Fuzzy VIKOR approach for selection of big data analyst in procurement management

Journal of Transport and Supply Chain Management

 
 
Field Value
 
Title Fuzzy VIKOR approach for selection of big data analyst in procurement management
 
Creator Bag, Surajit
 
Subject Supply Chain Management; Information Science; Big Data Big data; Business analyst; selection process; multi criteria decision making (MCDM); fuzzy sets; VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR)
Description Background: Big data and predictive analysis have been hailed as the fourth paradigm of science. Big data and analytics are critical to the future of business sustainability. The demand for data scientists is increasing with the dynamic nature of businesses, thus making it indispensable to manage big data, derive meaningful results and interpret management decisions.Objectives: The purpose of this study was to provide a brief conceptual review of big data and analytics and further illustrate the use of a multicriteria decision-making technique in selecting the right skilled candidate for big data and analytics in procurement management.Method: It is important for firms to select and recruit the right data analyst, both in terms of skills sets and scope of analysis. The nature of such a problem is complex and multicriteria decision-making, which deals with both qualitative and quantitative factors. In the current study, an application of the Fuzzy VIsekriterijumska optimizacija i KOmpromisno Resenje (VIKOR) method was used to solve the big data analyst selection problem.Results: From this study, it was identified that Technical knowledge (C1), Intellectual curiosity (C4) and Business acumen (C5) are the strongest influential criteria and must be present in the candidate for the big data and analytics job.Conclusion: Fuzzy VIKOR is the perfect technique in this kind of multiple criteria decisionmaking problematic scenario. This study will assist human resource managers and procurement managers in selecting the right workforce for big data analytics.
 
Publisher AOSIS
 
Contributor
Date 2016-07-28
 
Type info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion — Interview
Format text/html application/octet-stream text/xml application/pdf
Identifier 10.4102/jtscm.v10i1.230
 
Source Journal of Transport and Supply Chain Management; Vol 10, No 1 (2016); 6 pages 1995-5235 2310-8789
 
Language eng
 
Relation
The following web links (URLs) may trigger a file download or direct you to an alternative webpage to gain access to a publication file format of the published article:

https://jtscm.co.za/index.php/jtscm/article/view/230/450 https://jtscm.co.za/index.php/jtscm/article/view/230/451 https://jtscm.co.za/index.php/jtscm/article/view/230/452 https://jtscm.co.za/index.php/jtscm/article/view/230/442
 
Coverage — 2015-2016 Procurement Managers
Rights Copyright (c) 2016 Surajit Bag https://creativecommons.org/licenses/by/4.0
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