ADD ON COURSE
INTRODUCTION TO HR ANALYTICS
Modern organizations are ramping up on data science, recruiting talents by making huge investments for their work engagement. These firms are accelerating their digital transformation to deploy smart technologies around AI and big data to improve their talent management systems. In a world of work that is increasingly virtual (and perhaps even only virtual), the volume of data available to understand and predict employees’ behaviours will continue to grow exponentially, enabling more opportunities for managing through HR Analytics. As such, people analytics is a deliberate and systematic attempt to make organizations more evidence-based, talent-centric, and meritocratic, which, one would hope, should make them more effective.
Course Objective:
∙ Understand how HR and analytics have evolved and are transforming people management
around the world.
∙ Understand the role of HR Analytics and build skills to conduct a HR Analysis through
measuring staffing utility.
Course Outcomes:
At the end of the course, the student will be able to
CO1: Describe the role of descriptive and prescriptive analytics in HR analytics.
CO2: Explain the recent developments in decision based framework for staffing measurement.
CO3: Analyse the different utility models for taking staffing decisions.
CO-PO Mapping
| CO/PO |
PO1 |
PO2 |
PO3 |
PO4 |
PO5 |
| CO1 |
3 |
2 |
1 |
3 |
2 |
| CO2 |
3 |
2 |
1 |
3 |
2 |
| CO3 |
3 |
2 |
1 |
3 |
2 |
Course Delivery Methods
|
Lecture Mode |
Seminar |
Case studies |
Web References |
| CO1 |
√ |
√ |
√ |
√ |
| CO2 |
√ |
√ |
√ |
√ |
| CO3 |
√ |
√ |
√ |
√ |
Eligibility
Student should score 50% (30 Marks) & 80% attendance to be eligible for the certification Syllabus: (Total Hours Required – 30)
Module 1: HR Analytics: Analytics-Nature-Evolution of Human Capital Metrics-Steps in Analytics Role of Descriptive analytics & Prescriptive analytics in HR analytics – HR Analytics Frameworks: LAMP framework, HCM: 21 Framework& Talent ship Framework, Environmental scanning: The Big Picture-The value of statistical analysis-The importance of risk assessment, Predictive management
Module 2: Staffing Utility : Concept and Measures A Decision-Based Framework for Staffing Measurement, Overview: The Logic of Utility Analysis, Utility Models and Staffing Decisions, The Taylor-Russell Model, The Naylor-Shine Model, The Brogden- Cronbach- Gleser Model
Module 3: Absenteeism and Separation: Cost of Absenteeism – Direct Costs and the Incidence, Causes, Consequences, Categories of Costs, Analytics and Measures for Employee Absenteeism, Strategies to reduce absence, positive Incentives, Paid Time Off (PTO)
Module 4: Employee Turnover: Separations, Acquisitions, Cost, and Inventory, Voluntary Versus Involuntary Turnover, Functional Versus Dysfunctional Turnover, Pivotal Talent Pools with High Rates of Voluntary Turnover, Involuntary Turnover due to Dismissals and Layoffs, computing Turnover 15 rates, training cost, performance difference between separating employees and replacements, cost of lost productivity and lost business ,Promotion and succession planning analytics, Compliance analytics
Module 5: Employee Health Wellness and Welfare: Logic of Workplace Health Programs (WHP), Analytics for Decisions about WHP Programs, Measures: Cost Effectiveness, Cost-Benefit, and Return-on-Investment Analysis, Cost-Effectiveness Analysis, Cost Benefit and Return-on Investment Analysis, Employee Assistance Programs (EAPs) Future of Lifestyle Modification, WHP, and EAPs
References
- Wayne F. Cascio, John W. Boudreau, Investing in people: Financial Impact of Human Resource Initiatives, Pearson Education, New Jersey, US
- Tracey Smith, HR Analytics, The what, Why and How, 1e Create Space Independent Publishing Platform
- Laurie Bassie, Rob Carpenter: HR Analytics Handbook, Mc Bassi & Company; 1st paperback edition, Brooklyn ,US
- Jac Fitz-Enz, The New HR Analytics: Predicting Economic Value of Your Company’s Human Capital Investments. New York, NY: AMACOM.