Precise forecasts are essential for companies today. AI-supported forecasting software creates reliable predictions of your sales and consumption based on historical data so that you can always fully satisfy your customers‘ demand. At the same time, tied-up capital, depreciation, wasted storage space and spoilage of goods are avoided.

Different forecasts are already being used in many areas of the company, for example in inventory management, workforce planning or personnel requirements planning. Data-driven methods for forecasting demand have become established in recent years. In this context, statistical, univariate methods such as exponential smoothing or ARIMA models have long been superior to all other methods in terms of forecasting accuracy. However, development in terms of software, hardware and algorithms has now progressed to such an extent that AI-based forecasting methods now significantly outperform solutions based on statistical benchmarks.

Accurate forecasts are of immense importance for retailers in order to be able to recognize in good time which goods should be purchased at what time and in what quantity. This is the only way they can optimize the availability of goods and minimize capital commitment costs at the same time. Furthermore, a reliable forecast has the advantage that only as many employees are deployed in the warehouses and stores as are currently needed.

Such predictions are easy to make under stable conditions. However, the retail sector is a dynamic environment that is influenced by many constantly changing factors. This includes the following variables, among others:

– Recurring sales patterns (e.g. weekdays, public holidays, seasonality)
– Internal business decisions (e.g. price changes, offers, store displays, campaigns)
– External factors (e.g. weather, local events)
– Unknown factors (e.g. store opening of a direct competitor in the neighborhood)
It is impossible for a human being to take such a wealth of factors and data sufficiently into account to make an accurate forecast. AI algorithms (especially machine learning algorithms), on the other hand, are perfect for various forecasting applications: They automatically learn patterns and correlations in historical data, which can then be applied to new data to make predictions. Given the relevant data, it is possible to forecast as many values as, for example, sales in the food trade. For example, it is possible to predict exactly how many chocolate Easter bunnies will be sold in a particular store within a certain period of time. The AI takes into account the following factors, among others: Number of days until Easter, number of Easter bunnies sold in previous years, day of the week, general purchasing behavior of customers. This forecast can then be used to order exactly the right number of chocolate Easter bunnies at the right time.

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