Big Data analytics in supply chain management: some conceptual frameworks. (2016)

• Record Type: Journal Article
• Authors: Lamba, Kuldeep; Singh, Surya Prakash
• Is Part Of: International journal of automation and logistics. Volume 2:Number 4(2016)
• Page Start: 279
• Page End: 293
• Publication Date: 2016
• Subjects: big data analytics -- predictive analytics -- supply chain management -- SCM -- facility layout -- procurement -- supplier selection -- order allocation -- joint procurement; Automation -- Periodicals; Logistics -- Periodicals; Logistics -- Computer programs -- Periodicals; 658.514
• Languages: English
• ISSNs: 2049-6745

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.