Modeling drivers to big data analytics in supply chains


  • Md. Nura Alam Siddique Department of Industrial and Production Engineering, Bangladesh University of Engineering and Technology, Dhaka-1000, Bangladesh
  • Kazi Wahadul Hasan Department of Industrial and Production Engineering, Bangladesh University of Engineering and Technology, Dhaka-1000, Bangladesh
  • Syed Mithun Ali Department of Industrial and Production Engineering, Bangladesh University of Engineering and Technology, Dhaka-1000, Bangladesh
  • Md. Abdul Moktadir Department of Leather Products Engineering, Institute of Leather Engineering & Technology, University of Dhaka, Bangladesh
  • Sanjoy Kumar Paul UTS Business School, University of Technology Sydney, Sydney, Australia
  • Golam Kabir Industrial Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, Regina, SK, S4S 0A2, Canada


Big data analytics, Multi criteria decision making, Best-worst method, Drivers, Supply chain management


The recent emergence of data-driven business markets and the ineligibility of traditional data management systems to trace them have fostered the application of Big Data Analytics (BDA) in supply chains of the present decade. Literature reviews reveal that the successful implication of BDA in a supply chain mainly depends on some key drivers considering the size and operations of an organization. However, collective analysis of all these drivers is still neglected in the existing research field. Therefore, the purpose of this research is to identify and prioritize the most significant drivers of BDA in the supply chains. To this aim, a novel Best-worst method (BWM) based framework has been proposed, which has successfully identified and sequenced the twelve most significant drivers with the help of previous literature and experts’ opinions. Theoretically, this study contributes to the BDA literature by offering some unique drivers to BDA in supply chains. The findings show that ‘sophisticated structure of information technology’ and ‘group collaboration among business partners’ are the top most significant drivers. ‘Digitization of society’ is identified as the least significant driver of BDA in this study. The outcome of this study is expected to assist the industry managers to find out the most and least preferable drivers in their supply chains and then take initiatives to improve the overall efficiency of their organizations accordingly.


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2020-09-24 — Updated on 2020-10-27


How to Cite

Siddique, M. N. A. ., Hasan, K. W. ., Ali, S. M. ., Moktadir, M. A. ., Paul, S. K. ., & Kabir, G. (2020). Modeling drivers to big data analytics in supply chains. Journal of Production Systems and Manufacturing Science, 2(1), 4-25. Retrieved from (Original work published September 24, 2020)



Original Research Articles