Big Data Collection and Analysis for Manufacturing Organisations

Pankaj Sharma (Corresponding Author), David Baglee, Jaime Campos, Erkki Jantunen

    Research output: Contribution to journalArticleScientificpeer-review


    Data mining applications are becoming increasingly important for the wide range of manufacturing and maintenance processes. During daily operations, large amounts of data are generated. This large volume and variety of data, arriving at a greater velocity has its own advantages and disadvantages. On the negative side, the abundance of data often impedes the ability to extract useful knowledge. In addition, the large amounts of data stored in often unconnected databases make it impractical to manually analyse for valuable decision-making information. However, an advent of new generation big data analytical tools has started to provide large scale bene ts for the organizations. The paper examines the possible data inputs from machines, people and organizations that can be analysed for maintenance. Further, the role of big data within maintenance is explained and how, if not managed correctly, big data can create problems rather than provide solutions. The paper highlights the need to have advanced mining techniques to enable conversion of data into information in an acceptable time frame and to have modern analytical tools to extract value from the big datasets.
    Original languageEnglish
    Pages (from-to)127-139
    JournalBig Data and Information Analytics
    Issue number2
    Publication statusPublished - 2017
    MoE publication typeA1 Journal article-refereed


    • big data
    • CBM
    • manufacturing


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