• Written by Sonali Subhadarsini, Product Manager

A category manager for a large retailer is likely to have spent a significant portion of their week reviewing the upcoming seasonal range, trying to balance the need for new and exciting products with the uncertainty of future demand, all while working within the constraints of long lead times from suppliers. This process, known as range planning, is a critical component of the textiles and apparel industry, and one that can greatly benefit from AI process automation.

Challenges unique to textiles and apparel

The textiles and apparel industry presents a number of challenges that make process automation more difficult, particularly when it comes to range planning. One of the main issues is the uncertainty of future demand, which can make it difficult to determine which products to include in the range and in what quantities. Additionally, the industry is characterised by a large number of suppliers, many of whom are based in different countries, which can lead to fragmentation and communication difficulties. Finally, the risk of markdown exposure is high, as unsold stock can quickly become obsolete and require significant discounts to clear.

These challenges can be exacerbated by the long lead times involved in producing textiles and apparel, which can make it difficult to respond quickly to changes in demand. As a result, category managers and their teams must be able to plan and forecast with a high degree of accuracy, which can be a complex and time-consuming process. By automating some of the steps involved in this process, AI can help to reduce the risk of errors and improve the overall efficiency of the range planning process.

Mapping the current process

Before automating any part of the range planning process, it is essential to map out the current process in detail. This involves identifying all of the steps involved, from initial planning and forecasting to finalising the range and placing orders with suppliers. It also requires an understanding of the different roles and responsibilities involved, including those of the category manager, the shift supervisor, and the supply chain team. By taking the time to map out the current process, organisations can identify areas where automation can add the most value and ensure that any changes made are targeted and effective.

This process of mapping can be time-consuming, but it is a critical step in ensuring that any automation efforts are successful. It requires a detailed understanding of the current process, including all of the handoffs and exceptions that can occur. By taking a thorough and systematic approach to mapping the process, organisations can ensure that they are well-placed to take advantage of the benefits of AI process automation.

Handoffs and delays

One of the main sources of delay in the range planning process is the handoffs that occur between different teams and individuals. For example, the category manager may need to review and approve the initial range plan before it is passed to the supply chain team for implementation. Similarly, the shift supervisor may need to review and approve the production schedule before it is implemented on the factory floor. These handoffs can be a major source of delay, particularly if there are misunderstandings or miscommunications between teams.

By automating some of the steps involved in these handoffs, organisations can help to reduce the risk of delays and improve the overall efficiency of the range planning process. For example, AI can be used to automatically generate reports and updates, reducing the need for manual intervention and minimising the risk of errors. To find out more about how AI can be used to improve the range planning process, visit our website.

Exception handling

Exception handling is a critical component of the range planning process, as it involves dealing with any unexpected issues or problems that may arise. This can include everything from changes in demand to supply chain disruptions, and requires a high degree of flexibility and responsiveness. By automating some of the steps involved in exception handling, organisations can help to reduce the risk of delays and improve the overall efficiency of the range planning process.

For example, AI can be used to automatically identify and flag any potential exceptions, such as changes in demand or supply chain disruptions. This can help to ensure that the right people are notified and that action is taken quickly to mitigate any potential issues. Additionally, AI can be used to provide recommendations and suggestions for how to handle exceptions, based on historical data and industry best practices. To get in touch with our team and discuss how AI can be used to improve exception handling in your organisation, visit our contact page.

Limitations of AI process automation

While AI process automation can be a powerful tool for improving the efficiency and effectiveness of the range planning process, it is not a panacea. There are certain situations in which AI may not be the best solution, such as when the process is highly complex or nuanced, or when there is a high degree of uncertainty or variability involved. In these situations, a more traditional approach may be more effective, at least until the process can be simplified or streamlined.

Additionally, AI process automation requires a significant amount of data and expertise to be effective, which can be a barrier for some organisations. It also requires a high degree of trust and confidence in the technology, which can take time to build. As a result, organisations should carefully consider their options and choose the approach that best fits their needs and circumstances. By taking a thoughtful and considered approach, organisations can ensure that they are getting the most out of AI process automation and achieving the best possible results.