Empirical Study on Big Data Analysis for Supply Chain Management

Subhra Prosun Paul, Shormee Saha

Abstract


with globalization, outsourcing is reaching beyond continents. Design is done in one part of the world, manufacturing in another low-cost country and distribution to other countries in the world. Procurement is illuming as a central focus that requires to be synchronized with all other business functions. As a matter of course, a sizeable amount of a firm’s revenue goes for its supply chain that interprets the significance of the supply chain lays in a firm’s bottom-line. So, the supply chain has a tremendous opportunity to get used of data. Nowadays, the supply chain is attracting much and more attention because in terms of analytics it is behind other functions of a firm.  Specifically, this paper will (1) redefine, by research on scientific work, what BDA means in the context of Supply Chain Management, and how it differs and has evolved from analytics technologies; (2) evolve taxonomy of Big Data within SCM that identifies and classifies the different sources and types of data arising in modern supply chains and (3) suggest some applications of BDA and show the potential high value of this technology offers to solve intricate SCM challenges.  This research tries to explore how the behavior of Big Data can succor procurement and SCM in greater decision making. Big data can be a lightening of a resilient environment while managing suppliers in global SCM is a challenging task. Another studied aspect is having access to a greater pool of data and what kind of potential data can render benefit SCM. SCM professionals were interviewed to understand what they expect from their logistics, procurement and marketing systems and how Big Data can contribute to that. What type of transparency is needed? What requires to be automated? What delineation of data is useful? Furthermore, how Big Data can help with SCM risk management.

Full Text:

PDF

References


Assunção, M.D., Calheiros, R.N., Bianchi, S., Netto, M.A. and Buyya, R., (2015). “Big Data computing and clouds: Trends and future directions”. Journal of Parallel and Distributed Computing, 79, pp.3-15.

Baxter, P. and Jack, S., (2008). “Qualitative case study methodology: Study design and implementation for novice researchers”. The qualitative report, 13(4), pp.544-559.

Chen, H., Chiang, R.H. and Storey, V.C., 2012. Business intelligence and analytics: from big data to big impact. MIS quarterly, pp.1165-1188.

Chen, D.Q., Preston, D.S. and Swink, M., (2015). “How the use of big data analytics affects value creation in supply chain management”. Journal of Management Information Systems, 32(4), pp.4-39.

Chithur, D. (2014). “Driving Strategic Sourcing Effectively with Supply Market Intelligence”. Tata Consultancy Services (TCS).

Chowdhary, P., Ettl, M., Dhurandhar, A., Ghosh, S., Maniachari, G., Graves, B., Schaefer, B. and Tang, Y., (2011), “Managing procurement spend using advanced compliance analytics”. In e-Business Engineering (ICEBE), 2011 IEEE 8th International Conference on pp. 139-144. IEEE.

Demchenko, Y., Grosso, P., De Laat, C. and Membrey, P., (2013), “Addressing big data issues in scientific data infrastructure”. In Collaboration Technologies and Systems (CTS), 2013 International Conference on pp. 48-55. IEEE.

Fan, Y., Heilig, L. and Voß, S., (2015). “Supply chain risk management in the era of big data”. In International Conference of Design, User Experience, and Usability, pp. 283-294. Springer, Cham.

Gandomi, A. and Haider, M., (2015). “Beyond the hype: Big data concepts, methods, and analytics”. International Journal of Information Management, 35(2), pp.137-144.

Gao, J., Koronios, A. and Selle, S., (2015). “Towards a process view on critical success factors in big data analytics projects”.

Gunasekaran, A., Papadopoulos, T., Dubey, R., Wamba, S.F., Childe, S.J., Hazen, B. and Akter, S., (2017). “Big data and predictive analytics for supply chain and organizational performance”. Journal of Business Research, 70, pp.308-317.


Refbacks

  • There are currently no refbacks.


22crown login slot gacor