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
Link To Document :
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