With the evolution of manufacturing and delivery techniques, as well as globalization, supply chain management is becoming increasingly complex and dense.

The results of key figure calculations for inventory management, forecast reliability calculations, and even delivery management are also becoming more complex with these developments. Spreadsheet formulas have reached their limits for medium and large companies and do not provide the granularity of results.

However, with computerization, companies are generating large amounts of data (datalake) at all stages of the supply chain. With this data, it is possible to bring granularity to the results. But to achieve this, algorithms must be put in place to process the data and generate forecasts or decision support.

In addition to improving decision making, the use of machine learning also enables executives to be more responsive to sudden changes in market trends.