Abstract

It has been observed in recent research that Human Capital Management (HCM) is an important function that is getting much attention from analytics. One of the touch points in the HCM life cycle is the people development with effective recommendations and timely interventions. Several models cited in research use models built with both supervised and unsupervised approaches. In this study, a framework based on self-organizing neural network model and linguistic modeling using fuzzy logic is proposed for utilization and intervention. This hybrid approach enables building rules/functions for different groups of human resources separately. In the first stage, employees are segmented into clusters, that are characterized by similar features and then, in the second step, for each group, fuzzy logic is used to obtain rules that may provide profile for each segment. The main advantage of applying the integration of two techniques consists of building models that, may better profile and predict the human capital requirements better, than using each method separately. The results are compared with the results of the work available in literature. The results indicate that the proposed approach provides an alternative view of the insights.

Presented and Published in: Proceedings of the Sixth International Conference on Business Analytics and Intelligence, December 2018, IISc, India.