to comply. There are growing occupant needs and expectations. There is competition continuously raising the bar. And there are new technologies allowing new possibilities, which allow ever higher targets, expectations, and competition.
Finally, if we perform the 5 Whys exercise on each of those jobs, we realize those are just supporting jobs. They’re secondary. It all comes down to two primary jobs:
If the secondary jobs were being done well, it would be in service of these primary jobs. In my view, improving primary jobs requires the integration of secondary jobs. And most tools doing secondary jobs are not integrated together.
As we covered in part one of this series, a digital twin improves performance by combining the built environment technologies that came before it. Using a building’s digital twin, we can:
Let’s walk through some examples, broken down into the same progression we used for Google Maps. Because I haven’t worked with a modern digital twin myself, I think it would be silly for me to make up examples. So these are plucked from a report provided by global consultancy Arup. They cite real examples from twin implementations around the world.
Note: I intend to keep updating this list as new examples are documented. It’s barely scratching the surface of my imagination.
Surveys and walkthroughs
With augmented reality we can merge the digital twin with its corresponding physical building, in order to look at the walls of a building and instantly visualise the infrastructure behind them. We can look at building components such as HVAC and see exactly how they are performing — and where problems may be developing.
We can put data in front of users in contextually realistic ways. We can put city managers at the actual intersection, where they can then analyse the data. They can look at different aspects that are occurring around that intersection while they pull out different bits of data and do visual comparisons and correlations.
Maintenance and Equipment Service
On a typical system, a technician may receive an alert which reads “Fan broken, Office 14, Building 3”. What seems like a clear message is actually very ambiguous. What type of fan is it? What parts are required? Where exactly is the fan located?
Using the digital twin for the building, a vast amount of information can be accessed by the technician before they reach the job, allowing them to prepare fully for the task in hand. A virtual inspection can be carried out showing the exact type of fan, the most recent maintenance report, a video tutorial and an exact nomenclature.
The correct tools can then be prepared to carry out the job. The technician is given an accurate photo of the location as well as the most efficient route to take to reach the job. All of this combined reduces the time spent working out what is required and keeping the actual maintenance time to a min
Modeling and Simulation
By running simulations on the model, the digital twin can generate 100,000 times more data than can be provided by the sensors alone. In this way, the digital twin becomes smarter much more quickly than the unaided physical building ever would.
Furthermore, a digital twin simulation can interpolate many more virtual sensors than would ever exist in reality, thereby filling the information gaps which could not be gleaned from the sensor data alone. Beyond even this advantage, the digital twin will already understand the relation between these points, whereas discovering this relation solely from real-time building data would be extremely challenging, due to the many non-linear and dynamic physical responses characteristic of the built environment.
But, as with any other building, energy meters do not at present indicate whether the current building is over-consuming. And as with any other building, the Province House is a unique building. There is no other building of exactly the same shape, at the same location, experiencing the same weather conditions — and no building is used in exactly the same way as the Province House. There is no reference building against which to compare it. But by building a virtual twin of the building, we obtain an exact copy of the building against which to compare real-time data.
Automation
As the technology matures, so does the terminology, hence the need to include autonomy, learning and reasoning within the definition. The proposed definition acknowledges that the digital twin is still a model, but it has the potential to evolve into an autonomous system with less human intervention, through AI-enabled design and control.
It can start performing critical tasks on it’s own. It can transition away from preprogrammed controls. The twin starts to learn. It can use predictions in control sequences. It can simulate a control sequence, and then download it to the real world. (My addition: Like downloading an algorithm for “how to fly a helicopter” in the Matrix!).
Design
I believe that the traditional jobs of structural and M&E engineers will disappear — AI can do them very easily on a digital twin model, at a fraction of today’s time and cost. I believe that the only engineering that will survive in the medium term will be the one with a creative angle (…) —PROFESSOR CARLO RATTI, Director, MIT Senseable City Labs
Wayfinding
Current workplace designs rely on workers’ familiarity with office fitout to find their way around. Using data from 3D as-built BIM models and occupant geolocation, a digital twin can be used with an augmented reality app to guide users to free rooms, quieter or under-occupied spaces, or specific facilities.
Prediction
The revolution in software development and the democratisation of high- performance, rapid development computing power. Digital twins are getting attention because they also integrate things like artificial intelligence (AI) and machine learning (ML) to bring data, algorithms, and context together, enabling organizations to test new ideas, uncover problems before they happen, get new answers to new questions, and monitor items remotely.
Wayfinding + Space Management + Safety
The introduction of two-way interaction systems in conjunction with wayfinding can also be used to provide adaptive and real-time wayfinding in a space. This type of digital twin provides facilities managers with the ability to have real-time influence over the usage of a space.
This digital twin study quantifies the benefits of wayfinding tools and demonstrating the extreme value in it’s potential to affect time reduction and adaptive management applications. Additionally, wayfinding technology can benefit the safety of the space by using it for the identification of evacuation paths in emergencies.
Automation + Occupant Engagement
The Melbourne team has developed an app to gather information on worker comfort and perceived productivity. This qualitative information helps to feed a digital twin to yield intelligent suggestions for alternate working locations and to provide user control over personalised environments.
The result projected an increase of up to 11% in productivity. Additionally, this functionality establishes the methodology of using AI derived from empirical evidence to inform future designs.
Spatial Optimization
With the aid of a camera-and-sensor IoT network, a digital twin could ascertain current space usage. Pattern recognition could yield intelligent suggestions on how a space is optimised (e.g. through retrofitting or subleasing rooms or zones). (Or, as was discussed on a recent episode of Next Story Up, determine exactly where to put the coffee shop to maximize customers.)
In the next part, we’ll walk through the major questions to be answered and challenges to be solved before digital twins go mainstream.
Subscribe to make sure you get part 3 in your inbox:
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