Big Data Applications in Supply Chain Management

Emel Aktas ()
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Emel Aktas: Cranfield University

A chapter in The Palgrave Handbook of Supply Chain Management, 2024, pp 1301-1325 from Springer

Abstract: Abstract This chapter overviews emerging applications of big data analytics in supply chain management. The academic attention on big data applications and their practitioner uptake is growing. Many recent papers showcase descriptive, predictive, and prescriptive analytics applications where multiple benefits emerge from applying big data analytics to managerial problems. Such benefits include cost reduction, increases in revenues and profits, and minimization of the environmental impact of operations. Current concerns include the transition from traditional to digital supply chains and what can realistically be achieved over the next two decades. While we evidence excellent applications of big data analytics for supply chain planning and management problems, the issue of working in silos persists. For an organization to fully exploit big data applications, data should be perceived as an asset. When deploying novel artificial intelligence algorithms, the explainability of these algorithms should be at the forefront of an implementation strategy. Future research directions should be aimed at devising a connected and coordinated analytics approach that will enable the benefits of big data applications to go beyond what is currently realized.

Keywords: Data capture; Descriptive analytics; Predictive analytics; Prescriptive analytics; Logistics; Distribution; Warehousing; Retail (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-19884-7_74

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DOI: 10.1007/978-3-031-19884-7_74

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