In our collaboration with the Fraunhofer Innovation Platform for Advanced Manufacturing at the University of Twente, we focus on developing an advanced sales forecasting model using Artificial Intelligence. The model we developed allows companies to more accurately predict future demand and adjust inventory levels accordingly.
The importance of sales forecasting
Sales forecasting allows companies to gain insights into expected sales trends, seasonal fluctuations and customer preferences. These insights allow an organization to accurately anticipate demand, preventing under- or over-production.
- Improved Efficiency: By having an accurate picture of future demand, companies can better align their production and procurement planning with actual requirements. This means that production capacity can be utilized optimally, without unnecessary peaks or troughs.
- Lower inventory costs: Holding excess inventory incurs high costs, such as storage and obsolescence costs. Good sales forecasting helps companies replenish inventory at exactly the right time. This way, they always have the right materials or items in stock and avoid excess inventory.
- Improved customer satisfaction: With a good understanding of demand, a company can better respond to customer needs. This not only reduces delivery time, but also prevents customers from being disappointed due to lack of product availability.
- Sustainability and less waste: By making more accurate forecasts, companies can produce and purchase exactly the quantity needed, resulting in less waste of materials and resources. This contributes to more sustainable operations.
Why sales forecasting with AI?
Thanks to the application of AI, it is possible to analyze huge amounts of data and recognize patterns in it that were previously invisible. This makes it easy to tighten up purchasing planning and production lines and thus operate more efficiently. It enables companies to respond proactively to detected patterns or anomalies and thus respond quickly to changing market conditions.
- Data-driven insights: The application of machine learning allows companies to not only analyze historical data, but also discover patterns that were previously invisible. This leads to smarter decisions beyond traditional methods.
- Realtime analysis: The model uses AI to make real-time predictions, which means companies can dynamically adjust their inventory and sales strategies to current market conditions without spending time on analysis or reporting. For example, you can retrieve an accurate forecast every day, based on everything that has taken place up to the day before. Say goodbye to monthly or (semi-)annual reports!
- Proactive instead of reactive: Where companies used to react to fluctuations in supply and demand, AI-driven forecasting models enable them to be proactive. They can anticipate trends instead of reacting to the market.
With this collaboration, we are taking predictive technology to the next level. We help our customers not only reduce inventory, but also better serve their customers and reduce costs. This contributes to a more efficient and sustainable future.