Data Screening, Cleaning and Scoring
About the transfer project
In the fast-paced world of logistics, where artificial intelligence (AI) and data-driven decisions are becoming increasingly important, it is tempting to invest directly in the latest technologies and AI tools. But before you do so, you should ensure that the foundation is solid—and that foundation is your logistics master data.
After all, clean master data is the foundation on which all logistics processes are built. It ensures that information about products, suppliers, and inventory is accurate and up to date. This helps to avoid wrong decisions that can lead to delays, increased costs, and dissatisfied customers. However, logistical master data is often incorrect or inaccurate, which requires tedious manual maintenance and complicates the diverse range of data-driven innovations. AI models, for example, can work much faster and in a more targeted manner with accurate and comprehensive information – e.g., recognizing patterns and making reliable predictions. Without clean master data, performing reliable analyses becomes a gamble, which can significantly jeopardize strategic decisions and optimizations.
The good news is that unclean or poorly maintained data is not inevitable—there is a solution. DSCS enables comprehensive analysis and evaluation of master data quality and identifies valuable optimization potential. We proceed as follows:
Services
- Software automatically recognizes the data structure and runs through all entries in the data record.
- Missing master data is indicated
- Incorrect data records are filtered out and made available separately for later correction.
- High-quality master data is provided for subsequent optimizations.
- In addition, the master data quality is categorized for the customer (good, average, poor).
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customer benefit
- High-quality master data is a prerequisite for smooth operations and optimization projects.
- Manual effort for master data maintenance/analysis is reduced and erroneous data is detected.
- Increased efficiency: Data-driven (process) optimizations such as carton set optimizations (CASTN) or process support during the packing process (iPackAssist) are made possible.
- Companies gain transparency regarding data, products, and processes
