Demand forecasting: an efficient future thanks to historical data

The better your understanding of the historical and current demand for your products and services, the more efficient you can be in the future. 

Jumping on seasonal trends, tapping into new target groups, or entering a new market (physical or digital) are all things that demand forecasting can help you with. 

Granted, strictly speaking, you don't need demand forecasting to do those things. You can also dig into the data manually and draw your own conclusions. But the more parameters and variables involved, the more complex the story becomes. And that's exactly when Data modelling — and specifically demand forecasting — comes into its own. 

The statistical models we use for demand forecasting are based on machine learning — underpinned by artificial intelligence.

The recent AI revolution has had a major impact here. For example, certain tools and platforms are more specifically tailored to certain sectors or use cases. This means that we can get started more easily and work faster and more efficiently.

The best news: demand forecasting is not just for multinationals that have turnover of millions of euros. Smaller and medium-sized organisations in different sectors can also benefit from it. What do you need? A reasonable amount of ordered historical data.

There are still a lot of potential in the field of demand forecasting — for organisations that can see the opportunities. It’s essential that you find a partner who knows how to get the most out of demand forecasting. iO, for example.