• Written by Sonali Subhadarsini, Product Manager

A category manager for a fashion brand is tasked with deciding which styles to include in the upcoming seasonal range, a decision that will commit the company to significant production and inventory costs, and must balance the creative vision of the design team with the commercial realities of supplier lead times and markdown exposure, all while ensuring that the chosen styles align with the company's overall brand strategy and values.

The textiles and apparel industry presents a unique set of challenges when it comes to implementing enterprise AI governance, particularly in the areas of range planning, supplier management, and markdown exposure, where the need to make decisions under uncertainty and with long lead times can make it difficult to balance competing priorities and ensure that the chosen approach is aligned with the company's overall goals and values.

For example, a shift supervisor at a manufacturing plant may need to adjust production schedules and workflows in response to changes in demand or supplier availability, and must be able to trust that the data and insights provided by the AI system are accurate and up-to-date, and that the system is able to handle the complexities and nuances of the production process, including the need to manage multiple suppliers and production lines.

Range planning under uncertainty

One of the key challenges in the textiles and apparel industry is range planning under uncertainty, where the company must make decisions about which styles to produce and in what quantities, without knowing for certain how they will be received by customers, and must balance the need to minimize inventory costs and markdown exposure with the need to ensure that popular styles are available in sufficient quantities to meet demand.

This can be particularly difficult when dealing with long lead times, where the company must commit to production schedules and supplier contracts well in advance of the actual production and sales process, and must be able to trust that the data and insights provided by the AI system are accurate and reliable, and that the system is able to handle the complexities and nuances of the production process, including the need to manage multiple suppliers and production lines.

Enforcing permission at retrieval

Most people get wrong the idea that enforcing permission at the model level is sufficient to ensure that sensitive data is protected, when in fact it is the retrieval level that is most critical, as this is where the actual data is being accessed and used, and where the company must be able to trust that the AI system is able to enforce the necessary permissions and access controls to prevent unauthorized access or misuse of the data.

For example, a category manager may need to access sales data and supplier information in order to make informed decisions about which styles to include in the upcoming seasonal range, and must be able to trust that the AI system is able to enforce the necessary permissions and access controls to prevent unauthorized access or misuse of the data, and that the system is able to provide accurate and reliable insights and recommendations to support the decision-making process.

Auditability

Auditability is critical in the textiles and apparel industry, where the company must be able to track and verify the data and insights provided by the AI system, and ensure that they are accurate and reliable, and that the system is able to provide a clear and transparent record of all interactions and decisions made, including what was asked, what was returned, and on what data, and that the system is able to provide the necessary documentation and reporting to support audit and compliance requirements.

This can be particularly important in the event of a dispute or issue with a supplier or customer, where the company must be able to provide clear and transparent evidence of the decisions and actions taken, and demonstrate that the AI system was used in a fair and unbiased manner, and that the necessary permissions and access controls were in place to prevent unauthorized access or misuse of the data, and for more information on how AsscherAi can support audit and compliance requirements, please visit our website.

Categories that should route to a human

There are certain categories of decisions that should always route to a human, such as those involving sensitive or high-risk data, or those that require a high degree of creativity or judgment, and where the company must be able to trust that the AI system is able to recognize and escalate these decisions to a human operator, and that the system is able to provide the necessary support and guidance to ensure that the human operator is able to make an informed and effective decision.

For example, a shift supervisor may need to escalate a decision to a human operator if the AI system is unable to provide a clear and reliable recommendation, or if the decision involves a high degree of uncertainty or risk, and must be able to trust that the AI system is able to provide the necessary support and guidance to ensure that the human operator is able to make an informed and effective decision, and for more information on how AsscherAi can support human decision-making, please contact us.

Limitations and exceptions

While AsscherAi can provide significant benefits and insights in the textiles and apparel industry, there are certain limitations and exceptions that must be considered, such as the need for high-quality and accurate data, and the potential for bias or error in the AI system, and where the company must be able to trust that the AI system is able to recognize and mitigate these limitations and exceptions, and that the system is able to provide the necessary support and guidance to ensure that the company is able to make informed and effective decisions.

For example, the AI system may not be able to provide accurate or reliable insights if the data is incomplete or inaccurate, or if the system is not properly trained or calibrated, and the company must be able to recognize and address these limitations and exceptions in order to ensure that the AI system is able to provide the necessary support and guidance to drive business success, and that the system is able to provide the necessary documentation and reporting to support audit and compliance requirements.