ICF was engaged by the Office of Fraud Detection and National Security (FDNS) to conduct an analysis of the current workload and structure to assist in making decisions regarding staffing levels for field operations spanning more than 70 offices across the U.S. The ultimate objective was to develop a staffing model to enable FDNS management to forecast staffing needs based on varying workload.
The U.S. Bureau of Citizenship and Immigration Service (USCIS) oversees the lawful immigration to the United States, including the adjudication of petitions and applications of potential immigrants. FDNS supports the USCIS mission by detecting, deterring, and combating immigration benefit fraud, and strengthening efforts to ensure immigration benefits are not granted to persons who pose a national security or public safety threat.
ICF conducted interviews with managers and subject matter experts from FDNS offices across the country to identify core work products, processes, and drivers of workload and workload variance. ICF then analyzed existing data from recent case processing to determine the effects of variance in the data. A time-to-performance model was developed that used average processing times collected across a representative sample of offices.
Using the information gathered through interviews and data analysis, ICF then created staffing-to-workload decision tools in Microsoft Excel that modeled fraud and national security cases across three different office types. These models examine the hours required to process approximately 15 different types of cases. These decision tools allow for the creation of staffing estimates by individual office types based upon current and projected case loads specified in total staff hours and full-time-equivalent staff positions. When paired with management direction on policy priority, process improvements, and performance objectives, these models can be used to accurately estimate the staffing levels needed to respond to varying workload demands.
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