In recent years, over 100,000 untested sexual assault kits (SAKs) have been discovered in the custody of law enforcement and awaiting testing at publicly funded facilities. The backlog has been attributed to law enforcement discretion and a lack of resources at testing facilities. In response to the backlog, states have adopted various policies for testing SAKs. 11 states have adopted comprehensive policies, mandating testing of all backlogged and incoming kits, in addition to annual inventories, updated tracking systems, and increased funding for testing programs. 30 states have adopted “limited” policies and 4 states have proposed policy reforms. This research attempts to develop a process-modeling approach to SAK testing through a case study with Monroe County Crime Laboratory (MCCL). A model was created using Simio, a discrete-event simulation software, to assess the current state of the testing process within the facility, which was then modified to analyze various staffing levels and relative demand levels probabilistically determined by uniform distribution. This experiment resulted in 15 acceptable scenarios, given the New York State 90-day testing mandate for incoming SAKs, and a number of policy recommendations. Based on this research, jurisdictions with extremely low demand (relative demand < 3) are not recommended to assign full-time staff solely to SAK testing because all scenarios result in low worker utilization levels. There are also no scenarios which yields acceptable worker utilization for a staff of 10 or more full-time or full-time equivalent employees, assuming no change in resources or technology. Overall, process modeling is a useful tool for analyzing the allocation of resources and demand constraints for SAK testing facilities.

Library of Congress Subject Headings

Rape--Investigation--Government policy--United States; Evidence preservation--United States; Evidence, Criminal--United States; Forensic genetics--United States; Workflow--Computer simulation

Publication Date


Document Type


Student Type


Degree Name

Science, Technology and Public Policy (MS)

Department, Program, or Center

Public Policy (CLA)


Franz Foltz

Advisor/Committee Member

Michael Kuhl

Advisor/Committee Member

Qing Miao


RIT – Main Campus

Plan Codes