• Written by Sonali Subhadarsini, Product Manager

A shift supervisor in a fast-moving consumer goods company is likely to start their week by checking the maintenance log for any equipment issues that may impact production, while also keeping an eye on the stock levels of promotional items that are due to go on sale soon, and wondering what questions they can ask of their own data to inform their decisions.

The operations questions that currently wait on a report

The operations team in a fast-moving consumer goods company typically has to wait for a report to be generated before they can answer questions such as what is the current stock level of a particular product, or what is the status of a specific production run. This can be frustrating, especially when the team needs to make quick decisions to respond to changes in demand or supply. The shift supervisor may need to know what products are due to expire soon, so that they can plan production and inventory accordingly. They may also need to know which suppliers are performing well, and which ones are causing delays or quality issues.

These questions are critical to the smooth operation of the business, but the team often has to rely on manual processes or outdated reports to get the answers they need. This can lead to delays, mistakes, and inefficiencies, which can have a significant impact on the bottom line. By using AI for operations, the team can get the answers they need in real time, without having to wait for a report to be generated.

The data behind them: process and exception records, project status, budget consumption, supplier performance

The data that underlies these questions is typically stored in a variety of systems, including enterprise resource planning systems, supply chain management systems, and quality control systems. This data includes process and exception records, project status, budget consumption, and supplier performance metrics. The category manager, for example, may need to compare the performance of different suppliers, to determine which ones are providing the best quality products at the best price. They may also need to track the status of specific projects, such as the launch of a new product, to ensure that everything is on track and that the necessary resources are being allocated.

The data is often fragmented and difficult to access, which can make it hard for the operations team to get a clear picture of what is happening across the business. By using AI for operations, the team can bring all of this data together, and use it to inform their decisions. They can use the data to identify trends and patterns, and to anticipate potential problems before they occur. For more information on how AI can be used in operations, visit our operations page.

Sector constraints: promotional distortion in history and shelf life write-offs

One of the challenges that fast-moving consumer goods companies face is the impact of promotional activity on demand. Promotions can create spikes in demand that are difficult to predict, and can lead to stockouts or overstocking. The team may also need to deal with the impact of shelf life write-offs, which can occur when products are not sold before they expire. This can be a significant problem, especially for products with short shelf lives. The shift supervisor may need to work closely with the category manager to ensure that production and inventory are aligned with demand, and that the necessary steps are being taken to minimize waste and maximize sales.

These sector constraints can make it difficult for the operations team to plan and forecast, and can lead to inefficiencies and waste. By using AI for operations, the team can better anticipate and respond to these challenges, and can use data and analytics to inform their decisions. They can use the data to identify opportunities to improve efficiency and reduce waste, and to develop strategies for managing promotional activity and minimizing shelf life write-offs.

What improvement looks like: on-time delivery, exception rate, expediting cost

Improvement in the operations of a fast-moving consumer goods company can take many forms, but some common metrics include on-time delivery, exception rate, and expediting cost. The shift supervisor may be focused on ensuring that production runs are completed on time, and that products are delivered to customers when promised. The category manager may be focused on reducing the exception rate, which refers to the number of times that a product is not available when a customer orders it. They may also be looking to reduce expediting cost, which refers to the cost of rushing products to customers when they are not available on time.

By using AI for operations, the team can identify opportunities to improve these metrics, and can use data and analytics to inform their decisions. They can use the data to identify trends and patterns, and to anticipate potential problems before they occur. They can also use the data to develop strategies for improving on-time delivery, reducing the exception rate, and minimizing expediting cost. For example, they may use the data to identify the root causes of delays or stockouts, and to develop plans for addressing these issues.

What this does not do, or when it is the wrong choice

While AI for operations can be a powerful tool for fast-moving consumer goods companies, it is not a solution that is right for every situation. For example, if the company is looking to implement a completely new operations system, or to overhaul its entire supply chain, then AI for operations may not be the best choice. In these situations, a more comprehensive solution may be needed, one that involves significant changes to processes and systems. In these cases, it may be better to contact us directly to discuss the options, visit our contact page for more information.

Additionally, AI for operations is not a replacement for human judgment and expertise. While the technology can provide valuable insights and recommendations, it is still important for the operations team to use their own experience and knowledge to inform their decisions. The technology is a tool, not a solution, and it should be used in conjunction with other approaches to achieve the best results. By understanding what AI for operations can and cannot do, companies can make informed decisions about how to use the technology to improve their operations and achieve their goals.