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Open Access Journal Article

Predictive Analytics for Business Operations

by Daniel Anderson 1,*
1
Daniel Anderson
*
Author to whom correspondence should be addressed.
ORS  2022 4(1):28; https://doi.org/10.69610/j.ors.20220414
Received: 24 February 2022 / Accepted: 24 March 2022 / Published Online: 14 April 2022
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Abstract

This paper explores the application of predictive analytics in enhancing business operations. With the rapid advancements in technology, predictive analytics has emerged as a crucial tool for businesses seeking to optimize their processes and decision-making. The study aims to investigate how predictive models can be utilized to forecast future trends, anticipate customer needs, and streamline operational workflows. By integrating historical data, machine learning algorithms, and statistical techniques, predictive analytics enables businesses to gain valuable insights and make informed decisions. The paper discusses various predictive analytics models, their effectiveness in different sectors, and the challenges faced in implementation. It also highlights the importance of data quality, collaboration between departments, and ethical considerations when deploying predictive analytics in business operations. The study concludes that predictive analytics holds immense potential to transform the way businesses operate, providing competitive advantages and driving growth.

Keywords: Predictive analytics; Business operations; Machine learning; Data forecasting; Decision-making; Competitive advantage.;
Copyright: © 2022 by Anderson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Cite This Paper
APA Style
Anderson, D. (2022). Predictive Analytics for Business Operations. Operations Research and Statistics, 4(1), 28. doi:10.69610/j.ors.20220414
ACS Style
Anderson, D. Predictive Analytics for Business Operations. Operations Research and Statistics, 2022, 4, 28. doi:10.69610/j.ors.20220414
AMA Style
Anderson D. Predictive Analytics for Business Operations. Operations Research and Statistics; 2022, 4(1):28. doi:10.69610/j.ors.20220414
Chicago/Turabian Style
Anderson, Daniel 2022. "Predictive Analytics for Business Operations" Operations Research and Statistics 4, no.1:28. doi:10.69610/j.ors.20220414

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ACS Style
Anderson, D. Predictive Analytics for Business Operations. Operations Research and Statistics, 2022, 4, 28. doi:10.69610/j.ors.20220414
AMA Style
Anderson D. Predictive Analytics for Business Operations. Operations Research and Statistics; 2022, 4(1):28. doi:10.69610/j.ors.20220414
Chicago/Turabian Style
Anderson, Daniel 2022. "Predictive Analytics for Business Operations" Operations Research and Statistics 4, no.1:28. doi:10.69610/j.ors.20220414
APA style
Anderson, D. (2022). Predictive Analytics for Business Operations. Operations Research and Statistics, 4(1), 28. doi:10.69610/j.ors.20220414

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