Keeping a close watch on construction technology and how it’s vastly transforming the way we do business is technology consultancy JBKnowledge. Jobsite sat down with JBKnowledge’s CEO James Benham to talk about what’s new in the digital marketplace, why some businesses are reluctant to adopt new technologies and why now more than ever they need to embrace it.
Jobsite: Historically-speaking, why has the construction industry been reluctant to adopt technology?
James: It’s such an old industry, and it’s one that people are so familiar with. Their dads did it, their grandfathers did it, and their great-grandfathers did it. Building is pretty fundamental to human beings—it’s what we’ve been doing since we developed opposable thumbs. There’s a lot of reluctance to change the way something so fundamental to human beings has been done. Construction amazes me. The people are awesome and they are really tough, because it’s a really risky business. It’s a difficult business. It’s a dangerous business. Culturally, everyone in this industry is pressured and rushed and prone to risks, and there has always been a big tendency to avoid experimenting. That’s really what you end up with: A fairly risk-averse group of people who don’t want to jinx the way they do things because it’s scary; they don’t want to stick their neck out, and justifiably so – they already stick their neck out enough as it is. It’s a big challenge in front of them, and many of them don’t know where to start with technology.
Jobsite: You’ve spoken about the “cost of inefficiency.” Can you explain what you mean by that? What are the implications for the industry?
James: When you really calculate the wasted effort that results from the manual non-analytical processes that take place, it’s pretty staggering. The anecdotal evidence says we’re not productive. The objective observed evidence is that the industry has about a 40 percent primary trade productivity rate; your primary work is when you’re working at the work base, getting things done. Now, I’ve seen numbers anywhere from 37 percent to 43 percent, depending on who’s sourcing the data and where the data is coming from. That’s really abysmal.
We have to look at the industry’s struggles with profitability—it’s directly tied to productivity.
In the software industry, for example, the software productivity rates are over 90 percent. That means a coder is writing code 90 percent of their day. However, a construction worker spends only about 40 percent of their day actually building. This is why we have to look at the industry’s struggles with profitability—it’s directly tied to productivity.
That’s where they come up with what results in big buckets of money being buried all over the jobsite. It stems from the way they configure the jobsite, or the way they manage their people, or the way they sequence the schedule. The tools they choose to use.
Let’s have a look at Faith Technologies. They had a 43 percent productivity rate, and they instituted an initiative called The 30/30. They said that they wanted everything on a jobsite to be 30 feet away or 30 seconds away because the real problem was walking distance.
Everything was just too spread out. That’s from the bathroom to the tools to the materials to the work phase. They started using software and process to tighten up the entire jobsite. In five years, they managed to drive their primary productivity rate (the actual time spent working) from 43 percent to 68 percent. That’s a major difference in profitability for the company. Now, their current effort is called Strive for Five; they want everything to be within 5 feet in 5 seconds to drive productivity to 80 percent.
With technology, there are a lot of hard and soft cost savings, but the real benefit is being able to prevent mistakes.
That’s really what we’re talking about: how to get people focused on not accepting this industry as a low-margin business. You’re doing it to try and save on hard cost savings. There are four buckets that technology and process help save: money and hard costs (like printing paper, driving, fuel costs); soft costs and staff time; reimbursable expenses (a lot of contractors fail in getting reimbursed for their technology spend); the big bucket, however, is preventable mistakes. You’re trying to use technology to develop and build a more accurate jobsite—a more productive jobsite is also a more accurate one. With technology, there are a lot of hard and soft cost savings, but the real benefit is being able to prevent mistakes.
Take RFIs, for example. The average RFI costs $1,080. The real problem with RFIs though isn’t the soft cost of RFIs. The real problem with RFIs is that 22 percent of them never get answered. RFIs are created because something on the job was not properly defined in the document and specs to the clients. That’s the bucket I’m talking about (the big giant bucket of money that keeps getting buried on the jobsite): preventable mistakes. An unanswered RFI could be catastrophic for a construction project.
Jobsite: Besides the monetary benefits, what is the primary argument for more tech-averse companies to adopt technology?
James: I spoke to 7,000 contractors at conferences last year. I get a lot of interaction with various contractors. The reality is that I haven’t met a trade or a contractor that wasn’t able to adopt this technology. It’s really a matter of will. It’s a matter of being willing to hire the right people, spend the right money, and invest.
The other thing is that I find the ones who are really averse to it tried a piece of tech a few years ago. It didn’t work out well, and so they use that as the justification to never buy tech again. The reality is they probably didn’t pay for the support and implementation or training services they needed. They might have had one guy attend training, and he left, and they never trained anybody else on the software. Usually, the failure was a failure to launch. It was an implementation failure.
Jobsite: How does machine learning and AI come into the picture?
James: Machine learning is when you teach a machine how to learn. You teach it by presenting it a problem and presenting valid outcomes. So you train it, and you say: Here is a problem. Here are examples of good and bad outcomes from this problem; beforehand, it doesn’t know what a good answer is and what a bad answer is. You’re training it, and so you feed an algorithm good, bad, and source problem. Then you have it iterate so that it finds all the potential paths to both cases and then it can learn the best possible solution.
There are very few people actually using machine learning. Everyone is talking about it. One of the people actually using it is Smartvid.io. They’re using machine learning to identify people to objects in photos, transcribe text in videos, and automatically categorize pictures based on what’s in them. That’s machine learning.
The reality is, we need it. We have a labor shortage. Robots are here to help us, not replace us.
What you’re doing is you’re saying, ‘Here are five million pictures. Here are 200 red balls. Here are 200 green balls. Here’s a wrench. This is a forklift.’ Then as it goes through it, it learns how to recognize all of the objects in the picture, and then it auto-tags them. Machine learning is going to save us an enormous amount of time on mundane and menial tasks. The promise is huge.
The reality is, we need it. We have a labor shortage. Robots are here to help us, not replace us. Whether it’s a software robot or a hardware robot, they’re here to help. We still need people thinking. We just need them not doing all of the menial stuff that ties them down.
Jobsite: What are a few of your big takeaways?
James: People have to learn to experiment again. We’ve got to allocate dedicated staff and dedicated budget for technology and innovation. Technology and innovation doesn’t happen in spare time. Therefore, companies need to recognize that they need to innovate and develop new technology with dedicated staff or dedicated contractors. Dedicated facilities. Simply asking people to innovate in their spare time is not good enough. It just doesn’t drive the needed results.
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