To do this, companies need answers to questions such as: What merchandise is needed on a given day in the individual sales outlets and per sales channel? And how many staff are needed accordingly on which sales floor? And what about the personnel required in the warehouse? Or which raw materials are to be ordered in which quantities in the next week to ensure smooth production? Regardless of whether it's a retail or manufacturing company, the answer to all of these questions comes down to forecasting future sales.
With ever-changing customer demand and dozens of factors influencing buying behavior, making reliable forecasts is a real challenge for many companies. It is simply impossible for human planners to penetrate the entire spectrum of potential influencing factors and take the effects into account. And the previous planning tools, above all the frequently used Excel spreadsheet or even the SAP modules SAP APO or SAP IBP, quickly reach their limits when faced with the increasing number of potential influencing factors. The result? According to a study by the IHL Group, inventory shortages in the retail sector alone cause global revenue losses of $634 billion every year, while excess inventory due to markdowns leads to revenue losses of $472 billion.
Machine learning methods promise to remedy this situation. Through the use of machine learning algorithms, more consistent, accurate and transparent sales forecasts can be created, ultimately increasing planning reliability. The need to make quick and, above all, better and more informed decisions is becoming increasingly important, especially in the current times of the Corona pandemic. Companies should therefore rely on machine learning now to understand what is happening now and, more importantly, what is likely to happen in the future.
