Automation has long been a struggle for the clothing industry.
Attempts to automate sewing have tried to counter the complexities of limp textiles with equality complicated technologies. The proof-of-concept for a new technology produced the world’s first robotically-sewn garment – a simple T-shirt, but an important milestone for the industry.
The apparel assembly process has been basically unchanged since the invention of the sewing machine and the $1 trillion industry is still totally dependent on manual labor, leading to countless headaches across the supply chain.
Sewbo, an early-stage startup developing industrial automation tools for clothing factories has developed one of the first robots to sew an entire piece of clothing.
Automation has failed to find a place in garment factories due to a robot’s difficulty handling limp textiles. Sewbo’s process avoids these issues by treating the fabrics with a water-soluble thermoplastic, turning the formerly flexible fabrics into rigid composite materials.
It has developed a robot that can assemble a T-shirt by simply stiffening the fabric so that it’s more like a piece of cardboard. The arm then picks up the pre-cut pieces using suction and feeds them into a sewing machine. When its finished making the shirt, the bot simply drops it into hot water to remove the non-toxic polymer stiffener.
The Polyvinyl Alcohol plastic stiffener is already used in the garment industry and can be recovered and reused. As Sewbo points out, machines already cut and measure fabric, but can’t handle soft materials like cloth with much dexterity. With stiff fabrics, a bot could take the already-cut pieces and assemble them like sheet metal.
Sewbo’s approach is novel, but it’s not the first company to dream up an automated sewing system. Electroloom, for one, wants to “3D print” garments. However, it’s hard to see the benefit of automation when you can already buy a T-shirt for $5. And if such inventions did work, it could throw millions of garment industry workers, most of whom are women, out of work. However, Adidas is already using robots to build shoes, and so automated clothing production seems inevitable, too.
Watch: Sebow’s Sewing Robot Changes How Clothes Are Made http://engt.co/2coWoqL
Changing the Problem
In the early days of NASA’s attempt to explore space they faced a seemingly impossible problem.
How could they make a material that could stand up to the heat generated by the re-entry of a spacecraft? They had to find a material that would not melt. Countless trials ended in failure because the heat was so intense.
They solved that problem by focusing on finding a material that would melt. The solution was to add a shell that would melt off as the craft re-entered the earth’s atmosphere.
If you can’t find a solution, maybe you need to change the problem.
In the old world of work, the solution was to find a job where you could develop a career and work your way into management. You expected an average annual salary increase of 4 – 5% and earned accumulating weeks of vacation based on your years of service to a company.
In the new status quo, multiple employers are becoming the norm and the new solution is to develop several streams of income, ideally with one involving a residual income.
Your problem is NOT to get a job, it’s to DO a job for multiple customers (employers).
Your NEW challenge (problem) is to get multiple employers (customers) to provide you a diversified stream of income based on your delivery of solutions that help them make money, save money or solve a problem.
Start using this mindset and your job search will take a different perspective and offer you far more employment security than most people have ever had. Employment security used to come from a company, now it needs to come from YOU and the talent you offer.
Now more than ever, you need to focus on what you’re doing AND where you are going.
Decide what you want and then build a bridge to get it.
Your solution is to change the problem and come up with a new solution. Changing the problem may be as simple as reframing how you see the problem.