Predictive Analytics and Intelligent Decision Support Systems in Supply Chain Risk Management—Research Directions for Future Studies

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Abstract

Today’s supply chains (SC) are immersed in extremely dynamic environments, and supply chain management (SCM) has to deal with a multitude of risks. The domain of supply chain risk management (SCRM) has emerged, providing approaches on how to cope with risks in SC. However, due to increased complexity, volatility, and uncertainty, the number of risks in global SC has increased significantly. Harnessing the power of predictive analytics (PA), implemented in intelligent decision support systems (IDSS), offers huge potential in SCRM. However, research at the intersection of the domains of SCRM, PA, and IDSS is still in its infancy, and several research gaps have yet to be addressed. The paper elaborates on these research gaps by means of a systematic literature review. The results include a set of seven research questions and proposed research directions for future studies. Future research is presented with a plethora of starting points, which originate from the business perspective (i.e., the SCRM domain), the data-driven (i.e., the PA domain) as well as an IT-system perspective (i.e., the IDSS domain).

Original languageEnglish
Title of host publicationProceedings of 7th International Congress on Information and Communication Technology, ICICT 2022
EditorsXin-She Yang, Simon Sherratt, Nilanjan Dey, Amit Joshi
PublisherSpringer
Pages549-558
Number of pages10
ISBN (Print)9789811923937
DOIs
Publication statusPublished - 2022
Event7th International Congress on Information and Communication Technology, ICICT 2022 - Virtual, Online
Duration: 21 Feb 202224 Feb 2022

Publication series

NameLecture Notes in Networks and Systems
Volume464
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

Conference7th International Congress on Information and Communication Technology, ICICT 2022
CityVirtual, Online
Period21.02.202224.02.2022

Keywords

  • Decision support
  • Predictive analytics
  • Supply chain management
  • Supply chain risk management

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