Over the last four years, Arjun Chandar, the founder and CEO of IndustrialML, has been building and finely tuning the company to make factories smarter with his enterprise software as a service (SaaS) platform. I had the pleasure of sitting down with Arjun to take him back to where it all started before digging into the details of his fascinating story and garnering his insights from the industry he holds dear.
Fraser: Take us back to the very beginning—when did your manufacturing interest first arrive?
Arjun: As a kid, I was always interested in math and science. I had an eye for physics, particularly in high school, which partially informed me of wanting to do mechanical engineering. And as a result, MIT and Caltech were the schools I was most interested in attending when I was considering being an undergrad.
To tell you the truth, I knew very little about manufacturing all the way through my undergraduate course. Mechanical engineering at Caltech was heavily focused on mathematics, so I didn’t necessarily learn the processes behind making things until I applied to grad school and completed the manufacturing program at MIT. This is where my entire perspective changed, and I found my affinity for manufacturing, particularly with the operational and statistical aspects.
Fraser: How did you find your first few years in the manufacturing industry?
Arjun: There was huge pressure to start making some money after my master’s degree—everyone wanted something with a little more stability than could be offered in the startup world, so I turned to the fruits of the corporate tree.
The corporate world experience was a little bit dull for me—it’s not because of anything inherently wrong with the corporate space, just that the jobs there are more specialized. While working at Meggitt certainly taught me firsthand about the difficulties of acquiring and using data, it didn’t accelerate my career in the ways I was looking for.
Whereas I wanted to be able to do different things daily while focusing on making the production process more efficient. This is what’s so appealing about the startup world: There is great variety, and you work with people who don’t necessarily have an extensive academic background but have figured things out through their own intuition.
Fraser: So, having been more interested in the startup world, when did you pivot to the idea of focusing on data and creating IndustrialML?
Arjun: I can’t pretend that I had all the answers at the time, but I always saw data as a precious tool for universalizing language and making human interaction with that data accessible.
These are the two fundamental cores of IndustrialML to this day, and so when I met Shin Nakamura, President of One to ONE Holdings, for the first time in 2018 and pitched the idea to him, it was very straightforward. We just wanted to build something where we could harness data and communicate it to people who can use it to significantly impact operations in real time.
Fraser: How has your focus changed over the last few years?
Arjun: I think in my mid to late twenties, I fell into the trap of riding the wave of AI because of the media perception that it represented the entire future. I somewhat neglected the value of the human interaction side. This meant that in the early days, we saw ourselves as data integrators that subsequently used machine learning to provide more advanced insights from the factories.
However, via our interactions with customers and mounting evidence from Deloitte and other studies, it’s become clear that the human side of retaining and developing manufacturers of the future is vital.
Fraser: Would you say developing talent through IndustrialML technology is where your true passion lies?
Arjun: Absolutely, I’m far more excited about the prospect of taking a new recruit, regardless of their background, and helping them quickly adapt rather than finding an algorithm that will perhaps get a 1% improvement in operations.
I think this is just a far better service to the world, and it means companies don’t have to pay ever-increasing salaries to a dwindling pool of workers. Instead, they can help develop a workforce of adaptable and highly skilled workers.
Fraser: And following on from that, what do you think needs to change in the industry moving forward?
Arjun: The concept of loyalty to a company and expecting reciprocating loyalty as an employee has always been a myth, and it has been effectively shattered. We need to wake up to the reality that creating a positive work experience that is mutually beneficial to both the employee and the company means something different than it did in the 1970s.
Fraser: Lastly, are there any trends you’ve seen—from even rivals—in the industry that you think are interesting?
A few years back, some companies started employing a concept called reverse innovation for their product development. For example, in the mid 2000s, GE Healthcare was looking to break into the Chinese market with an EKG machine, but the versions they developed for the US and European markets were significantly more expensive.
So they redesigned their system to be about 50% of the speed but 15% of the cost, and it sold well in the Chinese market. Then they realized they could go back into the US and European markets with the lower cost option.
I think this almost egalitarian concept can allow manufacturers to affect a broader swath of the market, and it is starting to come into play more and more nowadays. This change in approach can enable manufacturers not to see somewhere which is perhaps less economically developed as a hindrance but rather a relishing the unique set of constraints and challenges.
Fraser: Thanks so much for your time and insights, Arjun. I’ll speak to you soon.
Arjun: No problem, bye for now.