A framework of Fuzzy Neural Network expert system for risk assessment of ERP projects

Risk is the potential for realization of undesirable consequences of an event. In other words, risk can be defined as the threat or probability that an action or event will adversely or beneficially affect an organization's ability to achieve its objectives. Implementation of Enterprise Resource Planning (ERP) projects are always accompanied by various risks and because of high rate of failure in such projects, managing of the risks in order to neutralize or at least decrease their effects on the success of the project is strongly essential. In this paper it is introduced a two stage Fuzzy Neural Network (FNN) which includes expertise to evaluate risk of ERP projects implementation. 18 risk factors are concluded from literature and for each of them two linguistic variables are considered: the probability of failure and the severity of impact. These variables are the inputs of first stage of the proposed FNN. The outputs of first stage, which are also the inputs of second stage, are values of ERP risk factors. Eventually, output of second stage is total risk of implementation of ERP project and it is derived from a feed-forward neural network. The architecture of the proposed framework and the development procedure are discussed, and numerical examples are provided.