Technology is slowly making its way into the creative spaces of the fashion industry. A key shift in the industry over the past few years is the method by which the apparel is designed. Rather than creative output, more brands are focusing on developing products using algorithmic outputs. There has even been an influx of brands that consider themselves technology companies as opposed to fashion brands.
Leveraging customer information and preferences to create designs is not a new concept in fashion. Zara has famously delivered a model for years that keeps store managers regularly in conversation with customers and decentralised design teams so that the teams that are producing the clothing know what customers want. Online retailers have now taken this to a new level using AI.
Despite its controversies, SHEIN is considered the most popular brand in the world making double the revenue of some of its closest competitors. The company uses AI to provide manufacturers with real time analytics that are used to maximise inventories. Products are developed in small batches and a trend-prediction model is leveraged to monitor performance in real-time so inventories can be adjusted.
As opposed to a typical design process, SHEIN’s model looks something like this:
Given the social and environmental controversies relating to real-time/fast fashion, by no means should it be the industry standard. However, there are elements of the process such as the ability to create products closer to when they are being sold and test demand via small batch production that are key requirements to make the fashion industry more demand driven.
Considering an estimated 30% of clothing that gets produced is never sold, the ability to create what customers want when they want it will make the industry less wasteful. To do this, the design to production process needs to be digitally connected to create a system that can meet demand while ensuring that the working conditions for the individuals involved in production are safe and fair.
Another interesting application of technology in design processes is the DALL-E machine learning model that was developed by OpenAI. DALL-E allows users to create photo realistics designs from text prompts. The technology is already being used by clothing design and production company, Cala. While the tool does remove some of the traditional artistic requirements to design clothing, it does still require individuals to determine the vision. By removing some of the traditional prerequisite design capabilities, it opens up apparel design to more people and therefore, more opportunities for creativity.
There is a general misconception that technology processes are better suited for programming tasks as opposed to creative tasks. This is actually untrue, since programming tasks have a correct answer, whereas creative tasks do not. The instantaneous iterative abilities of machine learning and artificial intelligence allow creatives to generate and test exponentially more options with significantly less effort, thereby increasing overall creative potential.
The subjective nature of what is right or wrong in design removes the requirement of human expertise for verification and opens up opportunities to anyone who has a keen interest. While there can be a belief that digital design tools like machine learning and artificial intelligence reduce creativity, the reality is that they actually open up a new era of design and creative potential.