AI for finance in FMCG
AI for finance in FMCG streamlines decision-making by providing real-time answers to financial questions, enabling the category manager to negotiate with suppliers and the shift supervisor to make informed decisions without waiting for reports, thus reducing mistakes and inefficiencies in procurement and budgeting.
When a finance team in a fast-moving consumer goods organisation tries to use AI to answer questions about their own data, they often find that the process stalls because the questions they need to ask are still waiting on a report from the accounting team, which can take days or even weeks to arrive, and by the time it does, the situation on the ground has already changed, making the report less relevant. The shift supervisor may have already made decisions based on incomplete information, and the category manager may have had to negotiate with suppliers without knowing the full picture of the company's financial commitments.
The finance questions that currently wait on a report
The finance team in a FMCG organisation typically needs to ask questions like what is the current spend with a particular supplier, or what is the total value of open commitments for a specific category of products. These questions are critical to making informed decisions about procurement, budgeting, and forecasting, but they often have to wait for a report to be generated by the accounting team, which can be a time-consuming process. The category manager may need to know the answer to these questions in order to negotiate with suppliers or to make decisions about which products to promote.
The problem is that the accounting team is often overwhelmed with requests for reports, and it can take a long time to generate the necessary information. In the meantime, the finance team has to make decisions based on incomplete or outdated information, which can lead to mistakes and inefficiencies. By using AI to answer questions about their own data, the finance team can get the information they need in real time, without having to wait for a report.
The data behind them: general ledger, budget, open commitments and purchase orders, receivables and terms
The data that the finance team needs to answer their questions is typically stored in a variety of systems, including the general ledger, budgeting systems, and procurement systems. The general ledger contains information about the company's financial transactions, including invoices, payments, and journal entries. The budgeting system contains information about the company's financial plans and forecasts, including budget allocations and variance analysis. The procurement system contains information about the company's purchasing activities, including purchase orders, contracts, and supplier information.
The finance team needs to be able to access and analyse this data in order to answer their questions and make informed decisions. However, the data is often scattered across different systems and departments, making it difficult to access and analyse. By using AI to integrate and analyse this data, the finance team can get a complete and up-to-date picture of the company's financial situation, and make decisions based on accurate and timely information. For more information on how AI can be used in finance, visit our finance page.
Sector constraints: promotional distortion in history and shelf life write-offs
One of the challenges of using AI in finance in the FMCG sector is dealing with the distortions caused by promotional activities and shelf life write-offs. Promotional activities, such as discounts and special offers, can create fluctuations in sales and revenue that can make it difficult to forecast and budget. Shelf life write-offs, on the other hand, can create distortions in the company's inventory and cost of goods sold. These distortions can make it difficult for the finance team to get an accurate picture of the company's financial situation, and can lead to mistakes and inefficiencies.
The finance team needs to be able to take these distortions into account when analysing the company's financial data, and to adjust their forecasts and budgets accordingly. By using AI to analyse the company's financial data, the finance team can identify and adjust for these distortions, and get a more accurate picture of the company's financial situation. This can help the company to make better decisions and to improve its financial performance.
What improvement looks like: variance to budget, commitment coverage, days sales outstanding
When the finance team is able to use AI to answer questions about their own data, they can start to see improvements in a number of key areas, including variance to budget, commitment coverage, and days sales outstanding. Variance to budget refers to the difference between the company's actual financial performance and its budgeted performance. Commitment coverage refers to the company's ability to meet its financial commitments, such as paying its suppliers and meeting its debt obligations. Days sales outstanding refers to the amount of time it takes the company to collect its receivables.
By using AI to analyse the company's financial data, the finance team can identify areas where the company is not meeting its budget or its financial commitments, and can take steps to improve its performance. For example, the category manager can use AI to identify areas where the company is overspending, and can take steps to reduce costs and improve profitability. The shift supervisor can use AI to identify areas where the company is not meeting its financial commitments, and can take steps to improve its cash flow and reduce its debt obligations.
The finance team can also use AI to identify opportunities to improve the company's financial performance, such as by reducing its days sales outstanding or improving its commitment coverage. By taking advantage of these opportunities, the company can improve its financial performance and increase its profitability. If you would like to learn more about how AI can be used to improve financial performance, please contact us.
What this does not do
While AI can be a powerful tool for improving financial performance in the FMCG sector, it is not a magic solution that can solve all of the company's financial problems. It is not a replacement for good financial management practices, such as budgeting and forecasting, and it is not a substitute for experienced financial professionals. Most people get wrong the idea that AI can automatically generate financial reports and forecasts without any human intervention, but this is not the case. AI is a tool that can be used to support and augment the work of financial professionals, but it is not a replacement for their skills and expertise.
AI is also not a solution for companies that do not have good financial data or systems in place. In order to get the most out of AI, companies need to have accurate and up-to-date financial data, as well as systems and processes in place to support financial analysis and decision-making. Without these foundation elements in place, AI is not likely to be effective in improving financial performance.
Frequently asked questions
How can AI improve financial decision-making in FMCG?
AI provides real-time answers to financial questions, reducing reliance on reports and enabling better decision-making.
What data does the finance team need to access to answer financial questions?
The finance team needs to access data from the general ledger, budgeting systems, procurement systems, and other sources to answer financial questions.
What challenges does the FMCG sector face when using AI in finance?
The FMCG sector faces challenges such as promotional distortion and shelf life write-offs, which can create fluctuations in sales and revenue.
What benefits can the finance team expect from using AI to answer financial questions?
The finance team can expect improved variance to budget, commitment coverage, and days sales outstanding, leading to better financial performance and increased profitability.