• DocumentCode
    130844
  • Title

    Big data and predictive analytics in ERP systems for automating decision making process

  • Author

    Prasad Babu, M.S. ; Sastry, S. Hanumanth

  • Author_Institution
    Dept. of CS & SE, Andhra Univ., Visakhapatnam, India
  • fYear
    2014
  • fDate
    27-29 June 2014
  • Firstpage
    259
  • Lastpage
    262
  • Abstract
    ERP systems, at present, are found to be inflexible to adapt to changing organizational processes. They are required to quickly adjust to changing processes and value-added chains and streamline their internal organizational structure. Data in ERP systems is becoming increasingly voluminous in their transactional programs. In this scenario, ERP systems are increasingly exposed to big data wherein the combined analysis of larger amounts of structured and unstructured data from disparate systems takes place in a short amount of time. Big data analytics requires greater use of predictive analytics to uncover hidden patterns and their relationships to visualize and explore data. The evolution of big data and predictive analytics have given a new way for exploring new frontiers in analytics-driven automation and decision management in highvolume, front-line operational decisions. In this paper the authors have focused on predictive capabilities of ERP systems, to analyze current data and historical facts in order to identify potential risks and opportunities for any organization. Analytical Decision Management & Business Rules are used to deploy decision as a service.
  • Keywords
    Big Data; business data processing; data analysis; data visualisation; decision making; enterprise resource planning; ERP systems; analytical decision management; analytics-driven automation; big data analytics; business rules; data visualization; decision making process automation; front-line operational decisions; internal organizational structure; organizational processes; potential risk identification; predictive analytics; predictive capabilities; transactional programs; value-added chains; Analytical models; Big data; Data mining; Data models; Decision making; Predictive models; Analytical Decision Management; Clustering; Decision Service; ERP; Forecasting; Predictive Analytics; Regression;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Software Engineering and Service Science (ICSESS), 2014 5th IEEE International Conference on
  • Conference_Location
    Beijing
  • ISSN
    2327-0586
  • Print_ISBN
    978-1-4799-3278-8
  • Type

    conf

  • DOI
    10.1109/ICSESS.2014.6933558
  • Filename
    6933558