Improving process performance management by predicting employee attrition in international company

Abstract

Employee attrition has become significant problem, because it affects organization’s process performance, and generates both – financial and business management loss. There is a tendency of growing employee attrition rate, and this is a reason why solution should be taken to lower it. It might be possible to lower employee attrition rate and thus to find a solution to this issue by predicting employee attrition and taking proactive actions for potential leavers. For employee attrition prediction, machine learning techniques and algorithms might be used. Machine learning algorithms makes predictions, using information learned from historical data, for this reason those predictions are more accurate, than just intuition-based predictions. There exist variety of different machine learning algorithms, hence it should be chosen depending on its reliability. Decision Tree algorithm was discovered to be the most reliable (97.4% accuracy) for predictive model in this study. However, having the predictive model itself doesn’t mean, that employee attrition problem will be solved. Model, as a tool should be integrated into organization’s business process management, to ensure continuity. The appropriate framework ensuring this continuity could be process performance management, which enables process measurement, control, and it helps to achieve better results in an organization by setting up KPIs. KPIs is the crucial part of process performance management because it aligns business activities with strategy. Therefore, a strategic employee attrition solution should be connected to one of organization’s KPI, to reach the target and ensure continuity.