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

Ozgur M. Araz, Tsan Ming Choi, David L. Olson, F. Sibel Salman

Research output: Contribution to journalArticle

6 Scopus citations

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.

Original languageEnglish (US)
JournalDecision Sciences
DOIs
StateAccepted/In press - 2020

Keywords

  • Analytics
  • Big Data
  • Operations
  • Risk
  • Supply Chains

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Strategy and Management
  • Information Systems and Management
  • Management of Technology and Innovation

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