A Cloud-based Business Analytics for Supply Chain Decision Support

  • Shah J. Miah
Keywords: Supply Chain Management, business analytics, business intelligence, decision support.

Abstract

Todays businesses are required to have control over the big volume of data, transaction information and records that are rapidly generated through networked sensors, Internet sites, smart devices, and industrial machines. This big data are significant to process, store, manipulate and communicate for various strategic and operational purposes. The pattern, growth or declining facts/rates of the big-data are important for developing business strategies, improving management and operational business decision making. Although through various individual interactions the big-data are continuously created or re-created by offline and online activities, the actual solution design by offering power of analytics have not been discussed at a greater extent over the past. In this paper, we introduce a combined requirement of developing cloud-based analytics system to handle, retrieve, and manipulate the big data for improving decision making in supply chain management. The main emphasis in the study goes after outlining a conceptual analytics approach for meeting the decision support needs in a hypothetical supply chain industry problem-domain, specially focusing on decision support application for individual chain managers.

References

Accenture Global Operations Megatrends Study (2014). Big Data Analytics in Supply Chain: Hype or Here to Stay?, accessed on 20 April, 2015, from: http://www.accenture.com/SiteCollectionDocuments/PDF/Accenture-Global-Operations-Megatrends-Study-Big-Data-Analytics.pdf

Cecere, L. (2013). The Role of Supply Chain Analytics in the Race for the Future, Data Informed: big data and analytics in the enterprise, accessed on 23 April, 2015, from: http://data-informed.com/role-analytics-race-supply-chain-future/#sthash.aUt9zOQJ.NinuOKhl.dpuf

De Meo, P., Quattrone, G. & Ursino, D. (2008), A decision support system for designing new services tailored to citizen profiles in a complex and distributed e-government scenario, Data & Knowledge Engineering 67, 161184

Evermann, J. (2005). Towards a cognitive foundation for knowledge representation, Information Systems Journal, 15, 147-178

Ganeshan, R. and Harrison, T.P. (2002). An Introduction to Supply Chain Management, accessed 20 April, 2015 from: http://lcm.csa.iisc.ernet.in/scm/supply_chain_intro.html

Gennari, J.H., Musen, M.M., Fergerson, R.W., Grosso, W.E., Crubezy, M., Eriksson, H., Noy, N.F. & Tu, S.W. (2003). The evaluation of Protg: An Environment for Knowledge Based systems development, International Journal of Human-Computer Studies, 58, 89-123.

Gibson, M., Arnott, D., & Carlsson, S., (2004). Evaluating the Intangible Benefits of Business Intelligence: Review & Research Agenda, Proceeding of the Decision Support in an Uncertain and Complex World: The IFIP TC8/WG8.3 International Conference, Prato, Italy, 295-305

Knight, P. (2011). Business Analytics and the Supply Chain: The new path to value, IBM Official Report, Accessed on 20 April, 2015, from: https://www.google.com.au/#q=Business+Analytics+and+the+Supply+Chain:+The+new+path+to+value

Kohavi, R., Rothleder, N.J. and Simoudis, E. (2002). Emerging trends in business analytics, Communication of the ACM, Vol. 45 (8), pp. 45- 48

Miah, S.J. (2014). A Demand-Driven Cloud-based Business Intelligence for Health Professionals Decision Making, In Sun, Z and Yearwood , J. (Eds), Demand-Driven Web Services: Theory, Technologies and Applications. IGI Global Publisher, USA, pp. 324-339

Miah, S.J., Kerr, D., & von-Hellens, L. (2014). A Collective Artefact Design of Decision Support Systems: Design Science Research Perspective, Information Technology & People, 27(3), pp. 259-279

Nurmi, D., Wolski, R., Grzegorczyk, C., Obertelli, G., Soman, S., Youseff, L. & Zagorodnow, D. (2010). The Eucalyptus Open-source Cloud-computing System, In the proceedings of 9th IEEE/ACM International Symposium on Cluster Computing and the Grid, retrieved on 22 April, 2011, from http://www.cca08.org/papers/Paper32-Daniel-Nurmi.pdf

Santos, N., Gummadi, K. P. & Rodrigues, R. (2009). Towards Trusted Cloud Computing, retrieved on 22 April, 2011, from http://www.mpi-sws.org/~gummadi/papers/trusted_cloud.pdf, Accessed on 13th July 2013

Simchi-Levi, D., Kaminsky, P. and Simchi-Levi, E. (2008). Introduction to Supply Chain Management, Designing & Managing the Supply Chain: Concepts, Strategies & Case Studies, McGraw-Hill Companies, Inc.

Stasie?ko, J. (2010). Business Intelligence as a Decision Support System, In Galina Setlak, Krassimir Markov (ed.), Methods and Instruments of Artificial Intelligence, Rzeszow, Poland-Sofia, Bulgaria, 141-148.

Trkman, P., McCormack, K., de Oliveira, M.P.V., and Ladeira, M.B., (2010). The impact of business analytics on supply chain performance, Decision Support Systems, vol. 49 (3), pp. 318-327.

Published
2015-06-01
How to Cite
Miah, S. J. (2015). A Cloud-based Business Analytics for Supply Chain Decision Support. Journal of Information Sciences and Computing Technologies, 4(1), 274-280. Retrieved from http://scitecresearch.com/journals/index.php/jisct/article/view/203
Section
Articles