• Written by Muhammed Abdulla NC, CEO

A merchandising manager checks the sales forecast for the upcoming season, trying to balance the need for new designs with the risk of excess inventory, and wonders how enterprise AI adoption in textiles and apparel can help with range planning under uncertainty. The textiles and apparel industry is notorious for its long lead times and fragmented supply chain, making it difficult to respond to changes in demand. This uncertainty makes it challenging to plan ranges, as suppliers may not be able to adjust production quickly enough to meet changing demand. Furthermore, the industry's reliance on seasonal ranges means that mistakes can be costly, with excess inventory often being sold at a markdown, resulting in significant losses.

Range planning under uncertainty, supplier fragmentation, markdown exposure

The textiles and apparel industry is particularly vulnerable to uncertainty, with fashion trends and consumer preferences changing rapidly. This uncertainty, combined with long lead times, makes it difficult for merchandising managers to plan ranges effectively. Supplier fragmentation is another challenge, with many suppliers producing different components of a product, making it difficult to coordinate production and ensure that all components are available when needed. Markdown exposure is also a significant risk, as excess inventory can result in significant losses if not sold at full price.

Most people get wrong the idea that enterprise AI adoption in textiles and apparel is simply a matter of implementing new technology, without considering the underlying business processes and constraints. However, the reality is that successful adoption requires a deep understanding of the industry's unique challenges and the ability to adapt technology to meet those challenges.

Attaching new capability to a routine that already exists

A shift supervisor checks the maintenance log to ensure that all equipment is functioning properly, and realizes that new technology can be attached to existing routines to improve efficiency. In the textiles and apparel industry, this means integrating enterprise AI adoption into existing range planning and supply chain management processes. By attaching new capability to a routine that already exists, businesses can minimize disruption and ensure that new technology is used effectively. For example, a category manager compares two suppliers using data from AsscherAi, which provides real-time answers to questions about the organisation's own data.

This approach also allows businesses to build on existing knowledge and expertise, rather than trying to replace it with new technology. By leveraging existing routines and processes, businesses can ensure that new technology is used in a way that is consistent with their overall strategy and goals. To learn more about how AsscherAi can help with this, visit our website.

Who needs to be able to ask, not just who needs the answer

A design manager needs to be able to ask questions about sales trends and consumer preferences in order to create effective designs, but often lacks access to the necessary data. In the textiles and apparel industry, it is not just the merchandising manager or category manager who needs access to data and insights, but also designers, suppliers, and other stakeholders. By providing access to data and insights to a wider range of stakeholders, businesses can ensure that everyone has the information they need to make effective decisions.

This requires a cultural shift, as well as changes to existing processes and systems. Businesses need to be willing to empower a wider range of stakeholders with data and insights, and to provide them with the tools and training they need to use that data effectively. To get in touch with our team and discuss how we can help with this, visit our contact page.

The first wrong answer and how trust is rebuilt

A wrong answer from a new system can be damaging to trust, but it is also an opportunity to learn and improve. When a new system provides a wrong answer, it is essential to investigate the cause of the error and to take steps to prevent it from happening again in the future. This may involve re-training the system, or adjusting the data that is being used to inform its decisions.

Trust is rebuilt when businesses are transparent about the limitations of their systems, and when they take steps to address any errors or inaccuracies that occur. By being open and honest about the potential for errors, and by taking a proactive approach to addressing them, businesses can rebuild trust and ensure that their systems are used effectively. This is particularly important in the textiles and apparel industry, where mistakes can be costly and have significant consequences.

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

Enterprise AI adoption in textiles and apparel is not a panacea for all business problems, and there are certain situations where it may not be the best solution. For example, in situations where data is limited or of poor quality, AI systems may not be able to provide accurate insights or predictions. In these situations, other approaches, such as traditional market research or analysis of historical data, may be more effective.

In addition, AI systems are not a replacement for human judgment and expertise, but rather a tool to support and augment it. Businesses need to be careful not to rely too heavily on AI systems, and to ensure that they are used in a way that is consistent with their overall strategy and goals. By understanding the limitations of AI systems, and by using them in a way that is tailored to the specific needs of their business, companies in the textiles and apparel industry can get the most out of enterprise AI adoption.