How can AI in demand forecasting help finance add value? 3 big wins

Demand forecasting has been around for decades. Is it easier and more accurate now that businesses have vast volumes of data at their disposal? Absolutely not … until artificial intelligence (AI) comes in. How can AI take demand forecasting to the next level? And how does that benefit not only the traditional operations planning and control processes, but also finance?

Simply put, demand forecasting refers to making estimations about future customer demand as a basis for informed decisions about pricing, inventory, service levels, business growth strategy, market potential, etc. Over the past few years, AI has been improving demand forecasting accuracy, as it can handle massive amounts of data from diverse sources. More than looking at historical data, seasonality and trends, machine learning uses demographics, weather, online reviews, social media data, etc. It will then analyze varied demand patterns and scenarios to estimate future demand.

While traditionally very popular in optimizing supply chain and S&OP processes, AI-driven demand forecasting also adds value to finance. Here are three big wins:

AI-driven demand forecasting provides a clearer picture of what might be. In this way, it helps finance to spot opportunities to save costs, optimize profit margins, improve cashflow and, consequently, define a well-founded growth strategy.

• Inventory-related insights show you how to manage storage space more efficiently and avoid inconsistent inventory buys, overstocking, understocking and consequent margin erosion.
• By gaining an accurate view of stock levels, you can encourage sales to monetize stock or use promotional planning to deal with overcapacity in specific regions.
• As machine learning considers demand drivers such as promotional details, new product introductions, social media, payment behavior, fluctuations in raw material prices, etc., it shows organizations how to respond dynamically to market events and consumer trends.

Artificial intelligence provides finance with an accurate view of what the company will sell to which customers. By including an extra AI model to predict payment terms, the finance team can help the business improve cashflow planning. Accurate forecasts boost investor confidence.

Internally, results from AI-driven demand forecast models challenge managers to think deeply about their business: what is the reason behind the forecasted peaks or drops, and what measures can be taken to augment or minimize them? In this way, AI helps identify opportunities and risks.

Excel is still a very popular tool among controllers and their colleagues in the finance department. Yet consolidating and comparing files to base predictions on is a manual, time-consuming and error-prone task that leads to mostly short-term conclusions. Pre-built software tools are not the best solutions either. The setup process is often tedious, and users wrestle with the tools’ complexity. By adding artificial intelligence to your existing platform, you can give your users a platform they trust that is easy to manage while distilling the most valuable insights from massive loads of data.

How we see artificial intelligence? It’s that extra person at the table who’s there to think with you. Especially in finance, that additional brain is very useful. Of course, machines cannot and will not be able to work alone. While they can offer better insights, the decision-making process is still dominated by humans. We remain responsible for the decisions machine learning is helping us make – in finance, and in any other domain.

Get started on your first AI-driven demand forecasting project. Get in touch with our experienced team.