Enhancing SAP EWM Labels with Machine Learning

Mahmoud Ahmed Saad HAMED

Master's thesis

Abstract:

The SAP EWM is known as one of the most sophisticated ERP systems for its capabilities in managing complex warehouse operations; However, because of these challenges, the correctness of handling unit labels remains a source of ongoing concern. This thesis lays forth a comprehensive research programme, with the ultimate goal of improving the accuracy of handling unit labels in SAP EWM through the use of machine learning techniques. The primary research goals include: Within the SAP EWM environment, the construction of a machine learning model capable of the exact detection and validation of handling unit labels. Comprehensive evaluation of the machine learning model's performance using actual warehouse data and thorough empirical experimentation. An in-depth examination of the practical consequences and potential benefits of integrating machine learning-based handling unit label accuracy verification in SAP EWM.

Abstract:

The SAP EWM is known as one of the most sophisticated ERP systems for its capabilities in managing complex warehouse operations; However, because of these challenges, the correctness of handling unit labels remains a source of ongoing concern. This thesis lays forth a comprehensive research programme, with the ultimate goal of improving the accuracy of handling unit labels in SAP EWM through the use of machine learning techniques. The primary research goals include: Within the SAP EWM environment, the construction of a machine learning model capable of the exact detection and validation of handling unit labels. Comprehensive evaluation of the machine learning model's performance using actual warehouse data and thorough empirical experimentation. An in-depth examination of the practical consequences and potential benefits of integrating machine learning-based handling unit label accuracy verification in SAP EWM.

Keywords

Handling unit label correctness, SAP EWM, machine learning, warehouse management, supply chain, data accuracy, inventory tracking

Language used: English

Date on which the thesis was submitted / produced: 9. 2. 2024

Thesis defence

• Supervisor: prof. Dr. Helena Liebelt

UNIVERSITY OF SOUTH BOHEMIA IN ČESKÉ BUDĚJOVICE

Faculty of Science

Master programme / field:
Artificial Intelligence and Data Science / Artificial Intelligence and Data Science