Customers want to receive the right product via the right channel in the right quantity and at the right time.
To do this, companies need answers to questions such as: Which goods are needed on a specific day in the individual sales branches and per sales channel? And how many personnel are required on which sales area? And what about the required personnel in the warehouse? Or which raw materials should be ordered in which quantities in the next week to ensure smooth production? Regardless of whether you are a trading or manufacturing company, the answer to all these questions involves forecasting future sales.
With constantly changing customer demand and dozens of factors that influence buying behavior, it is a real challenge for many companies to create reliable forecasts (forecasts). It is simply impossible for human planners to penetrate the entire spectrum of potential influencing factors and consider the effects. And the previous planning tools, in particular the frequently used Excel spreadsheet or even the SAP APO or SAP IBP modules, are quickly reaching their limits due to the increasing number of potential influencing factors. The result? One IHL Group study According to this, trade shortages alone cause global sales losses of $634 billion each year, while excess inventory due to copies results in sales losses of $472 billion.
Machine learning processes promise to provide a remedy here. By using machine learning algorithms, more consistent, more accurate and more transparent sales forecasts can be created, ultimately increasing planning security. The need to make quick and, above all, better and well-founded decisions is becoming increasingly important, especially in current times of the corona pandemic. Companies should therefore rely on machine learning now to understand what is happening now and, even more importantly, what is likely to happen in the future.