Analyzing Relationships Between Success Factors of Project Management in Global Software Development

This study investigates the effectiveness of Abrasive Water Suspension JIn current era of technological advancement, global software development (GSD) is expanding to an increasing extent. This rise is driven by the advantages of low-cost development, access to expert resources, proximity to markets and clients, as well as options for innovation and the exchange of best practices. However, the various key aspects of GSD, including geographical dispersion, work ethics, time zone differences and cultural variability make project management more complex in a GSD setting. This study harnesses the critical success factors (CSFs) approach to identify the model for successful management of GSD projects. Sixteen CSFs have been determined using literature review, expert judgement, and exhaustive interviews. The interrelationship among the identified CSFs has been modeled using interpretive structural modeling (ISM) and cross-impact matrix multiplication applied to classification (MICMAC) analysis. The proposed model has been validated using detailed interviews, conducted with twelve professionals from industry and academia. The expert participants were carefully chosen based on criteria which include working experience, roles, and participation in GSD projects. This research computes CSFs of software project management in GSD by assessing their driving and relative strengths. Furthermore, these CSFs are categorized using MICMAC analysis. The previous research studies lack in exploring the mutual influence of CSFs and their hierarchical relationships. With the application of ISM and MICMAC analysis, the proposed study offers understanding of these interdependencies, providing great insights into their driving and dependent function. These insights enhance knowledge in GSD project management by allowing practitioners to prioritize key criteria and formulate potent management strategies.et (AWSJ) Machining, a non-conventional erosion-based method, for machining carbon fiber-reinforced polymer (CFRP) composites. The focus was on analyzing key process parameters—abrasive size, feed rate, and standoff distance (SOD)—under submerged cutting conditions and their impact on material removal rate (MRR), kerf width, and surface roughness. Experimental trials were conducted, and advanced computational techniques, including Response Surface Methodology (RSM), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Artificial Neural Networks (ANN), were used for parameter optimization and predictive analysis. The results showed that submerged cutting significantly improved machining quality by reducing surface roughness and ensuring uniform kerf widths. Increasing the jet diameter in underwater conditions stabilized the nozzle, leading to smoother and more precise cuts. Among the predictive models, XGBoost demonstrated the highest accuracy and efficiency in forecasting MRR, while Random Forest and ANN provided competitive performance. The integration of RSM and machine learning (ML) techniques enabled effective optimization of machining parameters, showcasing the potential for cost-effective and high-precision CFRP machining. These findings are particularly relevant for industries like aerospace and automotive, where machining efficiency and precision are crucial.