Defense Date

4-10-2018

Graduation Date

Spring 5-12-2018

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

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