In order to answer to the new market demand industry turn to software vendors looking for specific ERP systems and starting specific projects for supporting Business Process Redesign (BPR). In such a context authors identified a lack of anticipatory models able to drive the ERP implementation process to the right thus proposing a meta-modeling approach able to bridge this gap. Proposed methodology integrates Data Analysis, Regression Meta-Modeling and Artificial Neural Networks processing, in order to identify hidden relationships among KPI guiding BPR decision makers. The paper presents the methodology as well as a practical application.

Neural Networks and Regressive KPI Metamodels for Business Corporate Management: Methodology and Case Study

REVETRIA, ROBERTO;TONELLI, FLAVIO
2010-01-01

Abstract

In order to answer to the new market demand industry turn to software vendors looking for specific ERP systems and starting specific projects for supporting Business Process Redesign (BPR). In such a context authors identified a lack of anticipatory models able to drive the ERP implementation process to the right thus proposing a meta-modeling approach able to bridge this gap. Proposed methodology integrates Data Analysis, Regression Meta-Modeling and Artificial Neural Networks processing, in order to identify hidden relationships among KPI guiding BPR decision makers. The paper presents the methodology as well as a practical application.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/234566
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