SELF-ADAPTIVE CUSTOMIZING WITH DATA MINING METHODS - A Concept for the Automatic Customizing of an ERP System with Data Mining Methods

Rene Schult, Gamal Kassem

Abstract

The implementation of an ERP system is a long and cost intensive process. Functions of the ERP system, which are delivered in an enterprise neutral but sector specific fashion need to be adjusted to the specific business requirements of an enterprise. Exact knowledge of the ERP system is required because each ERP system has its own technical concepts and terminologies. Therefore many enterprises employ ERP system experts in order to customise the ERP system to be introduced as well as to further enhance the customisation after its introduction. A concept for the implementation of a Self-Adaptive ERP System should allow for the automatic customisation of an ERP system on the basis of the of enterprise process models provided and analysis of the ERP system usage.

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Paper Citation


in Harvard Style

Schult R. and Kassem G. (2008). SELF-ADAPTIVE CUSTOMIZING WITH DATA MINING METHODS - A Concept for the Automatic Customizing of an ERP System with Data Mining Methods . In Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 6: ICEIS, ISBN 978-989-8111-38-8, pages 70-75. DOI: 10.5220/0001676800700075


in Bibtex Style

@conference{iceis08,
author={Rene Schult and Gamal Kassem},
title={SELF-ADAPTIVE CUSTOMIZING WITH DATA MINING METHODS - A Concept for the Automatic Customizing of an ERP System with Data Mining Methods},
booktitle={Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 6: ICEIS,},
year={2008},
pages={70-75},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001676800700075},
isbn={978-989-8111-38-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 6: ICEIS,
TI - SELF-ADAPTIVE CUSTOMIZING WITH DATA MINING METHODS - A Concept for the Automatic Customizing of an ERP System with Data Mining Methods
SN - 978-989-8111-38-8
AU - Schult R.
AU - Kassem G.
PY - 2008
SP - 70
EP - 75
DO - 10.5220/0001676800700075