A study on data mining tools directed towards modern day automobile industries. (22nd June 2022)

• Record Type: Journal Article
• Authors: Sriraam, Anirudh Ganesh
• Is Part Of: International journal of business and data analytics. Volume 2:Number 1(2022)
• Page Start: 1
• Page End: 19
• Publication Date: 2022-06-22
• Subjects: Apriori algorithm -- multiple regression -- process optimisation -- business intelligence -- data analytics -- suspected operational causes -- quality improvement -- downtime reduction; Commercial statistics -- Data processing -- Periodicals; Industrial management -- Mathematical models -- Periodicals; Business -- Mathematical models -- Periodicals; Management -- Statistical methods -- Periodicals; Business -- Research -- Periodicals; 658.403
• Languages: English
• ISSNs: 2515-9100

Abstract

The genesis of Industry 4.0 has brought with it a plethora of opportunities to use big data analytics in the manufacturing sector. The customer's increasing demand for customisability has led to increasingly complex manufacturing layouts. As most of the work in major manufacturing plant is done using robots, there is a gamut of sources of data. This data has never been utilised to its full potential. It has been used to monitor the status of production mostly and has helped in ad hoc maintenance scenarios. The purpose of this paper is to elucidate upon certain ways to increase efficiency of a big manufacturing plant using methods like data mining association rules and multiple regression. In addition, this paper can be referred to as a detailed tutorial on how to tackle huge datasets incoming from a large automobile manufacturing organisation and all the factors that need to be taken in consideration.