Developing KPIs for Regulatory Affairs Team Performance Assessment

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Er Akshun Chhapola
Delhi Technical University 

Rohini, New Delhi, Delhi,  India 110042 

akshunchhapola07@gmail.com

Abstract

Effective performance assessment of Regulatory Affairs (RA) teams is critical for ensuring compliance, accelerating product approvals, and optimizing resource allocation within pharmaceutical and biotechnology organizations. This manuscript proposes a comprehensive framework for developing key performance indicators (KPIs) tailored to RA team activities. Drawing on organizational theory, performance management literature, and regulatory science, we identify dimensions of RA performance—timeliness, quality, efficiency, and stakeholder engagement—and derive measurable KPIs for each. We apply a mixed-methods approach, combining qualitative expert interviews with quantitative analysis of historical project data from three mid-size pharmaceutical firms. Statistical analysis of KPI outcomes is presented to validate indicator reliability and sensitivity. Our findings demonstrate that a balanced KPI suite, integrating process, outcome, and relational metrics, yields actionable insights for continuous improvement. The proposed methodology and validated KPI set can guide RA leaders in benchmarking performance, identifying areas for training, and aligning regulatory activities with strategic business objectives. Recommendations for implementation, periodic review, and limitations are discussed. Additionally, we explore how advanced analytics and dashboard technologies can embed these KPIs in real-time monitoring systems, fostering a culture of proactive governance and iterative refinement within RA functions.

Keywords

 Regulatory Affairs, KPIs, performance assessment, pharmaceutical compliance, process improvement

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• Vardhan Akisetty, Antony Satya Vivek, Ashvini Byri, Archit Joshi, Om Goel, Dr. Lalit Kumar, and Prof. Dr. Arpit Jain. 2024. “Improving Manufacturing Efficiency with Predictive Analytics on Streaming Data.” International Journal of Progressive Research in Engineering Management and Science 4(6):2528-2644. https://www.doi.org/10.58257/IJPREMS35036.
• Bhat, Smita Raghavendra, Rakesh Jena, Rajas Paresh Kshirsagar, Om Goel, Arpit Jain, and Punit Goel. 2024. “Developing Fraud Detection Models with Ensemble Techniques in Finance.” International Journal of Research in Modern Engineering and Emerging Technology 12(5):35. https://www.ijrmeet.org.
• Bhat, S. R., Ayyagari, A., & Pagidi, R. K. (2024). “Time Series Forecasting Models for Energy Load Prediction.” Journal of Quantum Science and Technology (JQST), 1(3), Aug(37–52). Retrieved from https://jqst.org/index.php/j/article/view/89.
• Bhat, Smita Raghavendra, Aravind Ayyagari, Ravi Kiran Pagidi, Dr. S P Singh, Prof. (Dr.) Sandeep Kumar, and Shalu Jain. 2024. “Optimizing Cloud-Based SQL Query Performance for Data Analytics.” International Journal of Worldwide Engineering Research 02(11):285-301.
• Abdul, Rafa, Arth Dave, Rahul Arulkumaran, Om Goel, Lalit Kumar, and Arpit Jain. 2024. “Impact of Cloud-Based PLM Systems on Modern Manufacturing Engineering.” International Journal of Research in Modern Engineering and Emerging Technology 12(5):53. https://www.ijrmeet.org.
• Abdul, R., Khan, I., Vadlamani, S., Kumar, D. L., Goel, P. (Dr) P., & Khair, M. A. (2024). “Integrated Solutions for Power and Cooling Asset Management through Oracle PLM.” Journal of Quantum Science and Technology (JQST), 1(3), Aug(53–69). Retrieved from https://jqst.org/index.php/j/article/view/90.