• Written by Sonali Subhadarsini, Product Manager

When the category manager at a large FMCG company checks the sales data for a particular product, they often find that the numbers are skewed by promotional activities, making it difficult to determine the actual demand for the product, and this distortion in the data can have a ripple effect throughout the entire supply chain, leading to inaccurate forecasting and ineffective decision making. The shift supervisor at the manufacturing plant may then struggle to adjust production levels accordingly, resulting in excess stock or stockouts, which can be costly and damaging to the company's reputation. This is just one example of how the complexities of the FMCG industry can make process automation challenging.

FMCG complexities

The FMCG industry is characterized by high volume, short shelf life, and promotion-driven demand, which can create significant challenges for process automation. For instance, promotional activities such as buy-one-get-one-free or discounts can distort the sales data, making it difficult to determine the actual demand for a product. Additionally, the short shelf life of many FMCG products means that any delays in the supply chain can result in stock write-offs, which can be costly for the company. The lack of visibility into the distributor's inventory levels can also make it difficult to anticipate and respond to changes in demand.

The category manager may try to account for these factors when analyzing sales data, but it can be a time-consuming and labor-intensive process. The use of AsscherAi can help to alleviate some of these challenges by providing real-time insights into the company's data, but it is still important to understand the complexities of the FMCG industry and how they can impact process automation.

Mapping the current process

Before automating any part of the process, it is essential to map out the current workflow and identify areas where automation can add value. This involves understanding the roles and responsibilities of each team member, including the shift supervisor, category manager, and logistics coordinator, and how they interact with each other and with the company's systems. By mapping out the current process, companies can identify bottlenecks and inefficiencies and determine where automation can help to streamline the workflow.

This can be a time-consuming process, but it is crucial to ensuring that automation is effective and efficient. The category manager may need to work closely with the shift supervisor and logistics coordinator to understand the current workflow and identify areas for improvement. By taking the time to map out the current process, companies can ensure that automation is targeted at the areas that will have the greatest impact.

Handoffs and delays

One of the primary causes of delay in the FMCG industry is the handoff between different teams and departments. For example, when the shift supervisor at the manufacturing plant needs to adjust production levels, they may need to wait for approval from the category manager, which can cause delays. Similarly, when the logistics coordinator needs to adjust shipping schedules, they may need to wait for confirmation from the distributor, which can also cause delays. These handoffs can be time-consuming and can result in delays and inefficiencies in the supply chain.

By automating the steps around these handoffs, companies can help to reduce delays and improve efficiency. For instance, the category manager can use AsscherAi to analyze sales data and adjust production levels in real-time, without needing to wait for approval from other teams. This can help to reduce delays and improve the overall efficiency of the supply chain.

Exception handling

Exception handling is a critical component of process automation in the FMCG industry. Because the industry is characterized by high volume, short shelf life, and promotion-driven demand, there are often exceptions to the normal workflow. For example, a product may be recalled due to a quality issue, or a natural disaster may disrupt the supply chain. In these cases, the normal workflow may need to be adjusted, and exception handling procedures need to be in place to ensure that the company can respond quickly and effectively.

Exception handling can be time-consuming and labor-intensive, but it is essential to ensuring that the company can respond to unexpected events and minimize disruptions to the supply chain. The category manager and shift supervisor may need to work closely together to develop exception handling procedures and ensure that they are in place and effective. By automating the steps around exception handling, companies can help to reduce the risk of errors and improve the overall efficiency of the supply chain.

Limitations of AI process automation

While AI process automation can be a powerful tool for improving efficiency and reducing costs in the FMCG industry, it is not a panacea. There are certain situations where AI process automation may not be the best choice, such as when the workflow is highly variable or when there are significant cultural or organizational barriers to adoption. In these cases, companies may need to consider alternative approaches, such as process re-engineering or change management initiatives.

Additionally, AI process automation is not a substitute for human judgment and decision-making. While AI can provide insights and recommendations, it is still up to the category manager, shift supervisor, and other team members to make decisions and take action. By understanding the limitations of AI process automation, companies can ensure that they are using the technology effectively and efficiently, and that they are getting the most value out of their investment.