There are various ways in which machine Learning can be used to optimize SAP® systems and master data in particular. However, the use of machine learning also requires careful planning and implementation to ensure that the results are interpreted correctly and can be used meaningfully in business processes.
One way is to develop machine learning models to automatically classify and cleanse master data. This can be, for example, the classification of products, customers or suppliers. Machine learning models can be trained based on existing master data and other relevant information to increase the accuracy of classification and cleaning, thereby improving data quality.
Another option is to use machine learning models to predict future orders or sales. Here, historical data such as order and sales histories, as well as external factors such as market conditions and weather data, can be incorporated into the analysis to make more accurate predictions. These predictions can help optimize inventory levels and thus reduce costs.
Machine learning models can also help optimize the planning and control of production processes. For example, models can be developed that predict the optimal production quantity and timing based on various factors such as demand forecasts and available resources. By combining data in real time, companies can respond faster to their customers’ requests.
The system quickly learns which products sell best, for example, and companies can thus intervene in the production process in a targeted manner. Data combinations are particularly helpful when working across departments, for example between marketing/sales and the production department.
By using AI, the system immediately detects when there is an inconsistency of data. In the course of this, data cleansing already takes place during the input of new data. Furthermore, AI always checks whether the existing data is correct. In addition to the increased speed, the user experience also improves.
Improved data quality.
More efficient processes.
Better predictions and decisions.
Improved customer loyalty.
Automated error detection.
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