AI-basierte Materialflussanalyse

AI-Based Material Flow Analysis

How ONE LOGIC optimizes the analysis of complex, heterogeneous data files across various systems and visualizes this as an end-to-end process using ONE DATA

As a rule, data in major companies is distributed across a wide range of heterogeneous systems. However, central master data management is essential for comprehensive reporting. To be able to provide substantiated, cross-system analyses and forecasts, it is essential to create joint basis of data that – for example – unites material flow information and transactional data.

The customer in this use case had multiple ERP systems in operation, with scattered data leading to material flow problems due to a lack of transparency. The same type of materials had different IDs in the systems and across the individual production steps despite being technically identical. A lack of transparency within the material flow led to unnecessarily high inventories, poor delivery times and working capital being tied up. The customer turned to ONE LOGIC to improve its material flow using suitable algorithms and artificial intelligence, and to visualize this in an end-to-end data product.

AI-basierte Materialflussanalyse

Solution

Mapping & matching – the allocation and linking of master and variable data from various sources – is essential for being able to analyze and visualize the material flow across the different sites, facilities and production steps based on a difficult source of raw data. The customer had already started to manually map its data. ONE LOGIC paved the way for this process to be automated via machine learning and for the introduction of highly complex data management. To achieve this, a proof of concept was drawn up using the Data Product Platform ONE DATA. The data was compared, aligned, analyzed and – in part – reclassified using machine-based pattern analysis according to the same entities, including volume, weight, frequency, duration of storage, etc. This enabled ONE DATA to predict that material A and material B were identical and could be used for the same order. Visualization in ONE DATA allowed the material flow to be tracked and made transparent for the first time across the different sites and production steps. A specially created cockpit was used to further optimize the process. Pattern matching algorithms, decision trees and sequence mining were used, for example, to identify and visualize delivery delays and possible influencing factors. In addition, newly developed algorithms in ONE DATA meant that the entire process could be safeguarded.

Result

As an end-to-end solution, ONE DATA can be used to analyze even complex material flows and visualize them. The integration and linking of data is much quicker and more accurate, paving the way for reduced inventories and faster delivery times. The central Data Product Platform ONE DATA can also be used for other use cases, such as the AI-based analysis of customer relations management.


AI-basierte Materialflussanalyse

Download ‘AI-Based Material Flow Analysis’ for free

The distribution of data across many different, heterogeneous systems makes central master data management in companies difficult. With the use of the Data Product Platform ONE DATA, it is possible to create transparency and visibility in material flows and corresponding inventories and thus analyze and visualize raw data.

  • Using automated mapping and matching, highly complex data management is enabled
  • By using ONE DATA, data integration and linking runs faster and with higher accuracy
AI-Based Material Flow Analysis