The future of construction data and analytics | René Morkos

The future of construction data and analytics | René Morkos

Recently, Bridgit’s COO and Co-founder, Lauren Lake, had the opportunity to sit and talk with René Morkos, CEO and founder of ALICE Technologies, about construction digitization and where the industry is headed.

If you’re unfamiliar with ALICE Technologies or Morkos, here’s a quick overview:

  • René Morkos is the Founder and Chief Executive Officer (CEO) at ALICE Technologies Inc., as well as a Professor at Stanford University.
  • Morkos invented the World’s first Generative Construction Simulator that ingests BIM models and automatically generates millions of valid simulations to build a project.
  • Through ALICE, Morkos has already helped contractors reduce project durations by an average of 17% and labor and equipment costs by average of 13%
  • As a professor of Managing Fabrication and Construction at Stanford, Morkos is on the front lines teaching a new breed of construction production management. By using artificial intelligence, machine learning, and other theories Morkos has changed how the problem of construction production management is both formulated and solved.

Needless to say, Morkos is one of the leading minds in artificial intelligence for the construction industry and has plenty of insight into the future of construction digitization and contractor data management.

Here’s a look at the transcript from our conversation with Morkos, or feel free to check out the full recording of the interview in the above video.

Lauren Lake: We always hear that construction is lagging behind other industries. You’ve probably seen that very famous McKinsey graph that shows construction is one of the laggards in terms of technology adoption. We’ve always felt that this is a bit of an unfair comparison to make. Do you think this paints the industry correctly? What are your thoughts there?

Rene Morkos: That McKinsey graph basically states that the dollar of value produced per hour of work in construction has stayed the same, whereas in other fields it’s gone up. So other fields have become arguably more efficient. So that graph is correct, right? And lots of researchers have looked at it. It’s been validated by third, fourth, fifth parties and so on. The question, though, is why? And the general consensus is, “Oh, construction folks are not innovative.” That’s the part where… Forgive me, but that’s bullshit, right? And here’s why.

         Construction is the second least digitized industry in the world. The only industry less digitized than us is agriculture. The reason, as far as I can tell, and this is really from a decade of research, construction just tends to work on bigger, more difficult problems than other fields.

         Initially, it’s a broad statement, but being someone that has spent the last 10-13 years of my life working very specifically in construction operations problems, I can assure you that a $350 million gas refinery, like I worked on where you’re burning through $1.6 million a day, represents the biggest, most complex problems that our species has to solve. What we effectively do, is we build custom made factories for unique products, sometimes hundreds of miles away from civilization. And that’s not something that the other fields tend to do at all.

         The other reason is that for us to digitize construction, we have to wait for input, which is the design piece, to be digitized. And that happened with the advent of BIM, but the issue is that buildings are bigger and more complex than software engines. It’s only in 2015 in my mind that the processes started to become fast enough where they could actually crunch buildings in 3d, and you can put them in a cloud and then rotate them and visualize them.

         So I don’t think that we could really digitize construction until 2015 and later. Interestingly enough, when you look at what I call the construction Renaissance, which is like 2017, I think it was $140 billion of VC funding poured into construction.

         Suddenly digitization became this really sexy topic. When you look at construction as a field, research shows that construction actually very rapidly adopts technologies within a certain sector, for example, masonry, electricity, etc. However, technologies that span across disciplines have taken us some time, but my really strong opinion is that it’s not because we’re not innovative, it’s just that we’ve had to wait for the machines to catch up. If you look at the history of digitalization, you look at the first thing that got digitized was just finance. How hard is that? We’ve got a bunch of accounts, and numbers moving between accounts.

One of the things that I really get excited about is if you look at the percentage of GDP represented by the finance industry. In the ’80s, finance represented 10-12% of GDP. Today finance represents 22%. The fact that it represents 22% means that the salaries are higher, and people are better paid. What I’m hoping, or what I think will happen to construction, is as we start to digitize construction, and we introduce more and more efficiency, we will tend to have better jobs that are higher paid for less work hours. 

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Lauren Lake: I want to hear a little bit more about ALICE Technologies. So you take all of these parameters from different workflows, you look at available crews and resources, and you’re able to create the best case scenario in terms of all of those data points coming together. From what you’ve seen, are there any key data points that contribute to a project’s success or failure?

Rene Morkos: So what we did was we separated planning from simulating. What’s kind of interesting about ALICE is that the plan is the rules that govern your project, whereas the simulation is the solution to those rules. What we’ve realized is, hey, let the people do what they’re really good at. Let them use their gut sense, let them tell me how many tasks, how many crews I need, how many crews are available, what’s the radius of the crane, what’s the calendars, those are rules. 

         The other half of the puzzle, which is a simulation, let the computer crunch that. So let the computer figure out if I have 10 crews available on 20 tasks, which order I’m going to do them in. So that’s kind of how our system works. 

To answer your question from our perspective, what dictates a successful project is having a good idea of how I want to build it in the first place. And that seems obvious, but you’d be surprised. For example, we were brought into a $300 million bridge at one point, and I remember one of my colleagues saying, “Hey, they need to start construction in 30 days, but they don’t seem to have an idea of how they want to do it.” This is a problem. And so to answer your question, I could give you a canned response, which is, yeah, you need to have the list of tasks, the durations or production rates.

         But maybe from a deeper perspective, you need to know how you want to build it. There’s a misconception around ALICE. People think, “Oh, it’s AI.” So I’m going to open it, I’m going to say, “Hey, how do I build a hospital?” 

It doesn’t work that way. What it’s asking is, for example, if you want to build a hospital, how big is it? What does it look like? Give me a 3d model. What are the tasks required to build a column? Let’s apply that to all the columns. We actually stopped calling it AI, artificial intelligence, and started referring to it as IA, intelligence augmentation. Because what you’ll see over and over again is that, you need the human to set up what needs to be crunched and then interpret the results.

         If you don’t have any construction experience, ALICE, as a solution, is not useful for you. It’s a multiplier of your underlying ability. If your ability is zero, it’s going to multiply by zero. One of the nice things about ALICE is that it forces project teams to be clear about what we know and what we’re assuming, because we’re actually simulating that construction project for you.

         With ALICE you’ve got to be clear about, “Hey, here’s how I want you to build it. Here’s the way I want you to build.” And so that’s kind of what we’re seeing. It’s having that knowledge, that underlying knowledge before you enter into the project.

Lauren Lake: The construction industry is growing more and more competitive. How are you seeing your customers leverage the data they have in order to gain a competitive edge?

Rene Morkos: We were just involved in a $300 million bid with two general contractors involved. They had a buffer, and a contingency plan of X million dollars. We were working with one of the two GCs in the joint venture and by running the project through ALICE they realized that they could half that contingency. What’s interesting about it is that I kept thinking that the objective of ALICE is to find the best solution. I.e, to tell you, “Hey, the best way to build your product is to build it with two cranes, no over time, regular grind concrete,” and so on and so forth.

         What we started to realize was that humans are really good at gut sense. So the big deal with ALICE is that you can say, “Add a crane, re-simulate. Try over time, re-simulate.” And what humans would do is they would take that collective group of 20 solutions and start to understand how their systems behave. For example, a GC thought that the two crane option was going to be better, but they’ve run it a bunch of times and can see that the three crane option is what they’re looking for, or vice versa.

         To make a long story short, they ended up taking that half contingency and they won the bid.

Lauren Lake: That’s an impressive story. Is that using data that they had been collecting over many, many years? Or what was the timeline in terms of, do you know how long they had been building up that database?

Rene Morkos: I know that they were using databases of production rates and databases of durations that they accumulated. My advisor at Stanford was a guy called Martin Fisher. And so Martin, about three years ago, his big message to the industry was, “Hey, we don’t have a worked out data model.” In fancy sort of academic terms – a conceptual process model. So what is a conceptual model? What are the pieces of the puzzle? And so here’s a question that I kind of like to spring on my students, which is, is the production rate an attribute of the task? Is it a property of the task that you’re working on or is it a property of the crew?

         Or is it the property of the element that’s being worked on? The truth is it’s actually all three together. And so, something that’s interesting is if you look at the design world, you’ve got a number of data models. What’s interesting is in construction, we don’t have that worked out, which is kind of surprising. 

In design, there’s probably about 3000 people around the world that are working on these design standards. We don’t have that in construction. One of the things that we’ve done with ALICE, is actually built the first fully worked out data model for construction simulation. 

         In the design, you’ve got elements like columns, slabs, pipes, whatever it is, each of those elements has some construction information related to it. Up until now, the construction information that pertains to these elements has been across three or four data silos. Whereas with ALICE, we have these things called recipes. Recipes ask “what do I need to build this given element?” Each recipe then contains all the information required to build that element. As a result, I think that will enable people designing operations management systems to far better trade, exchange, store, and learn from data for the construction project.

         And from the GC perspective, the game’s becoming about, how do you take these technologies and integrate them? ALICE is a part of the solution, but contractors need to be looking at other solutions and then aggregate those together, and really think about the interfaces between those solutions, what data is being generated, where does it come from, and where does it go when you’re done. That’s the kind of stuff that you can start doing today.

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Lauren Lake: How do you see the world of data and analytics in the construction world evolving over the next, let’s say five years?

Rene Morkos: I think we are living through the most exciting period in construction history over the last two millennia, and started happening in 2017-18. I think that the machines and technology have reached a point where they’re able to represent the things we need to represent, and crunch the problems we need to solve. If you look at companies like Bridgit, ALICE, Versatile Natures, Doxil, Canvas, more and more of the construction universe is getting digitized. As more of it gets digitized, more digitalization can be built on top of it. And I think that’s a great thing, a great day for everybody.

         I personally believe that there’s a new ecosystem that is coming at us in this field. That new ecosystem is going to get decided by, say 30-50 companies today, that know what they’re doing. Those 30-50 companies are general contractors, designers, software companies, and so on and so forth. I believe the fundamental value proposition of general contractors is starting to slightly change. Because I believe that the GC of the future, success will be based on the ability of the general contractor to identify, evaluate and assimilate new technology. One of the questions I’m often asked is, “Well, why does a company like ALICE or Bridgit exist?” The answer is that our companies represent an outsourced R&D department of these larger GCs. That’s what we are.

         The name of the game has started to become: how do you leverage these outsourced R&D departments? And the way to do that is you’ve got to be clear about, okay, well, if I take these two or three solutions and put them together, how is it going to fit? How are they going to interact, play nice with each other, but how are they going to play nice with my existing ERP system?

         Another thing that I think is going to start is understanding how to compare groups of options. If we can generate 30 different ways to build, how do I compare these 30 versus those 30. And so those are kind of the two big ideas. One is data analytics – understanding how all these complex variables interact and being able to visualize it in a way that makes you understand which are the critical variables. The other is, how do you start comparing groups of options to groups of options versus comparing one to another? So those are two areas I see construction data and analytics evolving.