Title: Big Data analytics in supply chain management: some conceptual frameworks

Authors: Kuldeep Lamba; Surya Prakash Singh

Addresses: Department of Management Studies, Indian Institute of Technology Delhi, New Delhi, India ' Department of Management Studies, Indian Institute of Technology Delhi, New Delhi, India

Abstract: The plethora of processes involved in supply chain management (SCM) along with modern information technology laden infrastructure viz., RFID-enabled devices, internet of things, cloud computing, etc. have made it very convenient to collect hugely voluminous data which essentially possesses the 3V characteristics of Big Data, i.e., volume, velocity and variety. This Big Data has huge potential to unravel the hidden business insights that can help an organisation outperform its competitors. Due to the substantial benefits of Big Data in SCM, the field of Big Data and predictive analytics has been continuously growing from just an emerging area, not a long back to a full-fledged research area at present, drawing attention of practicing professionals from industry as well as academicians worldwide in equal measures. Many theoretical frameworks on Big Data for SCM have been proposed. However, the complexities associated with the 3V criterion of Big Data have generally not been addressed in these frameworks. The paper proposes three new Big Data frameworks in SCM domains viz., procurement, joint procurement, and facility layout problem.

Keywords: big data analytics; predictive analytics; supply chain management; SCM; facility layout; procurement; supplier selection; order allocation; joint procurement.

DOI: 10.1504/IJAL.2016.080341

International Journal of Automation and Logistics, 2016 Vol.2 No.4, pp.279 - 293

Received: 01 Jul 2016
Accepted: 11 Jul 2016

Published online: 15 Nov 2016 *

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