• Written by Sonali Subhadarsini, Product Manager

When a category manager at a large textiles and apparel organisation starts using AsscherAi to query the company's own data in natural language, they often expect to see a significant reduction in the time spent on range planning, but what actually happens is that the shift supervisor still has to manually check the production schedule to ensure that the new designs can be manufactured on time, and the category manager has to negotiate with suppliers to adjust the delivery dates, which can be a lengthy process, and it is only after several iterations that the category manager can finally start to see some benefits from using AsscherAi, but even then, it is not always clear whether the time saved is actually being redeployed in a meaningful way.

Textiles and apparel complexities

The textiles and apparel industry is particularly challenging when it comes to implementing AI productivity solutions, mainly because of the uncertainty surrounding range planning, which involves predicting consumer demand for specific products, often with long lead times, and the fragmentation of suppliers, which can make it difficult to negotiate prices and delivery dates, and the exposure to markdowns, which can significantly impact profit margins, all of which can make it difficult to measure the effectiveness of AI productivity solutions, and it is not uncommon for organisations to struggle to implement these solutions in a way that actually delivers tangible benefits.

For example, a category manager may use AsscherAi to analyse sales data and identify trends, but they still have to consider factors like weather patterns, economic conditions, and consumer preferences, which can be difficult to predict, and even with the help of AI, it is not always possible to accurately forecast demand, and this can lead to overstocking or understocking, which can have serious consequences for the organisation, and it is only by carefully considering these complexities that organisations can hope to successfully implement AI productivity solutions.

Time saved versus time redeployed

One of the main challenges of implementing AI productivity solutions in textiles and apparel is that the time saved by using these solutions is not always redeployed in a meaningful way, and it is not uncommon for organisations to simply absorb the time saved into existing processes, rather than using it to drive innovation or improvement, and this can make it difficult to measure the effectiveness of these solutions, and it is only by carefully tracking how the time saved is being used that organisations can hope to achieve tangible benefits from AI productivity solutions, and more information on how to do this can be found on our website.

For instance, a shift supervisor may use AsscherAi to quickly identify production bottlenecks, but if the time saved is not used to implement process improvements or provide additional training to staff, then the benefits of using AsscherAi may be limited, and it is only by taking a holistic approach to AI productivity that organisations can hope to achieve significant benefits.

Self-reported time savings

Self-reported time savings are often overstated, and this can make it difficult to accurately assess the effectiveness of AI productivity solutions, and it is not uncommon for organisations to report significant time savings, only to find that these savings are not actually being realised, and this can be due to a variety of factors, including the failure to account for the time spent on implementing and maintaining the AI solution, and the failure to consider the impact of other factors, such as changes in consumer demand or supplier lead times, and it is only by taking a rigorous approach to measuring time savings that organisations can hope to accurately assess the effectiveness of AI productivity solutions.

For example, a category manager may report that they have saved several hours per week by using AsscherAi, but if they have not actually redeployed this time in a meaningful way, then the benefits of using AsscherAi may be limited, and it is only by carefully considering the actual impact of AI productivity solutions that organisations can hope to achieve tangible benefits, and contacting us can provide more information on how to do this.

Removing unnecessary tasks

One of the most effective ways to achieve tangible benefits from AI productivity solutions is to remove unnecessary tasks, and this can involve streamlining processes, eliminating redundant activities, and automating routine tasks, and it is only by taking a careful and considered approach to removing unnecessary tasks that organisations can hope to achieve significant benefits from AI productivity solutions, and this can involve using AsscherAi to analyse workflows and identify areas for improvement.

For instance, a shift supervisor may use AsscherAi to identify tasks that are no longer necessary, such as manual data entry, and automate these tasks, freeing up staff to focus on higher-value activities, such as process improvement or innovation, and it is only by taking a holistic approach to AI productivity that organisations can hope to achieve significant benefits, and this can involve considering the impact of AI productivity solutions on the entire organisation, from the shop floor to the boardroom.

In order to remove unnecessary tasks, organisations should start by identifying the tasks that are most time-consuming and labour-intensive, and then use AsscherAi to analyse these tasks and identify areas for improvement, and this can involve using techniques such as workflow mapping and process simulation to identify bottlenecks and areas for improvement, and

• identifying redundant activities
• eliminating unnecessary tasks
• automating routine tasks

Limitations of AI productivity solutions

AI productivity solutions are not a panacea, and there are certain situations in which they may not be the best choice, such as when the organisation is undergoing significant change or upheaval, or when the processes being targeted are highly complex or nuanced, and it is only by carefully considering these limitations that organisations can hope to achieve tangible benefits from AI productivity solutions, and this can involve taking a careful and considered approach to implementing these solutions, and considering the potential risks and challenges, as well as the potential benefits.

For example, if an organisation is in the process of implementing a new enterprise resource planning system, it may not be the best time to implement an AI productivity solution, as this can add complexity and risk to the project, and it is only by carefully considering the potential impact of AI productivity solutions that organisations can hope to achieve significant benefits, and this can involve taking a holistic approach to AI productivity, and considering the impact of these solutions on the entire organisation.