Application of Big Data Analytics in Manufacturing Sector - A Review



EOI: 10.11242/viva-tech.01.05.001

Download Full Text here



Citation

Mansi Lakhani,"Application of Big Data Analytics in Manufacturing Sector - A Review", VIVA-IJRI Volume 1, Issue 5, Article 64, pp. 1-6, 2022. Published by Computer Engineering Department, VIVA Institute of Technology, Virar, India.

Abstract

The period of the fourth industrial revolution, called Industry 4.0, is characterized by new, innovative technologies such as: Cloud Computing; the Internet of Things; the Industrial Internet of Things; Companies that are able to analyze the current state of their processes, forecast its most optimal progress and proactively control them based on reliable predictions will be a decisive step ahead competitors. In terms of Industry 4.0, data analytics focus on “what will happen” rather than “what has happened”. These problems are entitled as predictive analytics and aims at building models for forecasting future possibilities or unknown events. The aim of this paper is to give detailed insight about these techniques, provide applications from the literature and present how big data analytics can change the dynamics of manufacturing sector across various functions.

Keywords

Big Data, Data Analytics, Industry 4.0, Artificial Intelligence, Machine learning.

References

  1. “Krumeich, J., Werth, D. & Loos, P., 2016. Prescriptive control of business processes. Business & Information Systems Engineering, 58(4), pp. 261-280.
  2. "Beneventi, F., Bartolini, A., Cavazzoni, C. & Benini, L., 2017. Continuous learning of HPC infrastructure models using big data analytics and in-memory processing tools. Design, Automation & Test in Europe Conference & Exhibition (DATE), pp. 1038-1043.
  3. "Chen, H., Chiang, R. H. L. & Storey, V. C., 2012. Business Intelligence and Analytics: From Big Data to Big Impact. MIS Quarterly, 36(4), pp. 1165-1188
  4. "Zhang, H. et al., 2017. Progress in Aluminum Electrolysis Control and Future Direction for Smart Aluminum Electrolysis Plant. JOM, 69(2), p. 292–300.
  5. "Zhuchkov, R. N., 2015. Application of predictive control approach in stabilizing control design of networked plants. Automation and Remote Control, 76(9), pp. 1704-1712.
  6. "Chongwatpol, J., 2016. Managing Big Data in Coal-Fired Power Plants: A Business Intelligence Framework. Industrial Management & Data Systems, 116(8), pp. 1779-1799.
  7. "Li, Y. & Kashiwagi, H., 2005. High-order Volterra Model Predictive Control and its application to a nonlinear polymerisation process. International Journal of Automation and Computing, 2(2), pp. 208-214.
  8. "Weese, M., Martinez, W., Megahed, F. M. & Jones-Farmer, L. A., 2016. Statistical learning methods applied to process monitoring: An overview and perspective. Journal of Quality Technology, 48(1), pp. 4- 24.
  9. "Stanley, G., 2018. Big Data Approximating Control (BDAC)—A new model-free estimation and control paradigm based on pattern matching and approximation. Journal of Process Control, Volume 67, pp. 141- 159.
  10. "Wang, C.-H., Cheng, H.-Y. & Deng, Y.-T., 2018. Using Bayesian belief network and time-series model to conduct prescriptive and predictive analytics for computer industries. Computers & Industrial Engineering, Volume 115, pp. 486-494.