Research being carried out by a research team around Professor Ohbyung Kwon at Kyung Hee University and Dr Christine (Eunyoung) Sung at Jake Jabs College of Business and Entrepreneurship, Montana State University, involves examining consumers' evaluations of fashion products designed using generative adversarial networks (GANs), an Artificial Intelligence (AI) technology. They analyse consumers' buying behaviour and offer practical advice for businesses that are considering using GANs to develop products for the retail fashion market. Artificial Intelligence (AI) technology is changing the retail landscape. Generative AI is being used to produce creative outputs; tasks that have traditionally been considered exclusive to humans. In particular, generative adversarial networks (GANs), an Artificial Intelligence technology, powerful machine learning models that can generate realistic images, videos, and voice outputs, are successfully performing creative tasks previously considered unique to humans.
Consumer fashion may be among the most unpredictable markets on the planet, but one startup in India has created an AI-based demand sensing platform that combines the brilliance of data scientists with seasoned industry experts to ferret out trends with uncanny accuracy. The idea is to close the gap between supply and demand. Omni-channel retailers are using AI to synch design and merchandising decisions with breaking consumer demand trends for sustainable growth. "We help companies create demand-driven fashion forecasts from consumer data across a holistic value chain," Ganesh Subramanian, founder and CEO at Stylumia. "Our demand sensing engine collects and analyzes publicly available global data to rank product trends, providing fashion designers, retail buyers, and merchandisers with a much deeper understanding of real-time consumer demand signals."
I remember coming across a beautiful poster, folded and inserted inside a National Geographic magazine. It was titled "Hidden Water," and contained various measurements of how much water it takes to produce different things. The poster said that it requires 2,900 gallons of water to make one pair of blue jeans and estimated the water cost of one cotton t-shirt at 766 gallons. It was my first exposure to "the truth" behind the fashion industry. The second time came when I watched "The True Cost."
However, designers and brands that embrace the latest technology to push the limits of design, manufacturing, and production will come out on top in the fast-changing world of fashion. Here are a few of the biggest tech trends that are transforming the fashion industry. Fashion brands are reshaping their approach to product design and development by predicting what customers will want to wear next. Trend forecasting is typically labor-intensive, involving manual or digital observation and data collection from fashion designers and influencers. By harnessing data directly from users, brands like Finery and StitchFix can have fast, easy access to information that helps them plan the styles people will love and know what quantities to manufacture.
Millenials or the GenYs and the GenZs (1980s to the present) may no longer have an inkling about the dawn milkman on horse/water buffalo carriages/carts or motorised milk floats. Those from or who have been in the Philippines may no longer connect with the provincial folk travelling around neighbourhoods and even as far as cities selling nipa/bamboo day-to-day furniture, decor and ware on board "carabao/cow-powered" covered rickety wagons. These were well-remembered on Wednesday afternoon when Dubai-based fashion entrepreneur Fareda Ali spoke with Gulf Today about the "EOO8" concept-cum-invention among other Artificial Intelligence (AI)-driven creations introduced to the media and fashion aficionadoes at the D3 or Dubai Design District. Ali is a UAE-born Sudanese-Canadian fascinated since childhood by the feel, silhouette, and sight of all fabrics, colours and designs elegantly to outlandishly laced. The fascination and passion led her to earn a degree in Fashion Design and Marketing from the Esmod Institute in Dubai two years back.
Fashion is one of the biggest industries globally, and revenues in the global apparel market are expected to reach $2.25 trillion by 2025 – but it's not the first industry that probably pops into your mind when you think about artificial intelligence. However, designers and brands that embrace the latest technology to push the limits of design, manufacturing, and production will come out on top in the fast-changing world of fashion. Here are a few of the biggest tech trends that are transforming the fashion industry. Fashion brands are reshaping their approach to product design and development by predicting what customers will want to wear next. Trend forecasting is typically labor-intensive, involving manual or digital observation and data collection from fashion designers and influencers.
AI (Artificial Intelligence) seems to be the next big thing in many industries today. On Gartner's 2020 Hype Cycle of Emerging Technologies, for example, we find no less than seven explicitly AI-related trends in the first steep curve of inflated expectations--such as composite AI, generative AI, responsible AI, embedded AI, and explainable AI. For a term that dates back to 1956 and celebrates its 65th birthday this year, this seems remarkable, especially since the productive application of the currently hyped AI variations is expected to take another two to ten years. In this arena of promising AI technologies, the Dutch AI-based startup Lalaland is an interesting case. They have found a way to make AI work in a way that is both tangible and speaks to the imagination. Using AI technology, they are one of the front-runners that may change the online fashion industry and, arguably, make it more inclusive, sustainable, and profitable, thereby speaking to all three P's of the Triple Bottom Line.
Textile manufacturing is a typical traditional industry involving high complexity in interconnected processes with limited capacity on the application of modern technologies. Decision-making in this domain generally takes multiple criteria into consideration, which usually arouses more complexity. To address this issue, the present paper proposes a decision support system that combines the intelligent data-based random forest (RF) models and a human knowledge based analytical hierarchical process (AHP) multi-criteria structure in accordance to the objective and the subjective factors of the textile manufacturing process. More importantly, the textile manufacturing process is described as the Markov decision process (MDP) paradigm, and a deep reinforcement learning scheme, the Deep Q-networks (DQN), is employed to optimize it. The effectiveness of this system has been validated in a case study of optimizing a textile ozonation process, showing that it can better master the challenging decision-making tasks in textile manufacturing processes.
Multi-objective optimization of the textile manufacturing process is an increasing challenge because of the growing complexity involved in the development of the textile industry. The use of intelligent techniques has been often discussed in this domain, although a significant improvement from certain successful applications has been reported, the traditional methods failed to work with high-as well as human intervention. Upon which, this paper proposed a multi-agent reinforcement learning (MARL) framework to transform the optimization process into a stochastic game and introduced the deep Q-networks algorithm to train the multiple agents. A utilitarian selection mechanism was employed in the stochastic game, which (-greedy policy) in each state to avoid the interruption of multiple equilibria and achieve the correlated equilibrium optimal solutions of the optimizing process. The case study result reflects that the proposed MARL system is possible to achieve the optimal solutions for the textile ozonation process and it performs better than the traditional approaches.
In today's world of fast fashion, retailers sell only a fraction of their inventory, and consumers keep their clothes for about half as long as they did 15 years ago. As a result, the clothing industry has become associated with swelling greenhouse gas emissions and wasteful practices. The startup Armoire is addressing these issues with a clothing rental service designed to increase the utilization of clothes and save customers time. The service is based on machine-learning algorithms that use feedback from users to make better predictions about what they'll wear. Customers pay a flat monthly price to get access to a range of high-end styles.