Salesforecast

Sales forecasting in transition: tradition vs. AI

Sales forecasting is an essential element of strategic planning in every company. It serves to accurately predict the expected sales figures for a product or service over a specific period of time. An efficient forecasting process is crucial for success and has a significant influence on decisions in the areas of procurement, production, sales and finance.

Humans vs. machines: How accurately can the future be predicted?

For decades, companies have been using traditional statistical methods to make sales forecasts. These methods range from simple algorithms such as moving averages to more complex approaches such as time series analysis. However, although these methods can provide valuable predictions, they have their limitations. They often assume stable, repetitive patterns and cannot cope with sudden changes quickly enough.

This is where artificial intelligence (AI) comes into play. With advanced algorithms, AI can recognise complex patterns that the human eye may overlook. In addition, AI is able to perform comprehensive analysis of historical data and combine it with current market trends to make more accurate forecasts.

Demand forecasting: sales forecasting 2.0

Demand forecasting is transforming the way sales forecasts are made through the use of artificial intelligence and machine learning. It enables a more detailed analysis of historical sales data and takes into account a variety of factors that can influence demand, such as seasonal fluctuations, economic indicators and competitive activities.

AI-powered demand forecasting can offer a decisive advantage when analysing data sets that are too large for manual analysis or traditional statistical methods. AI systems can detect patterns in the data that might otherwise be overlooked. They can also predict changing trends and make adjustments to the forecast as data comes in.

Artificial intelligence methods for sales forecasting

In the context of sales forecasting, artificial intelligence uses various methods such as neural networks, support vector machines and randomised decision trees to correct errors in traditional approaches and deliver accurate estimates. Through self-learning algorithms, AI can continuously improve and optimise forecasting models.

Advantages of artificial intelligence in demand forecasting

By using AI for demand forecasting, companies can increase their operational efficiency, reduce waste and lower operating costs. In addition, accurate sales forecasts can be made, enabling optimal resource allocation, which ultimately leads to higher profits.

Conclusion

Sales forecasting is an integral part of every company's business strategy. By combining traditional forecasting methods with advanced AI technologies, the quality of forecasts can be significantly improved. This accuracy is key to maximising revenue, reducing operational risk and creating a sustainable competitive advantage in an increasingly complex and dynamic business world.