The textile and apparel industry needs SMEs to have access to the digital tools that will allow them to compete on a global scale, explains Rafael Cervone, the executive director of Texbrasil, which is the International Program of the Brazilian Fashion Industry maintained by ABIT (Brazil’s textile and apparel industry association).
He continues: “Equally important is addressing the skills gap, which is a critical issue in many industries including ours. AI is driving rapid change but we need to leverage digital technologies with trust in mind.”
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During a session on AI in the textile value chain at the recent joint IAF & ITMF annual conference in Uzbekistan, he tells attendees: “AI has the tremendous ability to revolutionise the value chain from optimised production processes to enhanced customer experiences, but it must be guided by ethics and sustainability.”
Why is the apparel sector losing its skilled workers?
Thomas Gries, a professor and chair of textile engineering at the Institut für Textiltechnik of RWTH Aachen University in Germany argues there is a lack of skilled workers in the apparel industry, particularly in Europe so AI could be a potential solution.
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By GlobalDataHe explains: “In Europe, Baby Boomers will soon retire and the textile and apparel industry has lost its image to biotech and computer science.
“We have to look for skilled workers and I believe automation and robotics has a big opportunity to help with the skilled worker shortage.”
For Gries it’s a question of now or never as he highlights statistics suggest companies that do not automate now will not be competitive by end of the decade (2030).
He works in a big university with a centre of production technology that is strong in AI and ICT, but he admits if you look at the science you can get even more confused.
Instead, he advises the apparel industry to get real-world insights from living labs, such as the ILC (Innovation & Learning Center) in Aachen where you can dive into this world and get an impression of what does and doesn’t work.
“We have collaborative options where companies can come and develop small projects and we have larger projects on how to manage the use of AI, such as WIRKsam,” he says.
Crucially, he believes the need for automation exists in every sector of our industry, but lists AI to help with production as just one example. He also highlights collaboration is important and a knowledge transfer where people are brought in from other industries that are familiar with the use of AI.
He explains: “There are some things that can be done to capture information and share it but you also have to translate that information. There are some first tools available but it’s not enough.”
How can apparel industry executives embrace AI?
Gries’ suggestion to apparel company owners is to actively invest in and adopt AI technologies in the business: “You have to start small but you have to start.”
He adds: “At the end of the day you have to go to the worker level and improve it with AI.”
He’s adamant that a human-centred approach combined with collaboration is the magic formula to AI and you have to innovate within the workforce.
“Be a leader in the workforce and then you’ll attract the next generation of people,” he asserts.
From a skilled worker point of view he points out the sector needs interdisciplinary teams: “We have to speak simply and make drawings instead of words and have an explorative mindset based on wanting to try new things.”
He also shares it’s important to have an open company culture as managers can’t teach AI from the top down. It needs to be experience-based and collaborative.
Is AI the only solution to the apparel worker shortage?
AI is not the only solution, according to Gries and the human element remains critical. He explains: “You have to look at different kinds of mathematics and do something with signal processing.”
He claims the solution to the apparel sector’s worker issues will “definitely be a mixture of different mathematical models” as he believes its big data benefits have been overestimated.
“We have to look at what we can do with big data. The image processing and creation of all the colourful pictures in my presentation were made by AI so this is one area it can be used. There’s also predictions of consumer behaviour and then of course there’s language processing.”
For Gries, AI is very good at providing an initial “picture” but if you want precise data, AI is the wrong tool.
He adds: “AI should be renamed as best guess as it makes a good first guess” but it will be up to skilled workers with AI training to dig into the data and find the complete answers.”
