Risk management of Enterprise Resource Planning (ERP) projects is largely recognized as a very complex task both by academics and practitioners. Strict interconnections among risk factors often occur so that indirect effects on the overall project performance are very likely. Unfortunately, the implications of interdependency are usually underestimated by project managers and decision makers since they are difficult to include in any risk assessment logic. This work shows how Colored Petri Nets (CPNs) can be used to model risk factors in ERP projects in order to deal with the problem of interdependence in risk assessment. The technique is presented through an application to a real case study. Findings highlight the importance of interdependence and the indirect links for an effective ranking of risks. Furthermore, results emphasize the valuable support of CPNs in risk factor modelling since they allow both a more structured and systematic risk analysis and a more accurate planning for effective risk treatment actions.

Modelling and assessing ERP project risks: A Petri Net approach

ALOINI, DAVIDE;DULMIN, RICCARDO;MININNO, VALERIA
2012

Abstract

Risk management of Enterprise Resource Planning (ERP) projects is largely recognized as a very complex task both by academics and practitioners. Strict interconnections among risk factors often occur so that indirect effects on the overall project performance are very likely. Unfortunately, the implications of interdependency are usually underestimated by project managers and decision makers since they are difficult to include in any risk assessment logic. This work shows how Colored Petri Nets (CPNs) can be used to model risk factors in ERP projects in order to deal with the problem of interdependence in risk assessment. The technique is presented through an application to a real case study. Findings highlight the importance of interdependence and the indirect links for an effective ranking of risks. Furthermore, results emphasize the valuable support of CPNs in risk factor modelling since they allow both a more structured and systematic risk analysis and a more accurate planning for effective risk treatment actions.
Aloini, Davide; Dulmin, Riccardo; Mininno, Valeria
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/199926
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