The Use of Big Data Analytics in Operations Research: Challenges and Opportunities

Authors

  • Vidhi Dhyani

Abstract

Organizations are producing exponentially more data as a result of the development of technology and data. This big data presents a significant opportunity for organizations to gain insights and optimize their operations, particularly in the field of operations research (OR). Big data analytics (BDA) is a rapidly growing field that focuses on the use of advanced techniques and technologies to extract insights from large and complex datasets. The integration of BDA into OR has the potential to revolutionize the field, providing organizations with more accurate and comprehensive insights into their operations. Big data analytics' primary goal is to transform enormous amounts of raw data into actionable insights that businesses can use to enhance their operations and make better decisions. As the volume of data generated by organizations continues to grow, big data analytics is likely to become even more critical to the success of OR initiatives. Big data analytics is a critical component of the field of operation research (OR) because it allows organizations to make more informed decisions based on real-time data and insights. However, there are also several challenges associated with the use of BDA in OR, including the quality and availability of data and the complexity of BDA algorithms. In this study, we review the key applications of BDA in OR, the challenges associated with its use, and the future research directions in this field.

References

Gandomi AH, Haider M. Beyond the hype: Big data concepts, methods, and analytics. Int J Inf Manag. 2015; 35(2): 137–144.

Kelleher CD, Mac Namee B, D’Arcy A. Big data: A review of the state-of-the-art. In: Future Data and Security Engineering. Springer; 2015; 1–12.

Liu Y, Chen H. Big data: A survey. Mob Netw Appl. 2016; 21(2): 171–209.

Wirth R, Hipp C. Data warehousing and OLAP: concepts, tools, and applications. ACM SIGMOD Rec. 2000; 29(2): 65–74.

Chen H, Chiang RHL, Storey VC. Business intelligence and analytics: From big data to big impact. MIS Q. 2012; 36(4): 1165–1188.

Baldoni R, Fattore A. Big data analytics in healthcare: A review of the state of the art and future trends. Healthc Technol Lett. 2017; 4(3): 60–68.

Gartner. (2017). What is big data? Gartner IT Glossary. [Online].

Zhang Y, Zhao Y. Astronomy in the big data era. Data Science Journal. 2015 May 22;14.

Zhang X, Liu J, XuD. Big data: A survey. Mob Netw Appl. 2015; 20(2): 187–203.

LiJ, Li J, Liu X. Big data: A survey. Mob Netw Appl. 2015; 20(6): 1085–1095.

Ram J, Zhang C, Koronios A. The implications of big data analytics on business intelligence: A qualitative study in China. Procedia Comput Sci. 2016 Jan 1; 87: 221–6.

Ittmann HW. The impact of big data and business analytics on supply chain management. J Transp Supply Chain Manag. 2015 Jan 1; 9(1): 1–9.

Jeble S, Dubey R, Childe SJ, Papadopoulos T, Roubaud D, Prakash A. Impact of big data and predictive analytics capability on supply chain sustainability. Int J Logist Manag. 2018 May 14; 29(2): 513–38.

Ciampi F, Demi S, Magrini A, Marzi G, Papa A. Exploring the impact of big data analytics capabilities on business model innovation: The mediating role of entrepreneurial orientation. J Bus Res. 2021 Feb 1; 123(6): 1–13.

Garmaki M, Boughzala I, Wamba SF. The effect of Big Data Analytics Capability on Firm Performance. In PACIS. 2016 Jun 27; 301

Published

2023-05-15

How to Cite

Vidhi Dhyani. (2023). The Use of Big Data Analytics in Operations Research: Challenges and Opportunities . NOLEGEIN-Journal of Human Resource Management &Amp; Development, 5(2), 1–4. Retrieved from https://mbajournals.in/index.php/JoHRMD/article/view/1031