Just as there’s a storyteller behind every novel, every car on the road is made possible by the manufacturers of steel and aluminium. But not every manufacturer is created equal…

Led by Arun Thandapani, Hirschvogel is a pioneering steel and aluminium part supplier that’s changing the face of the sector as we know it. Arun and his team are taking the technological side of manufacturing to the next level, leveraging the manpower-freeing potential of automation to meet evolving industry demand.

In this eye-opening interview, Arun reveals how he and his team are going where no metal manufacturer has gone before – connecting data points to reality. By deriving meaningful insights hidden behind data, Arun and his crew are bringing the industry better results than ever before.

Ready to dive into the future of data-driven decision making in the steel and aluminium manufacturing sector? Let’s go!

How did you get involved in the automotive industry?

So, I started out studying mechanical engineering back in South India. In fact, I wanted to be a software engineer, but my father wanted me to be a mechanical engineer. So we struck a deal. I became a mechanical engineer and said, I will learn all the software on the side. This helped me when I decided to come for my masters here in Columbus, Ohio. I came to Ohio State University and pursued my industrial systems engineering, and that’s where I got introduced to manufacturing and especially metal farming forging and stamping technologies. As my first exposure to manufacturing research, I got hooked onto it.

I was able to use all my skill sets when it comes to what I learned in metal farming and also my software skill sets and programming as a research associate. I graduated doing research and stamping. But then I ended up joining Hirschvogel Incorporated here in Columbus, Ohio, as a process engineer.

And that’s my journey into manufacturing, per se. And I spent a lot of time on the floor doing specific process engineering work that gave me the hands on knowledge as to how to deal with process variables, how to handle variation, how to address them, and how to use my research skill sets to optimise the process.

Do you think your mix of Mechanical skills & Software knowledge has given you a competitive advantage?

One of the things that I notice is you usually have an engineer who knows mechanical stuff. They learn about hydraulics, pneumatics, specific servo motors and stuff like that. You learn basic components of a machinery, but then what you do not learn is how to take data from there and actually convert that data into information and insight. And that’s the portion where I feel the engineers who do not have a background in data or data analytics have a difficulty trying to analyse it.

So as an engineer and you’re doing some programming work, you could easily take that up and try to analyse whether it’s a correlation. You can sift through millions of data points and actually find out what are the erroneous ones, and you can remove them and do a lot of data cleaning. And that’s the majority of the time that the engineers will struggle because they see a lot of things, but they don’t know what to do with it. And if you can’t connect the data to the reality, the data is useless. So having this exposure of both working with data analytics and the process mining portion of it, along with the background of what happens, actually physically on the floor, helped me put things together and derive insights. Which was not easy.

Can you give us any examples of transformations that have been made through linking data to the processes happening on the shop floor?

Absolutely, usually data about inventory and so on is inside ERP packages, and it’s in a form where you can’t see it’s in numbers and screens. You have to scroll across a lot of screens to find out what’s happening. And what you’re missing sometimes is, what is our target? Because you don’t have a target inside, you just have no idea what’s going on.

For example, I put this table together and then we were like, okay, at the end of the year, we want to be at $50 million for the year in inventory. And then you see you blew through it because there are chip shortages and stuff like that happening in the industry and then you’re not able to control your inventory. But if you make it visual, people will get it, people see it. And if this data point is just sitting inside ERP and it’s individually managed by different people, and if you don’t give individual targets to them, they just won’t
understand what’s happening and they just don’t know how they can respond to it. That’s the difficulty when they see this. And if you see a full bar and you don’t understand what’s inside it, you got to split up.

Then you can start to ask questions and adjust KPI’s, this is the kind of thing that you can do on a day to day basis to enhance your company and your business as a whole.

So how do you think the manufacturing landscape will change over the next decade? What do you think the big drivers will be?

I think data driven decision making is going to be a commonplace going forward. We are all going to be needed to interpret the results and to ask more knowledgeable questions. Each of the machining modules and every piece of the puzzle in the value string is going to be smart, right? It’s going to put out a lot of data.

And I feel like over a period of time, certain industries will become like a black box almost where certain things are fine tweaked and there is like automated machine learning algorithms or AI engines that are running to interpret and optimise it further.

And then the people on top are going to look at the outcomes or potential choices for those algorithms to take. It could give us some meaningful courses of direction, saying, look, you could imagine almost like Iron Man kind of thing, that’s what’s going to happen, right? Can I change this or can I change that?

I think in 10-15 years from now, the spaces with which innovation is going on. I think it’s quite possible that we could go to a point where AI and ML are becoming the main interface into your manufacturing. Options could be thrown out based out from those AI engines. And the leaders and people would be making decisions from based on that what choices that it throws out.

Do you think that means hiring of people with different skill sets or just training of the people?

I think there’s got to be new engineers coming in. The young talent who come in, would need to know about dealing with data and trying to connect the pieces of the puzzle. You also have a workforce that’s already engaged at work. We need to now teach them, this is interesting stuff. You no longer have to use your muscle for things. Things are going to be automated. You’re going to use your brain for changing things. So, it’s exciting times.

And if we empower people the right way, I think it will be a very interesting journey for everyone involved. That’s where I think it’s going to happen, both sides. For example, we are starting our own apprenticeship programme because we would like to grow our own people. It’s a combination of mechatronics, like mechanical and electronics and robotics.

You will need engineers with all of the skill sets and that doesn’t exist. So we have to create our own. That’ll be a common place going forward. It’s not just us. Probably everyone would jump in.

Tell us about your own personal learning

Right now, there is digital twin technology, like simulation, wherein you could simulate your entire facility scenario.

Discrete event simulation is another topic which could also be interesting for the future. The way I see it is we need to be curious. Use our downtimes to steer energy towards learning. It satisfies your curiosity and keeps you engaged in achieving new things.

Power BI was something that I learned as well as digital discrete event simulation using Simio, during the pandemic. I had four weeks of being stuck at home, so started with learning, adding discrete event simulation to my portfolio. So I rebuilt the entire plant in 3D. You could see objects going around, like toys, moving material, and you could see what’s happening.

Use your time that’s available to learn. Learning is a journey that I feel it will not end whilst I’m alive. And that’s my moral, to keep going and keep myself contributing to the world.

Where can we get in touch with you?

At www.hirschvogel.com you can see more about my company and about myself. You could follow me on Twitter @AThandapani, find me on LinkedIn, Arun Kumar Thandapani.