Defense Date
4-10-2018
Graduation Date
Spring 5-11-2018
Availability
Immediate Access
Submission Type
thesis
Degree Name
MS
Department
Computational Mathematics
School
McAnulty College and Graduate School of Liberal Arts
Committee Chair
John Kern
Committee Member
Frank D'Amico
Keywords
Performance Management, Performance Measurement, Performance Metrics, Performance, Employee Performance
Abstract
Performance Measurement is an essential discipline for any business. Robust and reliable performance metrics for people, processes, and technologies enable a business to identify and address deficiencies to improve performance and profitability. The complexity of modern operating environments presents real challenges to developing equitable and accurate performance metrics. This thesis explores and develops two new methods to address common challenges encountered in businesses across the world. The first method addresses the challenge of estimating the relative complexity of various tasks by utilizing the Pearson Correlation Coefficient to identify potentially over weighted and under weighted tasks. The second method addresses the challenge of determining performers' influence on a metric by treating performance rankings as vectors and evaluating the change of the vector over multiple performance periods.
Language
English
Recommended Citation
Benis, J. D. (2018). Re-Evaluating Performance Measurement: New Mathematical Methods to Address Common Performance Measurement Challenges (Master's thesis, Duquesne University). Retrieved from https://dsc.duq.edu/etd/1427
Included in
Computational Engineering Commons, Mathematics Commons, Operations and Supply Chain Management Commons, Statistics and Probability Commons