Big Data Analysis Proposal for Manufacturing Firm

Big Data Analysis Proposal for Manufacturing Firm

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Submitted Feb 2, 2021
Published Feb 15, 2021
DOI https://doi.org/10.24018/ejece.2021.5.1.298
Abstract viewed = 195 times
Issue
Vol. 5 No. 1 (2021)
Section
Articles

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  •   Alicia Valdez

    •    Autonomous University of Coahuila, Mexico
  •   Griselda Cortes

    •    Autonomous University of Coahuila, Mexico
  •   Laura Vazquez

    •    Autonomous University of Coahuila, Mexico
  •   Adriana Martinez

    •    Autonomus University of Coahuila, Mexico
  •   Gerardo Haces

    •    Autonomous University of Tamaulipas, Mexico

Abstract

The analysis of large volumes of data is an important activity in manufacturing companies, since they allow improving the decision-making process. The data analysis has generated that the services and products are personalized, and how the consumption of the products has evolved, obtaining results that add value to the companies in real time.


In this case study, developed in a large manufacturing company of electronic components as robots and AC motors; a strategy has been proposed to analyze large volumes of data and be able to analyze them to support the decision-making process; among the proposed activities of the strategy are: Analysis of the technological architecture, selection of the business processes to be analyzed, installation and configuration of Hadoop software, ETL activities, and data analysis and visualization of the results. With the proposed strategy, the data of nine production factors of the motor PCI boards were analyzed, which had a greater incidence in the rejection of the components; a solution was made based on the analysis, which has allowed a decrease of 28.2% in the percentage of rejection.


Keywords: Big data, Data analytics, Hadoop, Power BI

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How to Cite
[1]
Valdez, A., Cortes, G., Vazquez, L., Martinez, A. and Haces, G. 2021. Big Data Analysis Proposal for Manufacturing Firm. European Journal of Electrical Engineering and Computer Science. 5, 1 (Feb. 2021), 68–75. DOI:https://doi.org/10.24018/ejece.2021.5.1.298.