A CONCEPTUAL FRAMEWORK FOR FORECASTING ERP IMPLEMENTATION SUCCESS A first step towards the creation of an implementation support tool

摘要

The continuing soar in popularity when it comes to standardized information systems sold en masse under the labelling of Enterprise Resource Planning (ERP) Systems is somewhat kept under control by the ever flowing stream of reports from the industry of implementations gone bad. According to some researchers it is possible to assume that as many as 90% of all initiated ERP implementation projects can be regarded as failures as a result of changes in scope, prolongation of the project time or simply budget overruns. With the implementation of an ERP system being a very costly and risky endeavour, organizations considering "getting on the bandwagon" stand much to gain from pre-emptively forecasting the probability of success for an ERP implementation in their enterprise. Given this, the purpose of this paper is to investigate a possible conceptual framework for forecasting ERP implementation success and discuss the role of such a framework in a software based tool. This was achieved through an initial in-depth literary review aimed at finding factors affecting the outcome of the ERP implementation projects. These results were then communicated to an industrial support group comprised of possible ERP implementation stakeholders. After lengthy discussions concerning the usability, validity and reliability of the proposed list of factors, a conceptual framework was agreed upon for forecasting ERP implementation success. The framework was then tested against a number of possible stakeholders outside the industrial support group. As the results show we have been able to create a conceptual framework for forecasting ERP implementation success that is currently in the second wave of testing. The usability, validity and reliability of the framework is discussed and elaborated upon, and this paper concludes that the perceived usability and hence also value of the conceptual framework is substantial, whereas the validity and reliability remain to be tested.