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T.EVO: Automatic Choice


T.EVO: Automatic Choice

A year on from when T.EVO first highlighted automated worklines in the sewn goods sector, we revisit Softwear Automation Inc. (SAI), a machinery and robotics start up that has just landed one of its biggest orders to date. Tony Whitfield and Chris Remington caught up with key figures at the company to find out more about the progress being made.


“This industry is very cost-focused,” Santora says. “However, the main issue it is facing right now is access to labour – there aren’t enough seamstresses available in the world and there will only be less as the population increases and clothing needs increase. Given that the average age of the seamstresses is 56 years old and kids aren’t even talking about it as a career move, manufacturers can’t easily fill the gaps. So, the first thing we’re solving is access to capability – the ability to not have to chase the needle around the world, (the needle being the seamstress). It means that cheap labour, wherever that exists, is now not the only strategy available.

“The second thing we solve is related to cost: can we provide this at an ROI that makes sense? And the answer is yes. We think the cost of our T-shirt line at 33 cents per T-shirt will be competitive in most places around the world. In places like Bangladesh, it’s still going to be considerably cheaper at around 11 cents per T-shirt, but we are going to be at least on par with China.”

The Sewbot component list includes: cameras and sensors, the sewing machine itself, and robotic hardware which enables the manipulation of materials as they distort – this allows the robot to continue to sew the product without interruption. Additionally, SAI says the control system and electronics make it possible for the system to work with a series of potentially different operations for machines. These include a variety of vacuum devices which manoeuvre the fabric pieces into place for the sewing machine. The technology uses the cameras to ensure the sewing happens according to the style type.

“The biggest complexity in this whole industry is the level of variation and the flexibility that you need to have in order to be able to work across a series of goods,” says Santora. “If everything was mass produced, it would still be difficult, but you’d only be dealing with the variation of material. When you look at automation, the reason technologies have failed in the past is because nothing really dealt with the fabric distortion. In the past, the only way to figure something out was with complex hardware systems and then as soon as you change the operation or the style or the fabric, you’d then have to change the hardware because they were based around that particular operation.

“The robot is learning constantly – like a child – you can teach it one skill and then several skills to keep adding to that skill-range. We started in home goods, where in one operation you can have a finished good. Then we’ve gone into more complex home goods; pillows and a number of other products that have five operations. Once you start going into apparel, you start looking at things like T-shirts on 10 operations, jeans at 30 operations, and then dress shirts at 70, so you have this scale and we’re really just building the capabilities of the robot.”


Read the full article and Q&A here.

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