BIG DATA CONCEPT IN THE FOOD SUPPLY CHAIN: SMALL MARKETS CASE

Valentinas Navickas, Valentas Gruzauskas

Abstract


The strategies of competitive advantage are changing dramatically, because of high technology development. The data size in the world is multiplying rapidly - the amount of information in the world doubles every 12 months. Therefore, the authors analyzed big data in the food supply chain. The food industry‘s supply is complicated, because of various regulations and a demand for high quality products just on time. Various companies are transporting partial freight. Therefore, the visibility, lead-time and cost minimization is essential for them. However, they are unable to use all the gathered information and are not utilizing the potential that is possible. The problem of data analysis is a bigger concern to the smaller markets. Many of the small markets are less developed countries that still is not using big data in their enterprises. In addition, new technologies are developing in the big data industry. Therefore, the gap of technology will increase even more between the large and small markets. The analyzed innovation level and technology usage indicated a need for the food industry to change competiveness strategies. Therefore, the authors developed a competiveness strategy that is oriented to the small market’s food industry. 


Full text: PDF

Keyword(s)


Keywords: Big data, supply chain, logistics, competiveness, food industry, small market.

JEL Codes


C80, L66, L91

References


References

B. Bilbao-Osorio, S. Dutta, B.L., 2014. Global Information Technology Report 2014, Geneva. Available at: http://reports.weforum.org/global-information-technology-report-2014/.

Beckeman, M. & Skjöldebrand, C., 2007. Clusters/networks promote food innovations. Journal of Food Engineering, 79(4), pp.1418–1425.

Benyoucef, L. & Jain, V., 2009. Editorial note for the special issue on “Artificial Intelligence Techniques for Supply Chain Management.” Engineering Applications of Artificial Intelligence, 22(6), pp.829–831.

Bielli, M., Bielli, A. & Rossi, R., 2011. Trends in models and algorithms for fleet management. Procedia - Social and Behavioral Sciences, 20, pp.4–18.

Bosona, T.G. & Gebresenbet, G., 2011. Cluster building and logistics network integration of local food supply chain. Biosystems Engineering, 108(4), pp.293–302.

Cecere, L., 2013. Big Data Handbook. Supply Chain Insights, pp.1 – 21.

Ernst & Young report, 2014. Big data Changing the way businesses compete and operate.

Ernst & Young Report, 2015. Big data project aims to improve Dutch flood control, save the government millions. IDG News Service. Available at: http://www.pcworld.com/article/2042941/big-data-project-aims-to-improve-dutch-flood-control-save-the-government-millions.html [Accessed May 23, 2015].

European Comission, 2008. Internet of Things in 2020: A roadmap for the future, Available at: http://www.smart-systems-integration.org/public/documents/publications/Internet-of-Things_in_2020_EC-EPoSS_Workshop_Report_2008_v3.pdf.

Global, A. & Megatrends, O., 2014. Big Data Analytics in Supply Chain : Hype or Here to Stay ? Big data analytics. Accenture company’s report, pp.1–20.

J. Manyika, M. Chui, B. Brown, J. Bughin, R. Dobbs, C. Roxburgh, A.H.B., 2011. Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute, (June), p.156.

Kriesel, D., 2005. A Brief Introduction to Neural Networks, Bonn: Dkriesel. Available at: http://www.dkriesel.com/_media/science/neuronalenetze-en-zeta2-2col-dkrieselcom.pdf.

McKinsey Center for Business Technology, 2012. Perspectives on Digital Business,

Mejjaouli, S. & Babiceanu, R.F., 2015. RFID-wireless sensor networks integration : Decision models and optimization of logistics systems operations. Journal Of Manufacturing Systems, 35, pp.234–245.

Ministry of Education Culture and Science of the Goverment of the Netherlands, 2014. A Vision for Science Education, Rotterdam.

Olsson, A., 2004. Temperature Controlled Supply Chains Call For Improved Knowledge And shared responsibility. Proceedings of the 16th Annual Conference Nofoma, 1(1), pp.569 – 582.

Ramaa.a, Subramanya, K.N. & Rangaswamy, T.M., 2012. Impact of Warehouse Management System in a Supply Chain. International Journal of Computer Applications, 54(1), pp.14–20.

Samuel R. Allen Deborah L. Wince-Smith, J.E., 2013. Global Manufacturing Competitiveness Index. Council on Competiveness.

SAS company’s report, 2012. Data equity Unlocking the value of big data. Cebr, (April), pp.1–44.

Schaeffer, D.M. & Olson, P.C., 2014. Big Data Options For Small And Medium Enterprises. Review of Business Information Systems, 18(1), pp.41–46.

Schuld, M., Sinayskiy, I. & Petruccione, F., 2015. Simulating a perceptron on a quantum computer. Physics Letters A, 379, pp.660–663.

Schwab, K., 2015. The global competiveness report 2014-2015, Geneva: World Economic forum.

Statistics Lithuania, Active enterprises in Lithuania, by economical sector. Available at: http://osp.stat.gov.lt/statistiniu-rodikliu-analize?portletFormName=visualization&hash=23259c80-3334-42b4-9378-aa7fdf0d7f0f [Accessed May 22, 2015a].

Statistics Lithuania, Enterprises using IT systems. Available at: http://osp.stat.gov.lt/web/guest/statistiniu-rodikliu-analize?portletFormName=visualization&hash=b4cc39dc-6666-46f4-b4a6-b16eef04368f [Accessed May 23, 2015b].

Talele, N., Shukla, A. & Bhat, S., 2012. Can Quantum Computers Replace the Classical Computer ? International Journal of Engineering and Advanced Technology, 2(2), pp.93–96.

The world bank report, 2014. Logistics Performance Index. Available at: http://lpi.worldbank.org/international/global [Accessed May 23, 2015].

Truong, D., 2014. Cloud-Based Solutions for Supply Chain Management: a Post-Adoption Study. Proceedings of ASBBS, 21(1), pp.697–708.

Ventana Company’s report, 2007. The Visible Supply Chain White Paper. , pp.1–6.

White, C., 2013. Big Data and Advanced Analytics Technologies and Use Cases " Data Growth : Choose an Analyst ! BI Reaserch, report.

Zhang, S. et al., 2015. Swarm intelligence applied in green logistics: A literature review. Engineering Applications of Artificial Intelligence, 37, pp.154–169.