Machine learning-based impact assessment of ERP system implementation in business process re-engineering

Abstract

Purpose - The purpose of this study is to predict the impact of Enterprise Resource Planning (ERP) implementation on Business Process Reengineering (BPR) in Sri Lankan organizations using machine learning algorithms. By applying the CRISP-DM framework, the research aims to develop and evaluate a predictive model that identifies patterns and relationships between ERP implementation and business process changes, providing actionable knowledge for improving organizational processes. Methodology - The study will apply the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework, integrating data collection, preprocessing, model development, and evaluation to effectively structure and analyze the impact of ERP implementation on BPR in Sri Lankan organizations. Machine learning algorithms will be employed to develop a predictive model, which will be refined and assessed for accuracy and reliability to achieve the research objectives. Findings - This study analyzes the impact of ERP implementation on BPR in Sri Lankan organizations using the developed model and multiple machine learning techniques. The results indicate that machine learning techniques can effectively predict ERP implementation outcomes, with varying accuracy depending on the algorithm used. Notably, Naive Bayes and Decision Tree models demonstrated high classification accuracy, particularly for responses indicating strong agreement on ERP’s impact on BPR. The Gradient Boosting Machines (GBM) model further reinforced these findings, exhibiting exceptional predictive capability, as evidenced by its near-perfect R-squared value. These findings suggest that ERP implementation significantly influences process transformation by streamlining operations, enhancing efficiency, and achieving reengineering goals. The strong predictive performance of GBM confirms the reliability of these assessments, making it a valuable tool for evaluating ERP impact on business processes. Potential Implications - This study will facilitate informed decision-making and strategic planning regarding ERP implementation and BPR, thereby enriching the body of knowledge in this field. The findings will serve as a valuable resource for practitioners, managers, and researchers in enhancing ERP implementations and driving successful business process transformations.