A Proposed System-Level Performance Modeling Framework for Service Industries with Access to Limited Data Open Access
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Business process optimization allows businesses to remain competitive by allowing management to observe, analyze, and improve the operations of the organization. Using data and related business insights developed through applied analytic disciplines to drive business process optimization allows for fact-based planning, decisions, execution, management, measurement, and learning. However, this luxury is not possible for all; terms such as efficient, proactive, and effective are not generally used to describe the service industry, especially regulatory agencies. This analysis aims to provide a framework that leverages the data that is readily accessible in service industries to quickly make insightful decisions, despite their data limitations. The proposed framework uses system dynamics, discrete event simulation, and queuing theory to provide a system-level data-driven strategy that can be utilized to identify bottlenecks in the current processes of service industries, serve as an aid to help management visualize where process hindrances are occurring, and assist management in investigating solutions that improve process efficiency. Publicly available data from the medical product review centers within the Food and Drug Administration was used to successfully illustrate the framework.