AI for procurement in FMCG
AI for procurement in FMCG streamlines the process by providing category managers with real-time data on supplier performance, price volatility, and inventory levels, enabling them to make informed decisions and reduce costs, while shift supervisors can provide valuable insights into promotional activities and shelf life write-offs.
A category manager in a fast-moving consumer goods company often finds themselves waiting for a report to answer a straightforward question, such as which suppliers are consistently delivering late or which products are experiencing the most price volatility. This wait can be frustrating, especially when the information is needed to inform a critical decision, like whether to renegotiate a contract or adjust inventory levels. The category manager may have to rely on the shift supervisor to provide some of the necessary data, which can further delay the process.
The procurement questions that currently wait on a report
The procurement team in a fast-moving consumer goods company typically has to deal with a high volume of transactions, which can make it difficult to get a clear picture of what is happening in real-time. Questions like what is the current spend with a particular supplier, or what is the average lead time for a specific product, can be difficult to answer without waiting for a report to be generated. The category manager may have to request a custom report from the IT department, which can take several days or even weeks to produce. In the meantime, the category manager has to make decisions based on incomplete or outdated information.
A shift supervisor, on the other hand, may be able to provide some of the necessary data, but it may not be in a format that is easily usable by the category manager. For example, the shift supervisor may be able to provide a list of recent deliveries, but it may not include information about the supplier or the product. The category manager would then have to manually combine this data with other information to get a complete picture.
The data behind them: purchase orders, supplier contracts and terms, invoice history, category structure
The data that the procurement team needs to answer their questions is typically scattered across multiple systems, including the enterprise resource planning system, the supply chain management system, and the contract management system. The category manager would need to access the purchase orders, supplier contracts and terms, invoice history, and category structure to get a complete picture of the procurement process. This can be a time-consuming and laborious process, especially if the systems are not well-integrated.
The category manager would also need to consider the relationships between different data points, such as how the supplier contracts and terms affect the purchase orders, or how the invoice history affects the category structure. This requires a deep understanding of the procurement process and the ability to analyze complex data sets.
Sector constraints: promotional distortion in history and shelf life write-offs
The fast-moving consumer goods sector is characterized by high volume, short shelf life, and promotion-driven demand, which can create distortions in the data. For example, a product may experience a surge in demand due to a promotion, which can make it difficult to determine the underlying trends. The category manager would need to take this into account when analyzing the data, and adjust their models accordingly. The shift supervisor can provide valuable insights into the promotional activities and their impact on the demand.
Shelf life write-offs are another constraint that the procurement team needs to consider. Products with short shelf lives may need to be prioritized, and the procurement team would need to ensure that they are not over-ordering or under-ordering. The category manager would need to work closely with the shift supervisor to ensure that the inventory levels are optimal.
What improvement looks like: spend under management, price variance to contract, supplier concentration
Improvement in the procurement process can be measured in terms of spend under management, price variance to contract, and supplier concentration. The category manager would need to track these metrics closely, and adjust their strategies accordingly. For example, if the price variance to contract is high, the category manager may need to renegotiate the contract or find alternative suppliers. The shift supervisor can provide valuable insights into the supplier performance and help the category manager to identify areas for improvement.
The procurement team can also use operations data to inform their decisions, such as data on inventory levels, shipping schedules, and production plans. By combining this data with the procurement data, the category manager can get a complete picture of the supply chain and make more informed decisions. If the category manager needs help with implementing these changes, they can contact us for more information.
What this does not do, or when it is the wrong choice
While AI for procurement in FMCG can provide valuable insights and help the procurement team to make more informed decisions, it is not a replacement for human judgment and expertise. The category manager would still need to use their knowledge and experience to interpret the data and make decisions. Additionally, AI for procurement in FMCG may not be suitable for companies with very simple procurement processes, or those with very limited data. In these cases, the benefits of AI for procurement in FMCG may not outweigh the costs.
In some cases, the procurement team may be better off using other tools or methods, such as
- spreadsheets
- manual reporting
Frequently asked questions
How can AI improve procurement processes in FMCG companies?
AI can help category managers analyze data on supplier performance, price volatility, and inventory levels, enabling them to make informed decisions and reduce costs.
What role do shift supervisors play in AI-powered procurement in FMCG?
Shift supervisors provide valuable insights into promotional activities, shelf life write-offs, and supplier performance, helping category managers to identify areas for improvement.
What metrics can be used to measure improvement in procurement processes?
Metrics such as spend under management, price variance to contract, and supplier concentration can be used to measure improvement in procurement processes and identify areas for further optimization.
When is AI for procurement in FMCG not the best choice?
AI for procurement in FMCG may not be suitable for companies with very simple procurement processes or limited data, where the benefits may not outweigh the costs, and other tools or methods may be more effective.