Current Happenings

The Paari School of Business successfully concluded its session on career development. Mr. Suresh S, a seasoned HR professional with years of experience, engaged students with informative segments on career planning, acing job interviews, and enhancing communication skills.

During the career planning segment, attendees were reminded of the critical importance of completing their courses and were enlightened about the more rigorous elimination processes often found in larger companies. The seminar emphasised the need for clarity in one’s career goals, whether it involves pursuing higher studies, venturing into entrepreneurship, or exploring international opportunities. The resounding message was to “dream big” and strive for ambitious career aspirations.

The interview segment of the seminar provided illuminating insights into the rapid evaluation process that candidates face during job interviews. The seminar also delved into Maslow’s Theory of Management, emphasising the significance of mastering the fundamental principles. A major takeaway from the event was the emphasis on merit over recommendations, highlighting the importance of proving one’s capabilities through skills and qualifications.

The seminar also equipped attendees with practical tips for interview success. These included guidance on dressing appropriately, avoiding squeaky shoes, mastering the art of tying ties, punctuality, and conducting thorough research about the prospective employer. Participants were encouraged to create clear and concise resumes, carry extra copies of their resumes and testimonials, and ensure that their references were in order. The event underscored that the first impression often leaves a lasting mark, making preparation and presentation crucial.

Effective communication skills were another key focal point, with an emphasis on articulating strengths and weaknesses concisely and convincingly. The seminar was a comprehensive guide for attendees, equipping them with the knowledge and skills needed to navigate the complex job market successfully.

Socially relevant and community-centric research is of primal focus at SRM University-AP leading to innovations that transpire into transforming society. Dr Vimal Babu and his research scholar Ms Rukma R, Paari School of Business, have undertaken creative research to understand employee attrition in organisations critically. The research cohort employed a modified Random Forest Algorithm for assessing employee attrition and analysing its findings and benefits to Human Resource departments of corporate houses. Their groundbreaking invention and research titled “A System to generate a Model Predicting an Employee Attrition Rate and a Method Thereof” with Application No: 202341031320 has recently been filed and published under the Patent Office Journal.

Abstract

The problem of employee attrition in every organization concerns the employee turnover ratio, thereby increasing the cost of investment in human resources. Various factors are reasonable for the rapid attritions at different phases. The purpose of the current study is to predict the employees who are likely to leave the organization. The factors that lead to attrition are identified using the Random Forest algorithm. Random Forest algorithm is one of the widely used supervised machine learning technique for both classification and prediction. However, the random forest algorithm has specific issues, such as it is too slow and ineffective for real-time predictions. i.e., the large number of trees can make the algorithm, which results in a slower model. Therefore, the study proposes a new alternative for choosing the appropriate decision trees based on the concept of fractional factorial design of experiments. The different performance criteria were compared across the modified random forest algorithm, existing random forest algorithm, Support Vector Machine (SVM), Logistic Regression (LR), Navie Bayes (NB), K – Nearest Neighbour (K-NN), Decision Tree, XG Boost tree and Neural Network (NN). It was found that the modified random forest algorithm excelled in all criteria and performed better than the existing ones.

The findings of this study offer valuable assistance to human resources departments by providing necessary insights into potential employees’ decisions to leave the company. By analysing the identified factors or reasons for leaving the organisation, HR professionals can better understand attrition patterns and make informed decisions to improve employee retention. The proposed method not only assists in identifying the factors contributing to employee attrition but also predicts the likelihood of attrition based on employee signals. This predictive capability enables organisations to proactively address potential attrition cases and take preventive measures to retain valuable employees.

Practical implications of the research

Overall, the practical applications include designing retention strategies, predicting attrition cases, tailoring employee engagement initiatives, developing HR policies, optimising recruitment and selection processes, benchmarking attrition rates, and implementing continuous improvement measures. These applications can collectively contribute to reducing employee attrition and enhancing overall employee retention in organizations.

Collaborations and Future research plans

The applicants, viz. Ms Rukma, R.; Dr Vimal Babu and Dr Vijaya Prabhagar, M. plan to collaborate in the intersecting area of HR and analytics to arrest employee attrition by employing sophisticated analytics models and explore the future scope of our research to come up with impactful research outputs benefitting the industry.

Phases of prediction of employee attrition