Masters Theses

Date of Award


Degree Type


Degree Name

Master of Science


Industrial Engineering

Major Professor

Rupy Sawhney

Committee Members

Joseph Wilck, Gregory A. Sedrick


Every organization has to deal with planning of the appropriate level of human resources over time. The workforce is not always aligned with the requirements of the organization and it increases an organization’s budget. A literature review reveals that there is no model that can systematically predict accurate human resource required within a complex organization. To address this gap, a human resource predictive model was developed based on material requirements planning (MRP). This approach accounts for complexity in workforce planning and generalized it with a logistic regression model. The model estimates the employee turnover number and forecasts the expected remaining headcount for the next time period based on employee information such as; age, working year, salary, etc. Moreover, external variables and economic data can be utilized to adjust the estimated turnover probability. This model also suggests the possible internal workforce movement in case of in-house manpower imbalance.

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