When it rains, hardly any barbecue meat is bought, and when the temperature is low, less sushi is bought. Simple correlations like these have two problems, however: Firstly, they are only available at very short notice, whereas production and supply chains are longer-term processes. Secondly, they are relatively imprecise, so a large buffer still has to be factored into planning. Retailers and manufacturers of perishable goods need accurate sales forecasts for their planning and strategic development. In the age of Artificial intelligence and machine learning, it is possible to calculate sales forecasts more accurately than ever before. This brings retailers great advantages and enormous potential

Competition, sales volumes and market shares in the food

For food manufacturers and retailers in particular, but also for consumer goods manufacturers, it is of enormous importance to correctly identify market opportunities and market risks at an early stage and to plan and calculate accordingly. Accurate sales forecasts give companies a head start that can give them a decisive advantage.

The biggest challenge with perishable food and consumer goods is to know the actual quantity demanded as accurately as possible in advance. The prerequisite for accurate sales forecasts is the availability of measurable data and corresponding data sources.

Optimised sales forecast thanks to AI

The factors on which volume planning is based are more complex than simple weather forecasts. Especially because several factors can interact, static models have not been accurate enough up to now. Especially when it came to longer-term forecasts, they reached their limits. Thanks to AI-supported methods However, today complex models can also be used as a basis for sales forecasts.

Analysing data on past sales can reveal patterns and relationships that can be used to predict future sales. The most important factors that can be taken into account are:

• Overall market development
• Market volume and market potential
• Demand
• Trends
• Weather
• Sales history

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High precision for short-, medium- and long-term forecasts

Particularly through the use of machine learning algorithms, a precision in sales forecasts can be achieved that was previously not possible in this level of detail and reliability. The learning ability of machine learning algorithms means that the forecasts become more and more accurate over time. This is achieved by using the adaptive Algorithms Get feedback in the form of actual sales.

Over time, the algorithms learn to weight the individual factors better and better and thus arrive at accurate sales forecasts. Predictive modelling with Artificial Neural Networks make long-term predictions possible that were highly error-prone with traditional methods. However, they are of particular importance for long-term planning.

The advantages of sales forecasting with AI

There are a whole range of benefits that can be derived from AI-supported sales forecasts. Not every retailer will exploit the full scope of what is possible in principle. Therefore, it is important to first determine the specific need, review the available data set and test the proof-of-value. Sales forecasts bring retailers and manufacturers the following advantages, among others:

• Better inventory planning thanks to precise forecasts
• Better market positioning vis-à-vis competitors
• Increase profit and minimise depreciation totals
• Optimal control of production, production resources and staff planning
• Building know-how within the framework of a data strategy
• Opening up and integrating different data sources into the company with the potential for further, additional data science projects

With advanced methods such as sales forecasts with AI, retailers and manufacturers can not only easily gain knowledge about weather conditions, but also obtain reliable, medium- and long-term forecasts within the framework of complex scenarios. These enable them to plan and control all processes better than ever before.