Multi-Dimensional Prediction Model for Estimating Software Project Implementation Outcomes from a Client Perspective in the Energy Sector
Open AccessIncreasing complexity in software systems and software project implementations results in 50% of software projects being over budget, late or lacking the required functionality, and causes organizations to incur financial losses of millions of dollars. This praxis presents a Multi-Dimensional Prediction Model (MDPM) by developing and comparing four machine learning models (Multiple Linear Regression, Decision Tree, Random Forest, Neural Network) to predict multi-dimensional software project implementation outcomes. The software project data set was obtained from a large-size Canadian organization in the Energy Sector that implements software projects in partnerships with external vendors. 102 project instances were identified for this praxis with ten Critical Success Factors (CSFs) as predictors. This praxis demonstrates how project sponsors can use the MDPM to support decision-making and cost benefit analysis to reduce the likelihood of failed projects. The final MDPM predicts, within a 20% margin of error, the schedule and cost contingencies required to manage project uncertainties and risks, and the number of system defects required to deliver a quality end product. The top five CSFs with the most significant influence on the output variables were Integration of the System, Project Base Cost, Project Base Schedule, Project Team Capability, and Top Management Support. Random Forest model was selected to be the most effective method in estimating multi-dimensional project outcomes.
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