When it comes to the artificial intelligence (AI) boom, the textile industry has not been immune to the technology. Within the sector, AI is being leveraged the most for textile sorting, signifying a shift in how discarded garments and fabrics are managed and sorted.
The clothing industry has a greater focus on sustainability due to the rise of fast fashion models, which generate a large amount of textile waste which ends up in landfills. By leveraging AI, businesses can make textile sorting faster and more sustainable.
What is Textile Sorting?
Textile sorting is the process of separating discarded textiles based on factors like material composition, quality, colour, and condition to facilitate their reuse, repair, or recycling. This process can be manual or automated and involves analysis of garments to determine their end-of-life fate, which garments can be sent to second-hand markets, and which ones can be turned into mechanical or chemical recycling streams to create new products and reduce waste.
In this article, we look at how AI-enabled textile sorting can benefit businesses and the consumers they serve.
How it Works: The Mechanics of AI in Textile Sorting
AI systems in textile sorting use computer vision and machine learning (ML) to see and interpret images and enable systems to learn from extensive datasets of textile images. How it works:
- Image acquisition: Textiles get passed under high-res cameras or scanners to capture detailed images of each item from various angles.
- Feature extraction: AI algorithms analyse the images and extract key features that differentiate textiles according to patterns, colours and levels of damage.
- Material identification: Near-infrared (NIR) spectroscopy is integrated to detect the fibre composition and distinguish between materials even when mixed fabrics are used.
- Automated separation: Robot actuators automatically separate the textiles into specific categories for reuse or recycling.
Benefits of AI-Enabled Textile Sorting
The benefits of AI in textile sorting extend past speed. Some of the other benefits include:
- Fibre-level sorting: AI systems can differentiate complex fibres, which can be difficult to do manually. This capability helps improve the quality of fibres and supports closed-loop recycling systems.
- Reduces landfill waste: Accuracy in sorting ensures more textiles can be reused or recycled, helping capture valuable materials that would be discarded and reducing landfill waste.
- Scalable: Because AI systems can sort large amounts of textiles, they are suitable for large-scale recycling operations and businesses looking to scale.
- Labour efficiency: AI systems can decrease the reliance on manual sorting, leading to lower labour costs and minimised human error.
The Disadvantages of AI in Supply Chains
While integrating AI can offer businesses various benefits, understanding its downsides is just as important. Here are some of the disadvantages of AI in supply chains like textile sorting.
1. High initial costs and investment
One of the biggest barriers for small to medium-sized enterprises (SMEs) when it comes to AI integration is the initial costs that come with acquiring and leveraging the technology. While the technology has its long-term benefits, the process of integrating AI into existing supply chains can be time-consuming, leaving businesses with lengthy timelines for installation and integration.
Solution: Before committing to any AI solution, you need to conduct a comprehensive cost-benefit analysis. Also consider starting small by integrating AI into specific parts of your supply chain. Lastly, explore cloud-based solutions as they are typically less costly and easy to scale.
2. Complexity of AI systems
AI is an advanced technology and can introduce a level of complexity that is a challenge for SMEs, especially during the implementation stage. AI technology involves specific steps and can require extensive customisation to align with the unique needs of the business and still work with current systems.
Solution: Before you launch AI into your supply chain, conduct comprehensive planning and research. This means understanding what your business needs, identifying key objectives and assessing potential challenges. Also ensure that the AI tech you choose has a user-friendly interface to reduce any adoption challenges for your employees.
3. Job losses for employees
As AI continues to be integrated into supply chains, there is a great concern that it could lead to job disruptions. While the technology can automate mundane tasks and optimise processes, it brings a lot of job insecurity even in areas where it is supposed to assist employees.
Solution: One way to avoid this problem is to invest in employee upskilling and reskilling. By training your employees in new technologies, data analytics, AI system management and cloud-based software, you can enable a smooth adoption period for your employees. It’s also important to communicate clearly with your employees when integrating AI to reduce any resistance and build trust.
4. Ethics in AI use
As AI technology is being integrated into supply chains, it’s important to understand ethical concerns and biases that may come with it. An example of this is if a company is using AI, and it makes complex decisions, it might be difficult to understand how it makes decisions. Lack of transparency can create ethical concerns around accountability and decision-making for things such as pricing, vendor selection, or employee evaluations.
Solution: You need to use tools that provide clear insights into how algorithms reach their conclusions. It’s also important that you develop clear data privacy and security policies that provide guidelines on how data is collected, stored and used in your AI systems.
As the world continues to leverage AI technology, it’s important that business owners understand the impact the technology has on their businesses and employees. Using AI in textile sorting has its advantages, especially in sustainability. However, it’s important that you understand the AI system you choose and ensure that you use it ethically.
