Selection of Big Data analyst in purchasing and supply management: fuzzy VIKOR approach. (2016)

• Record Type: Journal Article
• Authors: Bag, Surajit
• Is Part Of: International journal of automation and logistics. Volume 2:Number 4(2016)
• Page Start: 294
• Page End: 306
• Publication Date: 2016
• Subjects: big data -- business analysts -- selection process -- multicriteria decision making -- MCDM -- fuzzy sets -- fuzzy VIKOR -- purchasing -- supply chain management -- SCM -- analyst selection -- technical knowledge -- intellectual curiosity -- business acumen; Automation -- Periodicals; Logistics -- Periodicals; Logistics -- Computer programs -- Periodicals; 658.514
• Languages: English
• ISSNs: 2049-6745

Abstract

Big Data and predictive analysis is considered a major revolution in the history of data sciences. It has been instrumental in transforming the discipline of supply chain management. Recently a rising trend is observed in terms of research output in the area of Big Data and its application in supply chain management, particularly under top SCM journals which clearly indicates the importance of the subject. The current paper argues for the use of fuzzy VIKOR method to solve the Big Data analyst selection problem. Fuzzy VIKOR is a unique multi criteria decision making technique and here it uses linguistic variables with triangular fuzzy numbers to estimate weights of the criteria and ranking of each Big Data analyst. The findings suggest that technical knowledge, intellectual curiosity and business acumen are the strongest influential criteria and must be present in the candidate for the Big Data and analytics job in purchasing function. The findings may assist procurement heads in understanding the criticality related to Big Data and predictive analysis and further careful selection of candidates in the operations team.