ADERAH 1
ResearchProposal
Aderah,Mazama
Nancy Heckel
COLL300 B003
AmericanPublic University
17April 2016
Withthe rise in technology and the need to make on point decisions, theutilization of big data analysis in business has become a crucialundertaking. Business need to keep track or rather understand bettertheir market and operations (Wang, Gunesakaran, Ngai, &Papadopoulos, 2016). Also, they want to be competitive, productiveand efficient, and how best can they achieve this goal except throughadoption and utilization of big data technology in their logisticsmanagement. It will enable them to predict the likelihood of an eventand make timely decisions (Kethikidis, Koh, Dimitriadis, Gunesekaran,& Kehajova, 2008).
Thequestion remains, are these benefits to be enjoyed by just a fewplayers in the market? And if not so, can small and medium-sizedenterprises (SMEs) tap in the benefits of big data analysis in theirlogistic management?
Onewould think due to the technicality of big data analysis itsapplication in SMEs can be costly due to the insufficiency of enoughcapital, but thanks to technology. Growth in technology has madeeverything cheaper thus making the use of big data in SMEs logisticmanagement cost effective (Kethikidis et al., 2008). The use of bigdata techniques is becoming the game changer in the competitive worldof business, where only the best enjoy while others struggle tosurvive, and their benefits cannot be overlooked (Wang et al,2016).
Theresearch paper will seek to investigate the rate or level ofapplication of big data in SMEs’ logistics management and theimportance that can be derived from their implementation, throughsurveys. Though, I believe the use of big data techniques in thelogistic management of small and medium business enterprises is stillat its dawn.
References
Kethikidis,P., Koh, S., Dimitriadis, N., Gunesekaran, A. & Kehajova, M.(2008). The use of information systems for logistics and supplychain management in South East Europe: Current status and futuredirection. WhiteRose Research Online.Retrieved April 14, 2016 fromhttp://eprints.whiterose.ac.uk/3550/1/Ketikidis1_-_final.pdf
Mawhinney,L. & Self, R. (2015). . IS practices for SME success series.Universityof Derby.Retrieved April 14, 2016 fromhttp://commerce3.derby.ac.uk/ojs/index.php/itpsme/article/viewFile/91/68
Prajogo,D. & Olhager, J. (2012). Supply chain integration andperformance: The effects of long-term relationships, informationtechnology and sharing, and logistics integration. InternationalJournal of Production Economics.Vol. 135 (1) 514-522.
Waller,M. & Fawcett, S. (2013). Data science, predictive analytics, andbig data: A revolution that will transform supply chain design andmanagement. Journalof Business Logistics.Vol. 34 (2) 77-84.
Wang,G., Gunesakaran, A., Ngai, E. & Papadopoulos, T. (2016). Bigdata analytics in logistics and supply chain management: Certaininvestigations for research and applications. InternationalJournal of Production Economics. Vol. 176 (2016) 98-110.
Wu,Y., Cegielski, C., Hazen, B. & Hall, D. (2013). Cloud computingin support of supply chain information system management: Understanding when to go to the cloud. Journalof Supply Chain Management.Vol. 49 (3) 25-41.
Zhong,R., Huang, G., Lan, S., Dai, Q., Xu, C. & Zhang, T. (2015). Abig data approach for logistics trajectory discovery fromRFID-enabled production data. InternationalJournal of Production Economics. Vol. 175.