• Written by Muhammed Abdulla NC, CEO

The textiles and apparel industry is known for its complex supply chains and long lead times, which can make it challenging to implement new technologies like AI. However, as the US Census Bureau's Business Trends and Outlook Survey shows, 12.4% of US manufacturers, the Census sector that includes textile and apparel makers, reported using AI in the previous two weeks, a figure that indicates AI has started to make inroads into the industry. One place where AI has genuinely changed day-to-day work is in the office, where functions like sales and marketing have seen significant benefits from AI adoption.

Where AI has actually landed in textiles and apparel so far

AI has largely been adopted in functions whose output is text, such as sales and marketing, where 14.3% of US businesses used AI in the previous six months, according to the US Census Bureau's Business Trends and Outlook Survey. This is because text-based functions are more easily automated and can be improved with AI-powered tools. For example, a category manager can use AI to analyze sales data and make predictions about future trends, allowing them to make more informed decisions about product ranges.

In contrast, functions that handle physical work, such as production and distribution, have seen less AI adoption, with only 1.7% of US businesses using AI in production of goods, and 0.9% in distribution, as reported by the US Census Bureau's Business Trends and Outlook Survey. This is because these functions often require more complex and nuanced automation, which can be harder to implement.

Range planning under uncertainty

Range planning is a critical function in textiles and apparel, where businesses need to plan and predict sales and production for upcoming seasons. AI can help with this by allowing users to ask direct questions of the data in ERP, PLM, production planning, and merchandising tools, rather than waiting for a report. For example, a product manager can ask the system how many units of a particular product were sold in the previous season, and what the current inventory levels are, allowing them to make more informed decisions about production and inventory management.

This can be particularly useful in an industry with long lead times, where small changes in demand can have significant effects on production and inventory. By being able to ask questions and get answers in real-time, businesses can respond more quickly to changes in demand and make more informed decisions about production and inventory management.

Supplier fragmentation

Supplier fragmentation is a common problem in textiles and apparel, where businesses often work with multiple suppliers and need to manage complex supply chains. AI can help with this by providing answers to questions about supplier performance and inventory levels, allowing businesses to make more informed decisions about which suppliers to use and how to manage their inventory.

For example, a sourcing manager can ask the system which suppliers have the best lead times and pricing for a particular product, and what the current inventory levels are for that product, allowing them to make more informed decisions about which suppliers to use and how to manage their inventory. This can help to reduce the complexity and fragmentation of supply chains, and improve overall efficiency and effectiveness.

Markdown exposure

Markdown exposure is a critical issue in textiles and apparel, where businesses need to manage the risk of excess inventory and markdowns. AI can help with this by providing answers to questions about sales trends and inventory levels, allowing businesses to make more informed decisions about pricing and inventory management.

However, this is harder than it looks, and requires a significant amount of data and analytics infrastructure to be in place before AI can be effective. For example, businesses need to have accurate and up-to-date data on sales and inventory levels, as well as advanced analytics capabilities to analyze and interpret that data. Without this infrastructure in place, AI can actually add noise and complexity to the decision-making process, rather than improving it.

Why most adopters have not changed anything underneath

Despite the potential benefits of AI, many businesses have not made significant changes to their underlying systems and processes to take advantage of it. According to the US Census Bureau's Business Trends and Outlook Survey, 64.3% of US businesses that have adopted AI made no changes to their underlying systems or processes, and only 6.8% changed how they collect or manage data. This can limit the benefits of AI, as businesses are not able to take full advantage of its capabilities.

This is because implementing AI requires significant changes to underlying systems and processes, which can be time-consuming and expensive. However, without these changes, businesses are not able to realize the full potential of AI, and may actually see limited or no benefits from its adoption. To get the most out of AI, businesses need to be willing to make significant investments in their underlying systems and processes, and to change how they collect and manage data.

What a sensible first quarter looks like for a textiles and apparel business

A sensible first quarter for a textiles and apparel business looking to adopt AI might involve focusing on a single question, such as "what are our current inventory levels for a particular product?", and a single system, such as the ERP system. This allows businesses to start small and focus on a specific area where AI can add value, rather than trying to implement it across the entire organization at once.

By starting small and focusing on a specific area, businesses can begin to see the benefits of AI and build momentum for further adoption. They can also work with a single team, such as the product management team, to implement and test AI-powered tools, and to develop a deeper understanding of how AI can be used to improve decision-making and drive business outcomes. For more information on how to get started with AI, contact us to discuss your specific needs and goals.

What this does not do, or when it is the wrong choice

While AI has the potential to bring significant benefits to textiles and apparel businesses, it is not a silver bullet, and there are certain areas where it may not be the best choice. For example, AI is not well-suited to tasks that require a high degree of creativity or human judgment, such as designing new products or negotiating with suppliers. In these areas, human expertise and decision-making are still essential, and AI may actually add complexity and noise to the decision-making process.

Additionally, AI is not a replacement for underlying systems and processes, but rather a tool that can be used to improve and augment them. Businesses that are looking to adopt AI need to have a clear understanding of their current systems and processes, and to be willing to make significant investments in their underlying infrastructure in order to realize the full potential of AI. For more information on the current state of AI adoption in US businesses, see our analysis of the US Census Bureau's Business Trends and Outlook Survey, or visit our production planning page to learn more about how AI can be used to improve production planning and inventory management. You can also read our blog post on AI and IT investment in textiles and apparel to learn more about the benefits and challenges of AI adoption in the industry.