• Rohit Katkar
• Dr. Rajesh Buktar

In the fourth industrial revolution, smart manufacturing is reshaping the manufacturing in the industries. The use of IoT technologies facilitates machine-to-machine communication and the flow of information from source to system, which results in a vast amount of machine data getting generated. Using this data enterprises can gain actionable insights & make better data-driven decisions. Big Data Analytics is one of the emerging & advanced analytic industry4.0 technologies, which help to process large & diverse data sets. Today’s competitive environment forces enterprises to process this high-speed data & drive new opportunities. This paper starts with the definition and characteristics of big data, its sources, and its format. Further, it gives a brief explanation of the Hadoop ecosystem which helps us to understand a suite of services available on the cloud to solve big data problems. Big data technologies provide an opportunity to deploy predictive maintenance. Hence, in this paper, we have leveraged big data analytics for performing predictive analytics using a public dataset. Graphs have been plotted for various variables which help to bring visibility in the frequency of failure in advance & hence save the unplanned downtime. These graphs help to understand the range of any variable from which their machine component fails. In the end, an overview of the benefits of big data analytics in manufacturing has also been presented.

Big data, big data analytics, Hadoop ecosystem, Spark, Machine learning, predictive maintenance.

"Big Data and Predictive Analytics in Manufacturing Enterprises for Enhanced Decision Making", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 9, page no.b374-b384, September-2021, Available :http://www.jetir.org/papers/JETIR2109144.pdf

"Big Data and Predictive Analytics in Manufacturing Enterprises for Enhanced Decision Making", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 9, page no. ppb374-b384, September-2021, Available at : http://www.jetir.org/papers/JETIR2109144.pdf