• Written by Sonali Subhadarsini, Product Manager

On a typical Monday morning, the category manager for a large FMCG company is already dealing with the fallout from a promotional campaign that ended over the weekend, trying to reconcile the inventory levels with the sales data, and figuring out how to minimize the write-offs from expired products. This is a familiar scenario in the fast-moving consumer goods industry, where high volume, short shelf life, and promotion-driven demand create a unique set of challenges.

What FMCG makes harder

The FMCG industry is characterized by promotional distortion, which makes it difficult to accurately forecast demand and manage inventory. The shift supervisor at a manufacturing plant, for example, has to deal with the consequences of overproduction or underproduction, which can lead to wasted resources and lost sales. Additionally, the short shelf life of many FMCG products means that companies have to be able to quickly respond to changes in demand, or risk writing off expired products.

The lack of visibility into distributor operations is another challenge that FMCG companies face. The logistics manager, for instance, may not have real-time information about the inventory levels at different distribution centers, making it harder to optimize the supply chain. All these factors combined create a complex environment that can make it difficult to implement productivity improvements, including those enabled by AI.

The difference between time saved and time redeployed

When implementing AI-powered productivity tools, such as AsscherAi, it's essential to distinguish between time saved and time rededeployed. While the former refers to the reduction in time spent on a specific task, the latter refers to the ability to allocate that saved time to more strategic or value-added activities. The category manager, for example, may save time by using AsscherAi to analyze sales data, but if that time is not redeployed to optimize the product mix or improve supplier negotiations, the overall impact on productivity may be limited.

A good example of this is when a shift supervisor uses AsscherAi to quickly identify maintenance issues, saving time that would have been spent on manual checks. However, if that saved time is not used to implement preventative maintenance or improve overall equipment effectiveness, the benefits of the productivity gain may not be fully realized.

Why self-reported time savings overstate the effect

Self-reported time savings are often used to measure the effectiveness of productivity improvements, but they can be misleading. People tend to overestimate the time they save, and may not account for the time spent on other tasks that are not directly related to the productivity tool. Moreover, self-reported time savings do not take into account the time spent on training, implementation, and maintenance of the productivity tool, which can be significant.

For instance, the logistics manager may report that they have saved several hours a week by using AsscherAi to optimize routes, but they may not consider the time spent on training drivers to use the new system, or the time spent on troubleshooting issues that arise during implementation. To get a more accurate picture of the productivity gains, it's essential to use more objective measures, such as data from the system itself, or observations of how people are using their time.

The tasks worth removing first

When implementing AI-powered productivity tools, it's essential to prioritize the tasks that are most amenable to automation, and that will have the greatest impact on productivity. For example, tasks that involve repetitive data entry, or manual analysis of large datasets, are often good candidates for automation. The category manager, for example, may use AsscherAi to automate the analysis of sales data, freeing up time to focus on more strategic activities, such as optimizing the product mix or improving supplier negotiations.

By removing these tasks, companies can free up time and resources to focus on higher-value activities, such as innovation, customer service, or supply chain optimization. To learn more about how AsscherAi can help your organization, visit our website to see how our platform can help you query your own data in natural language and get answers in real time.

What this does not do

While AI-powered productivity tools, such as AsscherAi, can bring significant benefits to FMCG companies, they are not a silver bullet. They do not replace the need for good management practices, such as effective supply chain management, or strategic planning. Moreover, they may not be suitable for all types of tasks, or all types of organizations. For example, companies with very simple supply chains, or those that do not have large amounts of data to analyze, may not see significant benefits from using AsscherAi.

In some cases, the costs of implementing and maintaining an AI-powered productivity tool may outweigh the benefits, particularly if the company does not have the necessary infrastructure, or if the tool is not well-integrated with existing systems. If you are considering implementing an AI-powered productivity tool, contact us to discuss your specific needs and determine whether AsscherAi is right for your organization.