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The Future of Fashion: AI, QR Labels, and Smart Manufacturing ~ oh my!


As technology continues to advance faster than we can adapt alongside, the fashion industry will inevitably change with it. With the rise of Artificial Intelligence (AI), QR labels, and Smart Manufacturing, how we produce and consume Fashion is rapidly evolving. In this article, we will explore and unpack the implications of these advancements for the future of Fashion, both as an industry and an art form.

Let's start with a glossary to ensure the concepts we discuss don't end up in a confusing cloud of unknown tech terminology/applications:

Tech Terms you should know ~

+ Artificial intelligence is machines' ability to perform tasks typically associated with human intelligence, such as learning and problem-solving.

+ Generative AI is artificial intelligence capable of generating text, images, or other media using generative models. Generative AI models learn the patterns and structure of their input training data and then generate new data with similar characteristics. (Examples: DALL-E, Midjourney, and Stable Diffusion producing images, or ChatGPT that makes text).

+ ChatGPT (Chat Generative Pre-trained Transformer) is a type of Generative AI, a significant language model-based chatbot, which uses AI for tasks such as writing, changing the tone of texts, or translation. It is fed openly available information to answer a wide range of requests.

+ Industry 4.0 is a term used for the "fourth industrial revolution," describing the application in supply chains of rapid technological advancement in the 21st century. It is very similar to the concept of Smart manufacturing. AI is one of these advancements, together with cloud computing analytics and IoT, revolutionizing how companies manufacture, improve and distribute their products. Smart factories are equipped with advanced sensors, embedded software and robotics that collect and analyze data and allow for better decision-making. Some factories are so automated that they run 24h and are entirely dark because only robots operate in them! This is called Lights Out Manufacturing.

+ The Internet of Things (IoT) is the interconnection via the internet of computing devices embedded in everyday objects, enabling them to send and receive data. To put this in simpler words, it is the automated sharing of data between machinery that automatically adapts its functions based on data that is fed to it. Imagine a dyeing machine that adapts the type of dye used because it is automatically connected to a camera analyzing the exact colour of the cotton fed to it, which can vary from being a natural product. Another example can be data from stores that can directly speak to warehouses and place orders on what sizes need to be filled in again.

+ Automation is the use or introduction of automatic equipment in manufacturing. This can include a machine to stitch the same pocket over and over. However, it doesn't mean this machine is connected to other devices (IoT) or can automatically read data and adapt its stitches (AI).

Fashion Communication

QR codes are becoming increasingly popular in the fashion industry, but they are much simpler than AI, as they are a way for links to be stored in an image so that a camera can directly bring to a URL. These labels can give customers information about the garment, such as its origin, materials, and care instructions. This is very important for sustainability, to educate the consumer on how a garment is made, allowing them to track the garment throughout the supply chain, ensuring that it is produced ethically and sustainably. If consumers knew how much work goes into a garment, they would take care of it more carefully and reduce the rate at which cheaper clothes are made.

This technology can also reduce waste by allowing customers to resell or recycle garments more efficiently, with information on where they can discard their clothes. Luxury brands also use such systems to prove that their garments are from the original brand, not copies, so they can be valued accordingly when resold.

A new trend is rising around immersive shopping experiences. By scanning a QR code, customers can access additional product information, such as reviews, styling tips, and recommendations. Would you be interested in knowing more about your clothes, how to match them, and how to recycle them later on?


Forecasting and Supply Chain Management

We asked Chat GPT to write a short paragraph about the link between AI and Forecasting, and this is what came out, pretty accurate right?

You may have noticed that the writing style is quite mechanical, with very short sentences and a long list of concepts. However, it is accurate and a great source of inspiration if you are stuck. But we still have more to add!

AI can also improve the accuracy of demand forecasting, which can help reduce overproduction and waste. By analyzing historical sales data and customer behavior, such as different sales trends in different locations, AI algorithms can predict future demand and help companies make better production decisions.

AI can also help streamline the production process by identifying and addressing bottlenecks in the supply chain. By optimizing schedules, lead times can be reduced, and the speed at which products are delivered to customers improved.

Smart factories can monitor inventory levels, track the progress of garments as they move through the production line, and adjust production schedules in real time. This technology increases efficiency, reduces waste, and improves working conditions for factory workers.


AI-generated chatbots also serve as stylists, creating curated shopping experiences for online shoppers. SSENSE launched their own chatbot using ChatGPT and proprietary algorithms, which can be used as a plug-in on Google to answer styling questions from customers and provide direct product links to curated items. Other online retailers like Zalando are integrating this service to provide a more conversational, personalized experience that mimics sales associates.

Generative A.I / Generative Creativity

One primary application of AI is in the design process, as it can generate images when given a range of inputs. One example is the creation of prints that can be used directly or creating patterns from a drawing.

According to McKinsey's analysis, generative AI could add $150 billion conservatively and up to $275 billion in the apparel, Fashion, and luxury sectors' operating profits in the next three to five years. From co-designing to speeding content development processes, generative AI could create new space for creativity.

What will that creativity look like? Looking to the future, many creatives are wondering what their jobs will look like as AI becomes more integrated with Fashion. AI is already changing how photographers and creative directors exercise their creativity through fashion imagery and design. Designer Carl-Axel Wahlström created Copy Magazine, an AI-generated fashion magazine using generated imagery – eliminating the need for all people involved in that creative process to produce the final image.

Brands like Casablanca use MidJourney to generate imagery in a retro-futurist style for their entire S/S 23 collection. Upon first looking at the final images, they feel intentionally designed and high quality, removing the human quality of Fashion. Other brands have followed suit, using Generative AI to develop novel fashion design concepts.

We understand that there is fear around AI and how it will change many positions; however, we believe creative humans will always play a key role in designing clothing and setting trends. Designers are using AI to get inspiration in the same way they are using tools such as PowerPoint or Canva, which seemed extremely scary before they became widely adopted. When Excel was invented, there was a lot of fear around the role of accountants and engineers, which, however, remained very relevant and benefitted from this tool.

While AI is moving the industry in an exciting direction, it poses an existential question of humans' role in the creation process of Fashion. Many people have reverted to slow, traditional technologies in response to AI, while others are readily experimenting. Here’s a great talk on AI in Fashion that dives into the ethics, extensionalism, and limitations.

The future of Fashion will depend on how ready or resistant the industry is to AI. If we can balance our respect for human creativity and textile knowledge with AI's potential for improving the industry and generating novel ideas. In that case, AI can be used as a tool of good in Fashion.



Smart manufacturing can improve product quality by reducing the likelihood of human error. By automating specific production processes, smart factories can ensure that products are consistently produced to a high standard. Smart factories can also help reduce the environmental impact of fashion production by using energy and resources more efficiently, for example, by ensuring that the optimal amount of air conditioning is used based on external temperature, humidity, and machinery running in the factory.

3D weaving is not an AI concept in itself. 2D weaving consists of warp and weft yarns, whereas there is another dimension in 3D weaving. With the help of it, complex preforms can be produced to reduce the material cost and handling time and achieve better mechanical properties. AI can be used to turn images or concepts into programs to create this 3D structure and feed this data directly to machines. The same thinking can be applied to 3D.


As you read this, thousands of trucks, planes, and ships are going back and forth from warehouses, factories, stores, intermediaries, and third-party suppliers, bringing materials, products, trims, and packaging worldwide. Optimization of logistics can ensure that loads are maximized and combined to minimize carbon emissions. Of course, reducing the production volume and the need for logistics would be the best way to reduce its environmental impact; however, there will always be some logistics, and AI can improve its efficiency.

Ideally, a centralized system would receive all the volume, locations, and timeline requirements for all shipments and know all the types of fuels required for transportation modes. The best solution based on all parameters to have the least environmental impact would then be automatically calculated. This is already done to some degree, but more automation could bring this to a larger scale and precision.

Waste Sorting

Solutions for recycling clothes are increasing, for example, mechanically recycling cotton or chemically recycling all natural fibres by extracting their cellulose. A significant issue, however, is that most solutions can only apply to some types of fibres. The difference between 2% and 3% polyester in jeans can determine whether they can be recycled; therefore, meticulous sorting is required beforehand.

Sorting can be very time-consuming, and currently, virgin fibres remain mass-produced and very cheap, so sorting isn't profitable. Automatic sorting using AI to detect the fibre content, chemical content, and recycling possibilities could reduce the cost of sorting, improve its precision, and allow for more recycling.

Refibred is a start-up using AI to commercialize the textile recycling process. Their system collects and processes information fed into a machine-learning model to determine the composition of the textile and its recycling. Unlike other sorting solutions, Refibred's technology reaches a fidelity of material analysis, which enables cleaner sorting and accurate textile-to-textile recycling.

We dream of a small AI robot going through landfills and picking out clothes into different bins based on their material content and how they can be recycled. Hopefully, blended fibres and additions like buttons, seams, etc., that are hand processes can be sorted and properly recycled to avoid excess textile waste. Clothing and textiles currently comprise at least 7% of the total waste in global landfill space, so scaling textile solutions like this could be revolutionary!

New Business Models

Clothes swapping, renting, take-backs and repairs, vintage shopping, are all solutions that can allow a degrowth in Fashion production volume while ensuring a profit increase that will enable companies to exist in the current system of capitalism. You can read all about this in our Degrowth article. We are excited to see how AI can support the vast work required to shift towards these new models.


AI, QR labels, and smart manufacturing implications for the fashion industry are vast. These technologies are and will keep revolutionizing how we produce and consume Fashion, making it more sustainable, efficient, and customer-centric. We can imagine a range of improvements in waste sorting, waste reduction, and circular models for sharing clothes.

If these technologies are used correctly, rather than just to increase consumption, reduce prices, and sell more -- fashion companies can improve their bottom line and ensure that investors stay happy while reducing their environmental impact and improving the customer experience. As we continue to push the boundaries of what is possible, it is clear that the future of Fashion is bright and full of possibilities.

Until next time, stay diligent x


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