The Impact of Big Data Analytics on Company Performance in Supply Chain Management

Ionica Oncioiu, Ovidiu Constantin Bunget, Mirela Cătălina Türkeș, Sorinel Căpușneanu, Dan Ioan Topor, Attila Szora Tamaș, Ileana-Sorina Rakoș and Mihaela Ștefan Hint
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Ionica Oncioiu: Faculty of Finance-Banking, Accounting and Business Administration, Titu Maiorescu University, 040051 Bucharest, Romania
Ovidiu Constantin Bunget: Faculty of Economics and Business Administration, West University of Timisoara, 300223 Timisoara, Romania
Mirela Cătălina Türkeș: Faculty of Finance, Banking and Accountancy, Dimitrie Cantemir Christian University, 040051 Bucharest, Romania
Sorinel Căpușneanu: Faculty of Finance-Banking, Accounting and Business Administration, Titu Maiorescu University, 040051 Bucharest, Romania
Dan Ioan Topor: Faculty of Economic Sciences, 1 Decembrie 1918 University, 510009 Alba-Iulia, Romania
Attila Szora Tamaș: Faculty of Economic Sciences, 1 Decembrie 1918 University, 510009 Alba-Iulia, Romania
Ileana-Sorina Rakoș: Faculty of Sciences, University of Petrosani, 20 Universitatii, 332006 Petrosani, Romania
Mihaela Ștefan Hint: Faculty of Economic Sciences, 1 Decembrie 1918 University, 510009 Alba-Iulia, Romania
Authors registered in the RePEc Author Service: Attila Szora Tamas () and Sorinel Capusneanu

Sustainability, 2019, vol. 11, issue 18, 1-22

Abstract: Big data analytics can add value and provide a new perspective by improving predictive analysis and modeling practices. This research is centered on supply-chain management and how big data analytics can help Romanian supply-chain companies assess their experience, strategies, and professional capabilities in successfully implementing big data analytics, as well as assessing the tools needed to achieve these goals, including the results of implementation and performance achievement based on them. The research method used in the quantitative study was a sampling survey, using a questionnaire as a data collection tool. It included closed questions, measured with nominal and ordinal scales. A total of 205 managers provided complete and useful answers for this research. The collected data were analyzed with the Statistical Package for the Social Sciences (SPSS) package using frequency tables, contingency tables, and main component analysis. The major contributions of this research highlight the fact that companies are concerned with identifying new statistical methods, tools, and approaches, such as cloud computing and security technologies, that need to be rigorously explored.

Keywords: supply-chain management; implementation; big data analytics; industry 4.0; results; benefits; barriers; analytic tools (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:11:y:2019:i:18:p:4864-:d:264609

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