ERP Project Failure Prediction Using Machine Learning Algorithms

Enterprise Resource Planning (ERP) systems are of immense importance in simplifying business operations. However, most ERP projects fail owing to the complexity and scope of the projects. The present research attempted to determine the outcomes of ERP projects by employing machine learning methods and addressing factors which determine whether projects fail or succeed. This dissertation obtained data from different aspects of the projects that included successful and unsuccessful ERP deployments in terms of within which industry, project magnitude, the level of budget and time exceeding, background of team experience as well as technical challenges faced amongst others.
The research includes machine learning methods such as logistic regression, decision trees, and random forests in order to assess the importance of the relevant predictors of any project. By training and testing these applications on a sample composed of both successful and non-successful ERP projects, the goal of the model is to seek for factors and patterns which could help in forecasting troubling tendencies. This research is aimed at devising a functional framework that can be used by project managers, enabling them to take action before issuing their project plans for ERP systems. Such a predictive model could significantly help in decreasing the rates of ERP failures and hence assist businesses in carrying out successful implementations and enhancing their returns on technology investment.