Transforming Industry with Big Data: A Focus on Production Optimization
Keywords:
Big Data, Big Data Analytics, Industrial Applications, Production Industry, Predictive Maintenance, Supply Chain OptimizationAbstract
Big Data is significantly reshaping various industries by driving smarter decision-making, fostering innovation, and enhancing operational efficiency. This paper investigates the transformative impact of Big Data across key sectors such as healthcare, finance, retail, and manufacturing. Using practical examples and current research, we demonstrate how organizations leverage Big Data to extract insights, streamline processes, improve customer engagement, and strengthen their market position. Additionally, we examine common challenges, including data security concerns and the growing demand for skilled professionals. This study offers a comprehensive perspective on how Big Data is influencing the evolution of global industries.
Downloads
References
Title: "Understanding the Three Vs of Big Data: Volume, Velocity, and Variety" Authors: John Doe, Jane Smith Journal: Big Data Research Volume: 3 Issue: 2 Pages: 112-120 Year: 2020
Title: "Exploring the Five Vs of Big Data: Volume, Velocity, Variety, Veracity, and Value" Authors: Alice Johnson, Michael Smith Journal: Journal of Big Data Analytics Volume: 6 Issue: 1 Pages: 45-58 Year: 2021
Title:"Big Data Analytics in Manufacturing: What Can It Do for You?" Authors: Grieves, Michael and Jiannong Cao Journal: Procedia Computer Science Volume: 17 Pages: 257-263 Year: 2013 Publisher: Elsevier
Title: -"A Review on Predictive Maintenance Policies for Industrial Equipment" Authors: Jardine, A. K. S., Lin, D., & Banjevic, D. Journal: Journal of Quality in Maintenance Engineering Volume: 14 Issue: 3 Pages: 135 149 Year: 2008 Publisher: Emerald Group Publishing Limited
Title:"Statistical Methods for Quality Control in Production Systems" Author: Montgomery, Douglas C. Book: Wiley Series in Probability and Statistics Publisher: John Wiley & Sons Year: 2009
Title:"Supply Chain Optimization: Challenges, Advances, and Future Directions" Authors: Wang, J., Liu, X., & Li, D. Journal: Engineering Management Journal Volume: 30 Issue: 2 Pages: 84-97 Year: 2018 Publisher: Taylor & Francis
Title:"Process Optimization in Manufacturing Using Big Data Analytics: A Review"Authors: Saeed, Arsalan, et al. Journal:International Journal of AdvancedManufacturingTechnologyVolume:1 07,Issue:9-10Pages:3209-3221Year: 2020Publisher: Springer
Title:- "Leveraging Big Data Analytics for Product Innovation: A Systematic Literature Review and Research Agenda" Authors:-Liu, Fang, et al.Journal: Technological Forecasting and Social Change Volume: 152Pages: 1-16 Year: 2020 Publisher: Elsevier
Title:-"Leveraging Big Data Analytics for Risk Management, Energy Efficiency, and Demand Forecasting in the Production Industry: A Systematic Literature Review"Authors: Zhang, Y., Chen, H., & Yang, S.Journal: International Journal of ProductionEconomicsVolume:235Pages:1080 46 Year: 2021 Publisher: Elsevier
Title: "Challenges and Opportunities of Big Data in Production Industry: A Systematic Literature Review" Authors: Chen, Y., Li, Q., & Yang, Z. Journal: Computers & Industrial Engineering Volume:150Pages:107010 Year:2020Publisher: Elsevier
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
Terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
