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Role of Analytics for Operational Risk Management in the Era of Big Data

Journal(s): Decision Sciences
Published: June 1, 2020
Author(s): Özgür M. Araz, Tsan-Ming Choi, David L. Olson, F. Sibel Salman

General Description
Schools close, elderly populations isolate and businesses shut down due to the coronavirus pandemic. Dr. Özgür Araz, associate professor of supply chain management and analytics, believes decisions imposed to change the way we live in the short-term stem from critical research analysis now in the hands of government and health care providers.

Araz’s research examines decision sciences specifically related to health systems, like pandemic decision-making. He teaches predictive analytics and knows his work impacts lives in a time of crisis.

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Academic Abstract
Operational risk management (ORM) is critical for any organization, and in the big data era, analytical tools for operational risk management are evolving faster than ever. This paper examines recent developments in academic ORM literature from the data analytics perspective. We focus on identifying present trends in ORM related to various types of natural and man-made disasters that have been challenging all aspects of life. Although we examine the broader operations management (OM) literature, we keep the focus on the articles published in the well-regarded OM journals, including both empirical and analytical outlets. We highlight how the use of data analytics tools and methods have facilitated ORM. We discuss the need for data monitoring and the integration of various analytical tools into decision making processes by classifying the literature on application fields, analytics techniques, and the strategies used for implementation. We summarize our findings and propose a process to implement data-driven ORM with future research directions.

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