Demand forecasting: to predict, or to guess?

Demand forecasting has always been a crucial technique for companies in order to predict future situations, helping with inventory and production issues and setting prices. The problem with demand forecasting has always been the same: you would never be able to have a certain answer, as you are predicting future scenarios.

However, current advances in technology and the progression of AI have enabled companies to raise the accuracy of this forecast to unprecedented numbers (over 90%). Although this is an important improvement, the greatest change isn't the rise in accuracy that comes from being able to analyze many more factors than before, but the possibility of knowing the exact accuracy of that forecast, making companies able to know how much to depend on it.

How has technology improved predictions so much?

Traditionally, forecasting was done manually by companies, just by taking a clever guess based on historical sales. That wasn't a forecast, but a guess. Nowadays the data obtained by companies is much larger, and this data can be stored and used in thousands of different ways, obtaining great actionable insights.

The unification of huge volumes of information brought up a new problem: people were unable to read it and create insights from it in an efficient way. So AI was applied, with the ability to read that information and simplify it for those teams. The use of AI over data enables precise predictions, and also indicates the precision of those predictions. This precision depends on the amount of data and the quality of it. For a company to become data-driven it is crucial to be able to store and use wisely every single piece of data available.

Why is demand forecasting so useful?

1. Inventory and production efficiency boost

Stock issues have always been a problem for retail companies. Demand forecasting has been done for many years, but in an inaccurate way. This is changing radically as AI enables automated forecasting, which helps solve stock problems. With AI, companies can predict the volume of sales for each product to control their inventory and production, boosting efficiency and saving large amounts of capital.

2. Setting prices

Setting prices has always been a complicated task for companies, as it is not only a matter of trying to balance your numbers; it is fundamental to understand the demand of a product in order to set prices, depending on the channel's and product's demand. This can be done with a demand forecast driven by data and AI.

Demand forecasting is another one of FLYDE's possibilities

These solutions seem quite far away for medium-sized companies, as most tend not to be data-driven the way big multinationals are. But there has been a radical change in the market: the entrance of FLYDE, the easy-to-use intelligent CDP, makes this technology approachable by any company (sector or size), and, most importantly, each team can have control over the actions driven by this platform.

FLYDE gives companies the possibility to unify all of their customer data to exploit it in thousands of different ways. One of them is demand forecasting, which can be done by channel, product, product category, or even by earnings. These categories can also be combined to make the most precise forecast of demand possible.